This paper presents a novel vision-based approach for tracking deformable contour targets using Unmanned Aerial Vehicles (UAVs) through combining image moments descriptor and a Policy Iteration scheme ensuring stability and generalization of knowledge to new tasks. This computationally efficient and optimal control scheme is suitable for diverse dynamic environments such as the surveillance and tracking of targets with evolving features. Due to the ability of the proposed scheme to comprehend an optimization output, the generated control sequence, from an offline successively approximated policy, makes the process less challenging. The proposed methodology is validated through extensive simulations and real-word exper-iments of environmental target surveillance using an octorotor UAV.

A novel method for optimal motion planning in the context of a class of dynamical system is proposed in this work. Our approach is based on the design of a provably safe and convergent actor structure, which is optimized via a policy iteration method. The proposed actor has wide applications, from control of mechanical systems to providing acceleration commands for more complex robotic platforms. Extra care is taken to provide theoretical guarantees, and the scheme is validated against an existing sampling-based planner.

In this work, we present a nonlinear Model Predictive Control (NMPC) scheme for tracking a ground target using a multirotor with a cable-suspended load. The NMPC framework relies on the dynamic model of the UAV with the suspended load and, hence, an estimate of the load state is obtained by fusing the measurements of a downward-facing camera and a load cell with an Unscented Kalman Filter (UKF). Additionally, since the NMPC relies on the future behavior of the system, the trajectory of the ground target throughout the predicted time horizon of the NMPC, is required. Towards this direction, Bézier curves are employed in order to predict the future trajectory of the target, which moves in an arbitrary way. The ultimate goal of the proposed framework is to release the suspended load to the ground target and, consequently, a condition is checked at each time instant that triggers the opening of a gripper, located at the lower edge of the cable. The performance of the proposed control scheme is experimentally validated using an octorotor.

In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, convergence and optimality. Moreover, in simply-connected workspaces, our method is proven to asymptotically provide the globally optimal path. Our method is compared against a provably asymptotically optimal RRT⋆method, as well as a relevant reactive method and provides satisfactory performance, closely matching or outperforming the former.

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT∗, highlighting its contribution to the field of 3D optimal motion planning.

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

In this paper, we present an event-based control framework for the efficient tracking of contour-based areas, such as road pavements, using a multirotor aerial vehicle equipped with a bio-inspired Dynamic Vision Sensor (DVS). Concerning the detection part, the DVS camera captures events, which are asynchronously fed into a Neuromorphic Hough Transform algorithm running on a SpiNN-3 board and implemented as a Spiking Neural Network (SNN). Next, the asynchronous output of the detection module is fed into an analytically formulated event-based Partitioned Visual Servoing (PVS) algorithm, running on conventional processing hardware, which allows the multirotor to autonomously track and navigate along the detected contour. The proposed architecture achieves efficient tracking of contour-based areas, while constantly maintaining the latter inside the DVS camera's field of view. A set of real-time experiments in various settings employing an octorotor equipped with a downward-looking DVS and a SpiNN-3 board demonstrate the effectiveness of the suggested framework.

In this paper, an Event-triggered Image-based Visual Servoing Nonlinear Model Predictive Controller (ET-IBVS-NMPC) for multirotor aerial vehicles is presented. The proposed scheme is developed for the autonomous surveillance of contour-based areas with different characteristics (e.g. forest paths, coastlines, road pavements). For this purpose, an appropriately trained Deep Neural Network (DNN) is employed for the accurate detection of the contours. In an effort to reduce the remarkably large computational cost required by an IBVS-NMPC algorithm, a triggering condition is designed to define when the Optimal Control Problem (OCP) should be resolved and new control inputs will be calculated. Between two successive triggering instants, the control input trajectory is applied to the robot in an open-loop fashion, which means that no control input computations are required. As a result, the system's computing effort and energy consumption are lowered, while its autonomy and flight duration are increased. The visibility and input constraints, as well as the external disturbances, are all taken into account throughout the control design. The efficacy of the proposed strategy is demonstrated through a series of real-time experiments using a quadrotor and an octorotor both equipped with a monocular downward looking camera.

This paper addresses the problem of water sampling by using a multirotor UAV with a cable-suspended mechanism. In order to ensure the safe execution of the sampling procedure and the stabilization of the vehicle, the disturbances, induced by the water flow and transferred through the cable, have to be identified. Specifically, an estimate of the disturbances is extracted by integrating a depth sensor, a load cell, an ultrasonic sensor and a downward-looking camera into the UAV's sensor suite and fusing the respective measurements. Gaussian Processes are afterwards employed so as to learn the uncertain disturbances in real time and in a non-parametric manner. The predicted disturbances are incorporated into a geometric control scheme which is capable of stabilizing the UAV above the desired sampling position while compensating for the aforementioned disturbances. The performance of the proposed control strategy is demonstrated through both simulation and experimental results.

The connectivity of distributed networked multi-robot systems is a crucial operational specification, since the involved robots interact/communicate locally only with their immediate neighbors. Thus, in this work, we propose a distributed algorithm to estimate the algebraic connectivity of the underlying communication graph, which stands as a valid connectivity metric. Our method establishes robustness and fast convergence properties that can be adjusted independently via the appropriate selection of certain design parameters. Finally, we confirm the theoretical findings through simulated paradigms and verify the superiority of our method against a well-established solution of the related literature.

In this work we present a novel, approximate, efficient algorithm for determining the Trim Flight Envelope of a fixed-wing UAV, based on a generic, nonlinear numerical model. The resulting Flight Envelope is expressed as a convex intersection of half-spaces. Subsequently, a Model Predictive Controller (MPC) is designed which takes into account the Flight Envelope constraints, to avoid Loss-of-Control. The overall system is shown to operate in real-time in a simulation environment.

This paper presents a novel control strategy for the compensation of the slippage effect during non-rigidly grasped object manipulation. A detailed dynamic model of the interconnected system composed of the robotic manipulator, the object and the internal forces and torques induced by the slippage effect is provided. Next, we design a model-based variable impedance control scheme, in order to achieve simultaneously zero convergence for the trajectory tracking error and the slippage velocity of the object. The desired damping and stiffness matrices are formulated online, by taking into account the measurement of the slippage velocity on the contact. A formal Lyapunov-based analysis guarantees the stability and convergence properties of the resulting control scheme. A set of extensive simulation studies clarifies the proposed method and verifies its efficacy.

We propose a human-robot interface based on potentials recorded through surface Electroencephalographic sensors, aiming to decode human visual attention into motion in three-dimensional space. Low-frequency components are extracted and processed in real time, and subspace system identification methods are used to derive the optimal, in mean squared sense, linear dynamics generating the position vectors. This results in a human-robot interface that can be used directly in robot teleoperation or as part of a shared-control robotic manipulation scheme, feels natural to the user, and is appropriate for upper extremity amputees, since it requires no limb movement. We validate our methodology by teleoperating a redundant, anthropomorphic robotic arm in real time. The system's performance outruns similar EMG-based systems, and shows low long-term model drift, indicating no need for frequent model re-training.

In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to adjust appropriately the parameters of the underlying potential field towards minimizing the Hamilton-Jacobi-Bellman (HJB) error. The proposed method, outperforms consistently a Rapidly-exploring Random Trees (RRT*) method and consists a fertile advancement in the optimal motion planning problem.

This paper addresses the problem of cooperative object transportation for multiple Underwater Vehicle Manipulator Systems (UVMSs) in a constrained workspace involving static obstacles. We propose a Nonlinear Model Predictive Control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations such as: kinematic and representation singularities, obstacles within the workspace, joint limits and control input saturations. More precisely, by exploiting the coupled dynamics between the robots and the object, and using certain load sharing coefficients, we design a distributed NMPC for each UVMS in order to cooperatively transport the object within the workspace's feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Additionally, the feedback relies on each UVMS's locally measurements and no explicit data is exchanged online among the robots, thus reducing the required communication bandwidth. Finally, real-time simulation results conducted in UwSim dynamic simulator running in ROS environment verify the efficiency of the theoretical finding.

In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.

This work addresses the problem of robot navigation under timed temporal specifications in workspaces cluttered with obstacles. We propose a hybrid control strategy that guarantees the accomplishment of a high-level specification expressed as a timed temporal logic formula, while preserving safety (i.e., obstacle avoidance) of the system. In particular, we utilize a motion controller that achieves safe navigation inside the workspace in predetermined time, thus allowing us to abstract the motion of the agent as a finite timed transition system among certain regions of interest. Next, we employ standard formal verification and convex optimization techniques to derive high-level timed plans that satisfy the agent's specifications. A simulation study illustrates and clarifies the proposed scheme.

In this work, a hybrid control scheme is presented in order to address the navigation problem for a planar robotic platform of arbitrary shape that is moving inside an obstacle cluttered workspace. Given an initial and desired robot configuration, we propose a methodology based on approximate configuration space decomposition techniques that makes use of heuristics to adaptively refine a partition of the configuration space into non-overlapping, adjacent slices. Furthermore, we employ appropriate workspace transformations and adaptive potential field based control laws that integrate elegantly with the type of configuration space representation used, in order to safely navigate within a given cell and successfully cross over to the next, for almost all initial configurations, until the desired configuration is reached. Finally, we present simulation results that demonstrate the efficacy of the proposed control scheme.

MAVLink is a popular message protocol for small Unmanned Aerial Vehicles (UAVs). In this work, we present a Fault Detection and Isolation (FDI) framework for fixed-wing UAVs which takes advantage of the information conveyed in MAVLink telemetry streams and produces a bank of residual generators. Structural Analysis is employed to systematically handle the varying set of available measurements, identify the observable faults and adjust the FDI system accordingly. Structural detectability and isolability analyses are carried out. A case-study on a real-life telemetry log of a UAV crash demonstrates the efficacy of the proposed approach.

Parity-Based methodologies for fault diagnosis in UAVs often result in nonlinear residual generators. Still, a systematic framework to perform detectability and robustness analyses of residual generators does not exist. In this work, detectability and robustness metrics for static and dynamic residuals are presented, while numerical methods, specifically Particle Swarm Optimization, are employed to calculate them. The results are used to characterize the performance of a fault detection system. An application on a UAV model is shown, based on real flight data.

In this paper, we propose a visual servoing scheme that imposes predefined performance specifications on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the camera's limited field of view, despite the inevitable calibration and depth measurement errors. Its efficiency is demonstrated via comparative experimental and simulation studies.

This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme for underwater robotic vehicles operating in a constrained workspace including static obstacles. The purpose of the controller is to guide the vehicle towards specific way points. Various limitations such as: obstacles, workspace boundary, thruster saturation and predefined desired upper bound of the vehicle velocity are captured as state and input constraints and are guaranteed during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, the control inputs calculated by the proposed scheme are formulated in a way that the vehicle will exploit the ocean currents, when these are in favor of the way-point tracking mission which results in reduced energy consumption by the thrusters. The performance of the proposed control strategy is experimentally verified using a 4 Degrees of Freedom (DoF) underwater robotic vehicle inside a constrained test tank with obstacles.

This paper focuses on real-time estimation of the aerodynamic model parameters of small-scale fixed wing Unmanned Aerial Vehicles (UAVs) without the aid of wind-tunnel experiments, using exclusively flight data. The key tool of the following analysis centers around the principles of Total Least Squares estimation. Contrary to Ordinary Least Squares, this method accounts for errors in both explanatory data and variables to-be-explained. This is a highly desirable property for UAVs equipped with low-cost sensor systems. The proposed implementation combines both batch and real-time schemes, while deals efficiently with the problem of Insufficient System Excitation. Online adaptation to model changes is performed by applying a Variable Forgetting Factor to the estimation data. Finally, a Monte Carlo approach is developed for uncertainty estimation regarding compound aerodynamic variables.

Artificial Potential Fields (APFs) constitute an intuitive tool for designing autonomous robot navigation control schemes, though they generally suffer from the existence of local minima which may trap the robot away from its desired configuration, an issue usually addressed by appropriate offline “tuning” of the potential field's parameters. On the other side, most APF based approaches rely on a diffeomorphism to sphere worlds to handle realistic scenarios, which may be either costly to compute (e.g., conformal mappings) or requires some sort of preconditioning of the workspace (e.g., decomposition of complex geometries to simple elementary components). In this work, we first propose a constructive procedure to map multiply connected compact 2D workspaces to one or more punctured disks based on harmonic maps. Subsequently, we design an APF based control scheme along with an adaptive law for its parameters that requires no offline tuning to guarantee safe convergence to its goal configuration. Finally, an extensive simulation study is conducted to demonstrate the efficacy of the proposed control scheme.

This paper presents the design of a vision-based object grasping and motion control architecture for a mobile manipulator system. The optimal grasping areas of the object are estimated using the partial point cloud acquired from an onboard RGB-D sensor system. The reach-to-grasp motion of the mobile manipulator is handled via a Nonlinear Model Predictive Control scheme. The controller is formulated accordingly in order to allow the system to operate in a constrained workspace with static obstacles. The goal of the proposed scheme is to guide the robot's end-effector towards the optimal grasping regions with guaranteed input and state constraints such as occlusion and obstacle avoidance, workspace boundaries and field of view constraints. The performance of the proposed strategy is experimentally verified using an 8 Degrees of Freedom KUKA Youbot in different reach-to-grasp scenarios.

In this paper, we consider a networked multi-robot system operating in an obstacle populated planar workspace under a single leader-multiple followers architecture. We propose a decentralized reconfiguration strategy of the set of connectivity and formation specifications that assures convergence to the desired point, while guaranteeing global connectivity. In particular, we construct a low-level Decentralized Navigation Functions based controller that encodes the goals and safety requirements of the system. However, owing to topological obstructions, stable critical points other than the desired one may appear. In such case, we employ a high-level distributed discrete procedure which attempts to solve a Distributed Constraint Satisfaction Problem on a local Voronoi partition, providing the necessary reconfiguration for the system to progress towards its goal. Eventually, we show that the system either converges to the desired point or attains a tree configuration with respect to the formation topology, in which case the system switches to a novel controller based on the Prescribed Performance technique, that eventually guarantees convergence. Finally, a simulation study clarifies and verifies the approach.

Structural Analysis is a lucrative option for Fault Detection and Identification in Unmanned Aerial Vehicles (UAVs), because it handles detailed, large-scale mathematical models. It can be employed by an on-board flight computer to generate residual generators and implement automatic fault-detection. Contemporary algorithms applied on dynamic systems may yield residual generators which require the real-time solution of Differential-Algebraic Equation (DAE) systems. Depending on the form and differential index of each DAE system, its solution may not be possible exclusively by computational means. In this paper we explore the relation between Structural Analysis algorithms and the forms of DAE systems they produce, propose conditions under which all generated DAEs are Structural Index-1 and semi-explicit and provide a large-scale fixed-wing UAV model with that property.

This paper addresses the fault tolerant control problem for an omni-directional mobile platform with four mecanum wheels moving on a well-known flat and constrained workspace with static obstacles. As a fault, we consider the case where a wheel cannot be actuated and hence it rotates freely around its drive shaft owing to the friction with the flat surface. Depending on the multitude of the faults, a robust motion control scheme is developed that achieves any desired configuration within the operational workspace, avoids collisions with the obstacles and does not violate the workspace boundaries despite the presence of dynamic model uncertainties. The challenge with respect to the current state of the art in fault tolerant control for such mobile platforms, where only one faulty wheel has been considered (i.e., the platform still retains its full actuation capabilities), lies in completely compensating up to two faulty wheels (i.e., the model becomes underactuated in this way) despite the dynamic model uncertainty and the presence of static obstacles in the workspace. Navigation Functions are innovatively incorporated with adaptive control techniques to deal with the parametric uncertainty in the robot dynamics, extending thus greatly the current state of the art in robust motion planning and collision avoidance by studying second order dynamics with parametric uncertainty. Finally, an extensive experimental study clarifies the proposed method and verifies its efficiency in various faults.

In this paper, we consider the formation control problem for multi-agent systems with unknown nonlinearities and disturbances, under an undirected communication protocol. Exploiting the recently developed prescribed performance control methodology, a robust distributed control scheme of minimal complexity is proposed that achieves and maintains arbitrarily fast and accurately the desired formation. No information regarding the agents' dynamic model is employed in the design procedure. Moreover, contrary to the related works on multi-agent systems, the transient and steady state response is fully decoupled by the underlying graph topology, the control gains selection and the agents' model uncertainties. In particular, the achieved performance is a priori and explicitly imposed by certain designer-specified performance functions. Finally, the theoretical findings are clarified and verified by an extensive simulation study.

This paper proposes an autonomous landing scheme for a small-scale flybarless helicopter equipped with low-cost navigation sensors. The main contribution of this paper is the design of a model-free motion controller that guarantees autonomous landing with prescribed transient and steady state response, despite the presence of external disturbances acting on the vehicle. The proposed control scheme is of low complexity and does not require any knowledge of the helicopter dynamic parameters. Hence, it can be easily implemented in embedded control platforms integrated on small-scale helicopters. In order to provide the controller with accurate estimation of the vehicle's state vector during the landing procedure, an asynchronous sensor fusion and state estimation algorithm, based on an Unscented Kalman Filter (UKF), has been also implemented. The performance and the efficiency of the overall scheme are experimentally verified using a small-scale flybarless helicopter in a real autonomous landing process.

Grasp planning in multi-robot systems is usually studied in a centralized setting with all robots sharing common knowledge about the overall system. Relaxing this assumption would allow multiple mobile manipulators to cooperate even without strict and precise coordination. Moreover, most typical tasks for cooperative settings, such as transporting heavy objects, require certain forces/torques to be exerted along/around particular directions, for instance, compensating for the weight of the transported object. In this paper, we propose task specific multi-robot grasp planning strategies that allow decentralized planning. Each agent plans its own actions without precise information about the other's plans. The approach is based on analysing a task specific grasp quality metric in a probabilistic context, compensating thus for the incomplete knowledge. Results from simulation experiments demonstrate that task independent planning is clearly inferior when task characteristics are known and thus task specific quality measures should be used. Furthermore, the proposed decentralized planning approaches clearly outperform the baseline and show close to globally optimal performance.

In this paper a novel aerial manipulation system is proposed. The mechanical structure of the system, the number of thrusters and their geometry will be derived from technical optimization problems. The aforementioned problems are defined by taking into consideration the desired actuation forces and torques applied to the end-effector of the system. The framework of the proposed system is designed in a CAD Package in order to evaluate the system parameter values. Following this, the kinematic and dynamic models are developed and an adaptive backstepping controller is designed aiming to control the exact position and orientation of the end-effector in the Cartesian space. Finally, the performance of the system is demonstrated through a simulation study, where a manipulation task scenario is investigated.

This paper addresses the problem of cooperative object transportation by two nonholonomic wheeled robots, with the coordination relying exclusively on implicit communication. We implement a leader-follower scheme, considering compliant contact between the object and the follower. Only the leader has knowledge of the object's goal configuration. The follower employs force/torque measurements to keep the contact stable and align itself with the object. The control scheme of the follower is based on the prescribed performance methodology guaranteeing thus the satisfaction of certain predefined force/torque constraints. In this way, the overall system acts as a perturbed version of the nominal car-like model. As a result, the leader implements a discontinuous control scheme, that drives robustly the system arbitrarily close to the goal configuration. No explicit data is exchanged among the robots, thus reducing bandwidth and increasing robustness and stealthiness. Finally, the proposed method is experimentally validated using two Pioneer mobile robots interconnected with a rod.

In this work we study the problem of landing a quadrotor on an inclined moving platform. The aerial robot employs an forward looking on-board camera to detect and observe the landing platform, which is carried by a mobile robot moving independently on an inclined surface. The platform may also be tilted with respect to the mobile robot. The overall goal is to design the aerial robot's control inputs such that it initially approaches the platform, while maintaining it within the camera's field of view and finally lands on it, in a way that minimizes the errors in position, attitude and velocity, while avoiding collision. Owing to the inclined ground and landing surface, the desired final state of the aerial robot is not an equilibrium state, which complicates significantly the control design. In that respect, a discrete-time non-linear model predictive controller was developed that optimizes both the trajectories and the time horizon, towards achieving the aforementioned objectives while respecting the input constraints as well. Finally, an extensive experimental study, with a Pioneer mobile robot and a Parrot ARDrone quadrotor, clarifies and verifies the theoretical findings.

It is well known that a real-time visual servoing task which employs a Visual Tracking Algorithm (VTA) imposes high computational cost to robotic system, which consequently results in higher energy consumption and lower autonomy. Motivated by this fact, this paper presents a novel Image Based Visual Servoing-Model Predictive Control (IBVS-MPC) scheme which is combined with a mechanism that decides when the VTA needs to be triggered and new control inputs must be calculated. Between two consecutive triggering instants, the control input trajectory is applied to the robot in an openloop fashion, i.e, no visual measurements and calculation of the control inputs are required during that period. This results in the reduction of the computational effort, energy consumption and increases the autonomy of the system. These factors are of utmost importance in the case of small autonomous robotic systems which perform vision based tasks, such as surveillance and inspection of indoors and outdoors environments. The visibility and inputs constraints, optimality rate of the MPC, as well as the external disturbances, are being considered during the control design. The efficiency of the proposed scheme is demonstrated through a set of real-time experiments using an eye-in-hand mobile robotic system.

This paper presents a vision-based Leader-Follower cooperative scheme which consists of two underwater vehicles in the absence of explicit communications and direct information interchange. A novel method for implicitly calculating the relative pose between the two vehicles is introduced. The absolute and relative localization algorithm is solely based on the observation of visual features projected on the vehicles common workspace and it is performed in a strictly decentralized manner. The cooperation task consists of a Leader vehicle which is tele-operated in an open-loop fashion while inspecting a flat surface and a Follower vehicle which follows the Leader while keeping a fixed 2D distance offset. The Follower is able to track the Leader's motion at all desired configurations via a motion tracking controller designed accordingly. The proposed control scheme for the Follower is relayed on vision-based implicit communications data and has analytically guaranteed stability and convergence properties. The accuracy and reliability of the implicit vision-based localization system, the performance of the designed control scheme, as well as the efficiency of the overall system in the proposed cooperative task, are experimentally verified using two small Remotely Operated Vehicles (ROVs) in a test tank.

In this paper we present an open-source design for the development of low-complexity, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism. The differential mechanism used is a variation of the whiffletree (or seesaw) mechanism, which introduces a set of locking buttons that can block the motion of each finger. The proposed design is unique since with a single motor and the proposed differential mechanism the user is able to control each finger independently and switch between different grasping postures in an intuitive manner. Anthropomorphism of robot structure and motion is achieved by employing in the design process an index of anthropomorphism. The proposed robot hands can be easily fabricated using low-cost, off-the-shelf materials and rapid prototyping techniques. The efficacy of the proposed design is validated through different experimental paradigms involving grasping of everyday life objects and execution of daily life activities. The proposed hands can be used as affordable prostheses, helping amputees regain their lost dexterity.

In this paper we introduce an index for the quantification of anthropomorphism of robot arms. The index is defined as a weighted sum of specific metrics which evaluate the similarities between the human and robot arm workspaces, providing a normalized score between 0 (non-anthropomorphic artifacts) and 1 (human-identical artifacts). The human arm workspaces were extracted using data reported in anthropometry studies. The formulation is general enough to allow utilization in various applications, by adjusting the weighting factors according to the specifications of each study. The proposed methodology can be used for assessing the human-likeness of existing robot arms as well as to provide specifications for the design of new anthropomorphic robots and prosthetic devices. To assess the efficiency of the proposed methods a comparative analysis between five kinematically different robot arm models is conducted and simulated paradigms are presented.

In this paper, we study the decentralized control problem of a two-agent system under local goal specifications given as temporal logic formulas. The agents collaboratively carry an object in a leader-follower scheme and lack means to exchange messages on-line, i.e., to communicate explicitly. Specifically, we propose a decentralized control protocol and a leader re-election strategy that secure the accomplishment of both agents' local goal specifications. The challenge herein lies in exploiting exclusively implicit inter-robot communication that is a natural outcome of the physical interaction of the robots with the object. An illustrative experiment is included clarifying and verifying the approach.

This paper addresses the problem of cooperative object manipulation with the coordination relying solely on implicit communication. We consider a decentralized leader-follower architecture where the leading robot, that has exclusive knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law. On the other hand, the follower estimates the leader's desired motion via a novel prescribed performance estimation law, that drives the estimation error to an arbitrarily small residual set, and implements a similar impedance control law. Both control schemes adopt feedback linearization as well as load sharing among the robots according to their specific payload capabilities. The feedback relies exclusively on each robot's force/torque, position as well as velocity measurements and apart from a few commonly predetermined constant parameters, no explicit data is exchanged on-line among the robots, thus reducing the required communication bandwidth and increasing robustness. Finally, a comparative simulation study clarifies the proposed method and verifies its efficiency.

In this paper, we consider the two dimensional (2-D) predecessor-following control problem for a platoon of unicycle vehicles moving on a planar surface. More specifically, we design a decentralized kinematic control protocol, in the sense that each vehicle calculates its own control signal based solely on local information regarding its preceding vehicle, by its on-board camera, without incorporating any velocity measurements. Additionally, the transient and steady state response is a priori determined by certain designer-specified performance functions and is fully decoupled by the number of vehicles composing the platoon and the control gains selection. Moreover, collisions between successive vehicles as well as connectivity breaks, owing to the limited field of view of cameras, are provably avoided. Finally, an extensive simulation study is carried out in the WEBOTSTM realistic simulator, clarifying the proposed control scheme and verifying its effectiveness.

The majority of the works on grasping consider both object as well as robot hand parameters to be accurately known and do not take into account the constraints imposed by the robot hand. In this paper, a complete methodology is proposed that handles the grasping problem under a wide range of uncertainties. Initially, we search for an acceptable posture that provides robustness against positioning inaccuracies and maximizes the ability of the robot hand to exert forces on the object. Subsequently, in order to secure the grasp stability, we also deal with the determination of sufficient contact forces. Finally, an appropriate tactile sensor setup, mounted on the robot hand, allow us to reduce the magnitude of uncertainty regarding the grasping parameters. The efficiency of our approach is validated through extensive experimental paradigms using a 15 DoF DLR/HIT II robotic hand attached at the end effector of a 7 DoF Mitsubishi PA10 robotic manipulator.

This paper presents a novel Vision-based Nonlinear Model Predictive Control (NMPC) scheme for an under-actuated underwater robotic vehicle. In this scheme, the control loop does not close periodically, but instead a self-triggering framework decides when to provide the next control update. Between two consecutive triggering instants, the control sequence computed by the NMPC is applied to the system in an open-loop fashion, i.e, no state measurements are required during that period. This results to a significant smaller number of requested measurements from the vision system, as well as less frequent computations of the control law, reducing in that way the processing time and the energy consumption. The image constraints (i.e preserving the target inside the camera's field of view), the external disturbances induced by currents and waves, as well as the vehicle's kinematic constraints due to under-actuation, are being considered during the control design. The closed-loop system has analytically guaranteed stability and convergence properties, while the performance of the proposed control scheme is experimentally verified using a small under-actuated underwater vehicle in a test tank.

In this paper, robustness analysis of constrained Image Based Visual Servoing based on Nonlinear Model Predictive Control (NMPC) is presented. It is known, that real applications such an aerial or a fast underwater robotic systems, suffer from the presence of external disturbances. These kinds of disturbances are inevitable in the physical systems, so it is of great interest to employ robust controllers. Therefore, a rigorous robustness analysis should be conducted. In this paper, the Image Based Visual Servoing system under the MPC framework is proven to be Input-to-State Stable (ISS) and a permissible upper bound of the disturbances is provided. Finally, the validity of the theoretic results is illustrated through a simulated example.

This paper describes the design and implementation of a robust position tracking control scheme for an Autonomous Underwater Vehicle (AUV). The proposed controller does not require knowledge of the vehicle's dynamic parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances acting on the vehicle. The resulting scheme is of low complexity and computational cost and thus can be easily integrated to an embedded control platform of an AUV. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona500 AUV into two different missions: a) navigation and stabilization to a specific configuration, b) meandrus-like trajectory tracking. In both cases the vehicle was under the influence of time-varying external disturbances caused by a high-pressure water jet installed on the Girona500 manipulator.

In this paper, we address the attitude and vertical stabilization problem for small-scale helicopters. An emergency controller that would successfully stabilize the helicopter in a safe flight mode when a pilot/autopilot fails to control it, owing to unexpected reasons, is of outmost importance in flight control systems. In this direction, we propose a low complexity nonlinear control scheme that drives the angles and the vertical speed to zero with prescribed transient and steady state response, without incorporating any knowledge of the dynamic model parameters in the control design. The stereographic coordinates were employed to model the attitude state of the helicopter in an attempt to guarantee the safe stabilization for every possible initial orientation without introducing any representation singularities as in the Euler angles representation or increasing complexity as in conventional four element quaternions. Moreover, the transient and steady state performance of the proposed scheme is a priori determined even in the presence of external disturbances. Furthermore, the overall control scheme can be easily implemented on embedded flight systems equipped with low-cost sensors. Finally, simulation and experimental results on a realistic platform verify the efficacy of the proposed method.

In this paper, we propose an optimization scheme for deriving task-specific force closure grasps for underactuated robot hands. Motivated by recent neuroscientific studies on the human grasping behavior, a novel grasp strategy is built upon past analysis regarding the task-specificity of human grasps, that also complies with the recent soft synergy model of underactuated hands. Our scheme determines an efficient force closure grasp (i.e., configuration and contact points/forces) with a posture compatible with the desired task, taking into consideration the mechanical and geometric limitations imposed by the design of the hand and the object shape. The efficiency of the algorithm is verified through simulated paradigms on a hypothetical underactuated hand with the kinematic model of the DLR/HIT II five fingered robot hand.

In this paper, we propose a robust model free control scheme of minimal complexity (it is a static scheme involving very few and simple calculations to output the control signal) for robotic manipulators, capable of achieving prescribed transient and steady state performance. No information regarding the robot dynamic model is employed in the design procedure. Moreover, the tracking performance of the developed scheme (i.e., convergence rate and steady state error) is a priori and explicitly imposed by a designer-specified performance function, and is fully decoupled by both the control gains selection and the robot dynamic model. In that respect, the selection of the control gains is only confined to adopting those values that lead to reasonable control effort. Finally, two experimental studies in the joint and the Cartesian workspace clarify the design procedure and verify its performance and robustness against external disturbances.

In this paper, we propose a complete methodology for deriving task-specific force closure grasps for multifingered robot hands under a wide range of uncertainties. Given a finite set of external disturbances representing the task to be executed, the concept of Q distance is introduced in a novel way to determine an efficient grasp with a task compatible hand posture (i.e., configuration and contact points). Our approach takes, also, into consideration the mechanical and geometric limitations imposed by the robotic hand design and the object to be grasped. In addition, incorporating our recent results on grasping [1], the ability of the robot hand to exert the required contact forces is maximized and robustness against positioning inaccuracies and object uncertainties is established. Finally, the efficiency of our approach is verified through an experimental study on the 15 DoF DLR/HIT II robotic hand attached at the end effector of the 7 DoF Mitsubishi PA10 robotic manipulator.

In this paper, we propose a novel image based visual servoing scheme that imposes prescribed transient and steady state response on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the limited field of view (FOV) of cameras. Visualizing the aforementioned performance specifications as error bounds, the key idea is to provide an error transformation that converts the original constrained problem into an equivalent unconstrained one, the stabilization of which proves sufficient to achieve prescribed performance guarantees and satisfy the inherent visibility constraints. The performance of the developed scheme is a priori and explicitly imposed by certain designer-specified performance functions, and is fully decoupled by the control gains selection, thus simplifying the control design. Moreover, its computational complexity proves significantly low. It is actually a static scheme involving very few and simple calculations to output the control signal, which enables easily its implementation on fast embedded control platforms. Finally, real-time experiments using an eye-in-hand robotic system verify the theoretical findings.

Tracking an underwater chain using an autonomous vehicle can be a first step towards more efficient solutions for cleaning and inspecting mooring chains. We propose to use a forward looking sonar as a primary perception sensor to enable the vehicle operation in limited visibility conditions and overcome the turbidity arisen during marine growth removal. Despite its advantages, working with acoustic imagery raises additional challenges to the involved image processing and control methodologies. In this paper we present a robust framework to perform chain following, combining perception, planning and control disciplines. We first introduce a detection system that exploits the sonar's high frame rate and applies local pattern matching to handle the complexity of detecting link chains in acoustic images. Then, a planning system deals with the dispersed detections and determines the link waypoints that the vehicle should reach. Finally, the vehicle is guided through these waypoints using a high level controller that has been tailored to simultaneously traverse the chain and keep track of upcoming links. Experiments on real data demonstrate the capability of autonomously follow a chain with sufficient accuracy to perform subsequent cleaning or inspection tasks.

In this paper we present a series of design directions for the development of affordable, modular, light-weight, intrinsically-compliant, underactuated robot hands, that can be easily reproduced using off-the-shelf materials. The proposed robot hands, efficiently grasp a series of everyday life objects and are considered to be general purpose, as they can be used for various applications. The efficiency of the proposed robot hands has been experimentally validated through a series of experimental paradigms, involving: grasping of multiple everyday life objects with different geometries, myoelectric (EMG) control of the robot hands in grasping tasks, preliminary results on a grasping capable quadrotor and autonomous grasp planning under object position and shape uncertainties.

This work proposes a motion planning method based on the construction of a roadmap connecting the critical points of a potential field or a distance function. It aims to overcome the limitation of potential field methods due to local minima caused by concave obstacles. The roadmap is incrementally constructed by a two-step procedure. Starting from a minimum, adjacent saddle-points are found using a local saddle-point search method. Then, the new saddle-points are connected to the minima by gradient descent. A numerical continuation algorithm from the computational chemistry literature is used to find saddle-points. It traces the valleys of the potential field, which are gradient extremal paths, defined as the points where the gradient is an eigenvector of the Hessian matrix. The definition of gradient bisectors is also discussed. The presentation conclude simulations in cluttered environments.

In this paper a comparative analysis between the human and three robotic hands is conducted. A series of metrics are introduced to quantify anthropomorphism and assess robot's ability to mimic the human hand. In order to quantify anthropomorphism we choose to compare human and robot hands in two different levels: comparing finger phalanges workspaces and comparing workspaces of the fingers base frames. The final score of anthropomorphism uses a set of weighting factors that can be adjusted according to the specifications of each study, providing always a normalized score between 0 (non-anthropomorphic) and 1 (human-identical). The proposed methodology can be used in order to grade the human-likeness of existing and new robotic hands, as well as to provide specifications for the design of the next generation of anthropomorphic hands. Those hands can be used for human robot interaction applications, humanoids or even prostheses.

In this work, we present a novel concept in the area of optimal grasp synthesis, confronting both geometric and mechanical constraints. Initializing from a locally optimal force distribution on some predefined feasible contact points, our method improves gradually the grasp quality avoiding simultaneously singularities and mechanical limitations. The proposed scheme implements sequential perturbations on the contact points and the wrist's position/orientation incorporating a post-optimality method in an iterative process to derive the consecutive optimal states. The main novelty of this work lies in the fact that only local information of the object's surface is required, which can be provided for instance by an appropriate tactile sensor suite. Finally, a simulation study on the DLR/HIT Hand II clarifies and verifies the efficiency of the approach.

This paper presents a Leader-Follower formation control strategy for underactuated marine vehicles which move under sensing and communication constraints in the presence of bounded persistent environmental disturbances. We assume that the vehicles do not communicate for exchanging information regarding on their states (pose and velocities), and that their sensing capabilities are restricted, due to limited range and angle-of-view. Sensing constraints are thus realized as a set of inequality state constraints which should never be violated (viability constraints). The viability constraints define a closed subset K of the configuration space (viability set K). The control objective is thus reduced into to coordinating the motion of the vehicles in a Leader-Follower formation, while system trajectories starting in K always remain viable in K. The proposed control design employs dipolar vector fields and a viability-based switching control scheme, which guarantees that system viability is always maintained. The efficacy of the proposed algorithm, as well as its relevance with surveillance of (stationary) targets are demonstrated through simulations.

This paper describes the design and implementation of a visual servo control scheme for an Autonomous Underwater Vehicle (AUV). The purpose of the control scheme is to navigate and stabilize the vehicle towards a visual target. The controller does not utilize the vehicle's dynamic model parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances representing ocean currents and waves. The proposed control scheme is of low complexity and can be easily integrated to an embedded control platform of an Autonomous Underwater Vehicle (AUV) with limited power and computational resources. Moreover, through the appropriate selection of certain performance functions, the proposed scheme guarantees that the target lies inside the onboard camera's field of view for all time. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona 500 AUV.

This paper describes the design and implementation of a model-based sonar servoing control scheme for Autonomous Underwater Vehicles (AUVs). The proposed controller is designed for autonomous surveillance of underwater structures and it is robust against external disturbances and parametric uncertainties in the AUV dynamic model. The sensor suite includes a Multi-beam Imaging Sonar which provides measurements to a RANSAC-based algorithm for structure detection and pose estimation of the vehicle with respect to the structure. The sonar-based pose estimation is properly fused with the rest of the state measurements provided by a navigation module and the resulted state vector is incorporated as feedback to the controller. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Nessie VI AUV in the presence of external disturbances (medium height waves).

We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on the differentiability of the model with respect to the parameters. The identification scheme consists of three distinct modules: a) System Excitation, b) Metric Calculator and c) Optimization Algorithm. The System Excitation module sends excitation inputs to the vehicle. The Optimization Algorithm module calculates a candidate parameter vector, which is fed to the Metric Calculator module. The Metric Calculator module evaluates the candidate parameter vector, using a metric based on the residual of the actual and the predicted commands. The predicted commands are calculated utilizing the candidate parameter vector and the vehicle state vector, which is available via a complete navigation module. Then, the metric is directly fed back to the Optimization Algorithm module, and it is used to correct the estimated parameter vector. The procedure continues iteratively until the convergence properties are met. The proposed method is generic, demonstrates quick convergence and does not require a linear formulation of the model with respect to the parameter vector. The applicability and performance of the proposed algorithm is experimentally verified using the AUV Girona 500.

In this paper teleoperation and telemanipulation with a robot arm (Mitsubishi PA-10) and a robot hand (DLR/HIT 2) is performed, using a human to robot motion mapping scheme that guarantees anthropomorphism. Two position trackers are used to capture position and orientation of human end-effector (wrist) and human elbow in 3D space and a dataglove to capture human hand kinematics. Then the inverse kinematics (IK) of the Mitsubishi PA-10 7-DoF robot arm are solved in an analytical manner, in order for the human's and the robot artifact's end-effectors to achieve same position and orientation in 3D space (functional constraint). Redundancy is handled in the solution space of the robot arm's IK, selecting the most anthropomorphic solution computed, with a criterion of “Functional Anthropomorphism”. Human hand motion is transformed to robot hand motion using the joint-to-joint mapping methodology. Finally in order for the user to be able to detect contact and “perceive” the forces exerted by the robot hand, a low-cost force feedback device, that provides a mixture of sensory information (visual and vibrotactile), was developed.

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

Studying human motor control has received increased attention during the past decades. Both the design and control of robotic artifacts may benefit from observation of human behavior. In this paper a novel method for capturing the dynamic behavior of the human hand is presented. The low dimensional kinematics of the human hand, including the wrist, and the low dimensional representation of the muscular activations were correlated through a linear time invariant (LTI) state space model. A linear output regulation controller was used in order to drive a simulated hand and the resulting trajectories were compared with the experimentally captured trajectories.

We extend Navigation Functions (NF) to worlds of more general geometry and topology. This is achieved without the need for diffeomorphisms, by direct definition in the geometrically complicated configuration space. Every obstacle boundary point should be partially sufficiently curved. This requires that at least one principal normal curvature be sufficient. A normal curvature is termed sufficient when the tangent sphere with diameter the associated curvature radius is a subset of the obstacle. Examples include ellipses with bounded eccentricity, tori, cylinders, one-sheet hyperboloids and others. Our proof establishes the existence of appropriate tuning for this purpose. Direct application to geometrically complicated cases is illustrated through nontrivial simulations.

This paper proposes a method to construct Navigation Functions (NF) from experimental trajectories in an unknown environment. We want to approximate an unknown obstacle function and then use it within an NF. When navigating the same destinations with the experiments, this NF should produce the same trajectories as the experiments. This requirement is equivalent to a partial differential equation (PDE). Solving the PDE yields the unknown obstacle function, expressed with spline basis functions. We apply this new method to anthropomorphic grasping, producing automatic trajectories similar to the observed ones. The grasping experiments were performed for a set of different objects, Principal Component Analysis (PCA) allows reduction of the configuration space dimension, where the learning NF method is then applied.

Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the musculoskeletal system of the upper limbs. In this paper, we study the muscular co-activation patterns during a variety of reach-to-grasp motions, and we introduce a learning scheme that can discriminate between different strategies. This scheme can then classify reach-to-grasp strategies based on the muscular co-activations. We consider the arm and hand as a whole system, therefore we use surface ElectroMyoGraphic (sEMG) recordings from muscles of both the upper arm and the forearm. The proposed scheme is tested in extensive paradigms proving its efficiency, while it can be used as a switching mechanism for task-specific motion and force estimation models, improving EMG-based control of robotic arm-hand systems.

In this paper we propose a generic methodology for human to robot motion mapping for the case of a robotic arm hand system, allowing anthropomorphism. For doing so we discriminate between Functional Anthropomorphism and Perceptional Anthropomorphism, focusing on the first to achieve anthropomorphic solutions of the inverse kinematics for a redundant robot arm. Regarding hand motion mapping, a “wrist” (end-effector) offset to compensate for differences between human and robot hand dimensions is applied and the fingertips mapping methodology is used. Two different mapping scenarios are also examined: mapping for teleoperation and mapping for autonomous operation. The proposed methodology can be applied to a variety of human robot interaction applications, that require a special focus on anthropomorphism.

Coupling the human upper limbs with robotic devices is gaining increasing attention in the last decade, due to the emerging applications in orthotics, prosthetics and rehabilitation devices. In the cases of every-day life tasks, force exertion and generally interaction with the environment is absolutely critical. Therefore, the decoding of the user's force exertion intention is important for the robust control of orthotic robots (e.g. arm exoskeletons). In this paper, the human arm manipulability is analyzed and its effect on the recruitment of the musculo-skeletal system is explored. It was found that the recruitment and activation of muscles is strongly affected by arm manipulability. Based on this finding, a decoding method is built in order to estimate force exerted in the three-dimensional (3D) task space from surface ElectroMyoGraphic (EMG) signals, recorded from muscles of the arm. The method is using the manipulability information for the given force task. Experimental results were verified in various arm configurations with two subjects.

This paper presents a solution to the robust practical stabilization of a unicycle-like marine vehicle, under non vanishing current-induced perturbations. A hysteresis-based switching control strategy is proposed, rendering the system globally practically stable to a set G around the origin. The control scheme consists of three control laws; the first one is active out of G and drives the system trajectories into G, based on a dipole-like vector field. The other two control laws are active in G and alternately regulate the position and the orientation of the vehicle. The system is shown to be robust, in the sense that the vehicle enters and remains into G even if only a maximum bound of the perturbation is known. The efficacy of the solution is demonstrated through simulation results.

Human-robot control interfaces have received increased attention during the last decades. These interfaces increasingly use signals coming directly from humans since there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from the muscles of the human upper limb are used as the control interface between the user and a robot arm. A switching regime model is used to decode the EMG activity of 11 muscles to a continuous representation of arm motion in the 3-D space. The switching regime model is used to overcome the main difficulties of the EMG-based control systems, i.e., the nonlinearity of the relationship between the EMG recordings and the arm motion, as well as the nonstationarity of EMG signals with respect to time. The proposed interface allows the user to control in real time an anthropomorphic robot arm in the 3-D space. The efficiency of the method is assessed through real-time experiments of four persons performing random arm motions.

This paper presents a visual servoing control scheme that is applied to an underwater robotic vehicle. The objective of the proposed control methodology is to provide a human operator the capability to move the vehicle without loosing the target from the vision system's field of view. On-line estimation of the vehicle states is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU) using an asynchronous Unscented Kalman Filter (UKF). A controller designed at the kinematic level, is backstepped into the dynamics of the system, maintaining its analytical stability guarantees. It is shown that the under-actuated degree of freedom is input-to-state stable and an energy based shaping of the user input with stability guarantees is implemented. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using a small Remotely Operated Vehicle (ROV) in a test tank.

This paper introduces a framework that guides the design of stabilizing feedback control laws for systems with Pfaffian constraints. A new class of N-dimensional vector fields, the dipole-like vector fields is proposed, inspired by the form of the flow lines of the electric point dipole. A general connection between the dipole-like field and the Pfaffian constraints of catastatic nonholonomic systems is exploited, to establish systematic guidelines on the design of stabilizing control laws. The methodology is applied to the stabilization of the unicycle and of the nonholonomic double integrator. Based on these guidelines, switching control laws are constructed. The efficacy of the methodology is demonstrated through simulation results.

This paper tackles the problem of tracking walking people with multiple moving robots equipped with laser rangefinders. We present an adaptation to the classic Multiple Hypothesis Tracking method, which allows for one-to-many associations between targets and measurements in each cycle and is thus capable of operating in a multi-sensor scenario. In the context of two experiments, the successful integration of our tracking algorithm to a dual-robot setup is assessed.

Humans perform a wide range of skillful and dexterous motion by adjusting the dynamic characteristics of their musculoskeletal system during motion. This capability is based on the non-linear characteristics of the muscles and the motor control architecture that can control motion and exerted force independently. Mechanical impedance (i.e. stiffness, viscosity and inertia) constitutes the most solid characteristic for describing the dynamic behavior of human movements. This paper presents a method for estimating upper limb impedance characteristics in the three-dimensional (3D) space, covering a wide range of the arm workspace. While subjects maintained postures, a seven-degrees-of-freedom (7-DoFs) robot arm was used to produce small displacements of subjects' hands along the three Cartesian axes. The end-point dynamic behavior was modeled using a linear second-order system and the impedance characteristics in the 3D space were identified using the measured forces and motion profiles. Experimental results were confirmed with two subjects.

This paper presents a novel modification of the Dual Unscented Kalman Filter (DUKF) for the on-line concurrent state and parameter estimation. The developed algorithm is successfully applied to an under-actuated underwater vehicle. Like in the case of conventional DUKF the proposed algorithm demonstrates quick convergence of the parameter vector. In addition, experimental results indicate an increased performance when the proposed methodology is utilized. The applicability and performance of the proposed algorithm is experimentally verified by combining the proposed DUKF with a non-linear controller on a modified Videoray ROV in a test tank. The on-line estimation of the vehicle states and dynamic parameters is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU).

Human motor control has always acted as an inspiration in both robotic manipulator design and control. In this paper, a modeling approach of anthropomorphism in human arm movements during every-day life tasks is proposed. The approach is not limited to describing static postures of the human arm but is able to model posture transitions, in other words, dynamic arm movements. The method is based on a novel structure of a Dynamic Bayesian Network (DBN) that is constructed using motion capture data. The structure and parameters of the model are learnt from the motion capture data used for training. Once trained, the proposed model can generate new anthropomorphic arm motions. These motions are then used for controlling an anthropomorphic robot arm, while a measure of anthropomorphism is defined and utilized for assessing resulted motion profiles.

Human-robot control interfaces have received increased attention during the past decades. With the introduction of robots in everyday life, especially in providing services to people with special needs (i.e., elderly, people with impairments, or people with disabilities), there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the user's skin, making the user's upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real time an anthropomorphic robot arm in 3-D space, using upper limb motion estimates based only on EMG recordings. Moreover, the proposed interface is robust to EMG changes with respect to time, mainly caused by muscle fatigue or adjustments of contraction level. The efficiency of the method is assessed through real-time experiments, including random arm motions in the 3-D space with variable hand speed profiles.

As robots come closer to humans, an efficient human-robot-control interface is an utmost necessity. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. A mathematical model is trained to decode upper limb motion from EMG recordings, using a dimensionality-reduction technique that represents muscle synergies and motion primitives. It is shown that a 2-D embedding of muscle activations can be decoded to a continuous profile of arm motion representation in the 3-D Cartesian space, embedded in a 2-D space. The system is used for the continuous control of a robot arm, using only EMG signals from the upper limb. The accuracy of the method is assessed through real-time experiments, including random arm motions.

In this paper we present a multi-agent coordination algorithm suitable for systems with aircraft-like kinematic constraints. A model of a system of input-constrained non-holonomic agents is constructed, suitable for use with formal verification tools. The agents considered are uniform and have bounded velocities and limited turning capabilities. We demonstrate how a model checker can be used to generate a counterexample trace for such a system, usable as a trajectory that satisfies our safety and liveness requirements.

In this paper, we discuss an algorithm for distributed formation of a lattice structures using a team of mobile robots. Lattice structures can be used for solving task allocation problems, as well as for utilizing cooperative swarm like motion for the agents. We are specifically dealing with the problem of constructing a lattice decentralized with the agents having only a limited sensing radius and a possibly imperfect communication channel that make ¿agree and go¿ methods unsuitable for solving the problem. The proposed algorithm is based on reducing the 2D problem to an 1D problem, i.e. moving on a curve that connects all the lattice points without intersecting itself. The algorithm is decentralized and is exact in the sense that all agents will converge to the lattice structure.

Human-robot control interfaces have received increased attention during the last decades. With the introduction of robots in every-day life, especially in developing services for people with special needs (i.e. elderly or impaired persons), there is a strong necessity of simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the userpsilas skin, letting the userpsilas upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real-time an anthropomorphic robot arm in three dimensional (3D) space, by decoding EMG signals to motion. However, since EMG changes due to muscle fatigue are present in this kind of control interface, a probabilistic framework has been developed, which can detect in real-time the muscle fatigue level. By complying to those fatigue-related signal changes, the proposed method can provide accurate decoding of motion through long periods of time. The system is used for the continuous control of a robot arm in 3D space, using only EMG signals from the upper limb. The method is tested for a long period of operation, proving that muscle fatigue does not affect the decoder accuracy. The efficiency of the method is assessed through real-time experiments including random arm motions in 3D space.

We propose an inverse agreement control strategy for multiple nonholonomic agents that forces the team members to disperse in the workspace in a distributed manner. Both the cases of an unbounded and a circular bounded workspace are considered. In the first case, we show that the closed loop system reaches a configuration in which the minimum distance between any pair of agents is larger than a specific lower bound. It is proved that this lower bound coincides with the agents' sensing radius. In the case of a bounded workspace, the control law is modified to force the agents to remain within the workspace boundary throughout the closed loop system evolution. Moreover the proposed control guarantees collision avoidance between the team members. The results are supported through relevant computer simulations.

In this paper we present a polynomial navigation function (NF) for a sphere world that can be constructed almost locally, with partial knowledge of the environment. The presented navigation function is C2and as a result the computational complexity is very low, while the construction uses local knowledge and information. Moreover, an almost locally computable diffeomorphism between convex obstacles and spheres is presented, allowing the NF scheme to be used in a workspace populated by convex obstacles. Our approach is not strictly local in the epsiv sense, i.e., the field around a point is not influenced only by an e region around the point, but rather it is local in the sense that the NF around each obstacle is influenced only by the obstacle and the adjacent obstacles. In particular, we require, in the vicinity of an obstacle, the distance between the obstacle and the adjacent obstacles. Simulations are presented to verify this approach.

There is a great effort during the last decades towards building robotic devices that are worn by humans. These devices, called exoskeletons, are used mainly for support and rehabilitation, as well as for augmentation of human capabilities. Providing a control interface for exoskeletons, that would guarantee comfort and safety, as well as efficiency and robustness, is still an issue. This paper presents a methodology for estimating human arm motion and force exerted, using electromyographic (EMG) signals from muscles of the upper limb. The proposed method is able to estimate motion of the human arm as well as force exerted from the upper limb to the environment, when the motion is constrained. Moreover, the method can distinguish the cases in which the motion is constrained or not (i.e. exertion of force versus free motion) which is of great importance for the control of exoskeletons. Furthermore, the method provides a continuous profile of estimated motion and force, in contrast to other methods used in the literature that can only detect initiation of movement or intention of force. The system is tested in an orthosis-like scenario, during planar movements, through various experiments. The experimental results prove the system efficiency, making the proposed methodology a strong candidate for an EMG-based control scheme applied in robotic exoskeletons.

In this paper, we introduce the concept of a novel robotic module, the ldquoR-Cellrdquo. R-Cell can be utilized in constructing distributed, homogeneous robotic systems. Each R-Cell is a rectangle endowed with: (a) motion capabilities provided by four revolutionary joints, each equipped with a clamping mechanism, and (b) deformation capabilities realized by four prismatic joints. The proposed module provides the resulting modular robotic structures with dexterous deformation and force creating capabilities. The resulting robotic structures are not defacto rigid, but can change shape & form even without any cell-reconfiguration. Our concept could be useful for a great variety of applications encompassing modular robotics like self-assembly, self-repair and reactive shape optimization just to mention a few, that conventional robots cannot accommodate.

This paper describes a position-based visual servo control scheme designed for an underwater vehicle. The methodology proposes a path planning technique, which guarantees that a flat target is kept in the camera optical field, while the vehicle avoids collision with the surface the target lays on. The vehicle pose (position and orientation) with respect to the target is obtained using a laser vision system (LVS). The LVS projects two laser dots in the image plane while it tracks the target using computer vision algorithms. The position of each laser dot in the image plane is directly related to the distance between the vehicle and the surface the target is located. The path planning strategy is based on the artificial potential field method (APF). The attractive part of the APF is responsible for minimizing the error between the current vehicle position and the desired. The repulsive part of the APF restricts the target inside the camera optical field while keeps away the laser dots from image regions related to small distances between the vehicle and the surface the target is located. The steering control of the vehicle is achieved by feeding the computed points of the path planning into a Cartesian kinematic controller, which was slightly modified for the needs of the methodology. The overall efficiency of the system, was proved through an extensive experimental procedure, using a small remotely operated vehicle (ROV) in a test tank.

We present a methodology to steer the end effector of a robotic manipulator, which is constrained in terms of joint rates, on the surface within the workspace. We develop controllers for stabilizing the end effector to a point, and for tracking a trajectory on this surface, while respecting the input constraints. We show that the resulting closed loop system is uniformly asymptotically stable and we verify our analytical development with computer simulations.

A distributed swarm aggregation algorithm is developed for a team of multiple kinematic agents. Specifically, each agent is assigned a control law, which is the sum of two elements: a repulsive potential field, which is responsible for the collision avoidance objective, and an attractive potential field, which forces the agents to converge to a configuration where they are close to each other. Furthermore, the attractive potential field forces the agents that are initially located within the sensing radius of an agent to remain within this area for all time. In this way, the connectivity properties of the initially formed communication graph are rendered invariant for the trajectories of the closed-loop system. It is shown that under the proposed control law, agents converge to a configuration where each agent is located at a bounded distance from each of its neighbors. The results are also extended to the case of nonholonomic kinematic unicycle-type agents and to the case of dynamic edge addition. In the latter case, we derive a smaller bound in the swarm size than in the static case.

In this paper, we propose a methodology for implementing multirobot navigation-function-based controllers to mixed teams of holonomic and nonholonomic agents. A new nonsmooth backstepping controller is introduced for translating kinematic controllers to equivalent dynamic ones, while maintaining bounded velocity specifications. The derived backstepping controller is applied to a dynamic model of the mobile robots, yielding a globally asymptotically stable dynamic controller. The effectiveness of the methodology is verified through nontrivial computer simulations.

In this paper we study the problem of controlling a micro robot which moves either on a line or on circle of predetermined radius. Locomotion constraints of this type are usual in micro robots, and therefore, it is important to have efficient and easy to implement ways of coping with these constraints. To solve the problem we present a multilevel motion controller that can be easily implemented on a robot with scarce computational resources as it does not rely on complex logical operations. It reduces the kinematics of the micro robot to the kinematics of a unicycle, screening in effect the micro behavior from the high level planner. Simulated results are provided to verify the proposed methodology.

In this paper we present a new navigation function for a sphere world that can be computed locally with limited knowledge of the environment. By requiring smooth and not analytic NF, the effect of each obstacle is exactly nullified outside a sensing zone around the obstacle (the only required parameter is the width of the sensing zone). This allows the computation of the navigation function using information from a single obstacle each time. We present simulations to verify the validity of this approach.

In this paper we present a methodology to drive the end effector of a robotic manipulator across the surface of an object in the workspace, and at the same time the manipulator can apply a force to the object, through its end-effector. Three typical tasks are considered, namely stabilization of the end effector over the object's surface and applying a specific force on it, motion planning and eventually trajectory tracking of the end effector across the object's surface. The proposed controllers utilize navigation functions and are based on the belt zone vector fields concept. The derived dynamic controllers are realized using an integrator backstepping methodology. The derived feedback based controllers guarantee global convergence and collision avoidance. The closed form solution provides fast feedback rendering the methodology particularly suitable for implementation on real time systems. The properties of the proposed methodology are verified through non-trivial computer simulations.

In robot teleoperation scenarios, the interface between the user and the robot is undoubtedly of high importance. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a remote robot arm. The proposed interface consists of surface EMG electrodes, placed at the user's skin at several locations on the arm, letting the user's upper limb free of bulky interface sensors or machinery usually found in conventional teleoperation systems. The motion of the human upper limb entails the activation of a large number of muscles (i.e. more than 30 muscles, not including finger movements). Moreover, the human arm has 7 degrees of freedom (DoFs) suggesting a wide variety of motions. Therefore, the mapping between these two high-dimensional data (i.e. the muscles activation and the motion of the human arm), is an extremely challenging issue. For this reason, a novel methodology is proposed here, where the mapping between the muscles activation and the motion of the user's arm is done in a low-dimensional space. Each of the high-dimensional input (muscle activation) and output (arm motion) vectors, is transformed into an individual low-dimensional space, where the mapping between the two low-dimensional vectors is then feasible. A state-space model is trained to map the low-dimensional representation of the muscles activation to the corresponding motion of the user's arm. After training, the state-space model can decode the human arm motion in real time with high accuracy, using only EMG recordings. The estimated motion is used to control a remote anthropomorphic robot arm. The accuracy of the proposed method is assessed through real-time experiments including motion in two-dimensional (2D) space.

This paper presents a methodology of teleoperating a robot arm, using electromyographic (EMG) signals and a trajectory monitoring technique based on human motion analysis. EMG signals from the flexor and extensor muscles of the elbow joint are used to predict the human elbow joint angle, using an auto-regressive moving average with exogenous output (ARMAX) model. A position tracker is attached in the user upper arm, before the elbow joint. It has been identified from previous works on human physiology that the trajectory of the human hand during planar catching tasks lays on a straight line. This motion law is used in order to monitor and refine the trajectory of the human hand that is predicted through EMG and the ARMAX model. The experimental results show that the ARMAX model estimation for the elbow angle, in conjunction with the trajectory monitoring technique, is able to predict the user motion with high accuracy, within different target points unknown to the system, and various hand velocities

This paper contains two main features: a probably correct distributed control strategy for convergence of multiple nonholonomic agents to a desired feasible formation configuration and a connection between formation infeasibility and flocking behavior in nonholonomic kinematic multi-agent systems. In particular, it is shown that when inter-agent formation objectives cannot occur simultaneously in the state-space then, under certain assumptions, the agents velocity vectors and orientations converge to a common value at steady state, under the same control strategy that would lead to a feasible formation. Convergence guarantees are provided in both cases using tools form algebraic graph theory and Lyapunov analysis. The results are verified through computer simulations. This is an extension of a result established in our previous work for multiple holonomic kinematic agents

A distributed feedback control architecture that guarantees collision avoidance and destination convergence for multiple sphere world holonomic agents is presented. The well established tool of decentralized navigation functions is redefined to cope with the communication restrictions of the system. Each agent plans its actions without knowing the destinations of the others and the positions of those agents lying outside its sensing neighborhood. The stability properties of the closed loop system are checked via Lyapunov stability techniques for hybrid systems. The collision avoidance and goal convergence properties are verified through simulations. The key advantage of the proposed algorithm with respect to the previous ones is the significant decrease of computational load and its applicability to large scale groups

In the area of micro-robotics, control problems have to cope with a number of issues not found on macro-scale robotics. Micro robots moving in the micro-world have limited capabilities and frequently must cooperate in large numbers. In this paper we focus at an object carrying task performed by a number of micro-robots. We study a number of combinations, of agent-object contact types and agent motion principles in order to find algorithms of decomposing a given object trajectory to agent trajectories.

In this paper we present a methodology that drives and stabilizes a robotic agent moving in a three dimensional environment, to a 2-dimensional manifold embedded in the workspace. Once the agent reaches the manifold, depending on the application, it performs a motion planning or a trajectory tracking task. Appropriately constructed belt-zone vector fields guarantee that the agent will not depart the 2-D manifold proximity area, while carrying out the motion planning or trajectory tracking task. The derived closed form feedback control law guarantees global convergence and collision avoidance. The properties of the proposed algorithm are verified through non-trivial computer simulations.

A methodology for a robotic manipulator teleoperation is presented. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller but electromyographic (EMG) signals from the muscles of a human arm. EMG signals are acquired from biceps brachii, main responsible muscle for elbow flexion. The robot elbow is controlled using joint angle computed from EMG signal during smooth forearm motion, while the shoulder of the robot is controlled by a position tracker placed on the user's arm. Identification techniques are used to approximate the user-dependent parameters of the model used to compute the elbow angle based on EMG signals.

In this paper we consider the problem of navigating a team of input constraint robotic agents. The main motivation comes from the field of micro-robotics, where several input sets are more favorable than others, in the sense of incurring a lower relative actuation error. A switching controller that actuates the system in those favored input regions is derived based on Multirobot Navigation Functions. The system is shown to possess theoretically guaranteed global convergence and collision avoidance properties. Non-trivial computer simulations show the effectiveness of the methodology.

A dead-reckoning scheme appropriate for skid-steered mobile robots is introduced to serve in a wider scheme for simultaneous localization and map-building. It is based on internal sensors (inertial data and odometry) only. The information from an experimentally derived kinematic model and an onboard inertial navigation system (INS), after a necessary pre-filtering stage, is fused using a simple and fast modified Kalman filter. We verify our approach with large scale experiments in outdoors structured environments and variable terrains, following paths with steep turns and variable velocity.

This paper represents an extension of our previous work [D. Dimarogonas, et al. (2003), S. Loizou, et al. (2003)] on multiagent navigation to the case of decentralized control of multiple nonholonomic vehicles. Our main motivation comes from the field of air traffic management systems and from the field of micro robotic multiagent systems. A discontinuous feedback control scheme, based on dipolar navigation fields, is implemented and integrator backstepping is applied to suppress chattering behavior. The methodology has guaranteed global convergence and collision avoidance properties, which are verified by nontrivial computer simulations.

We present a methodology that simultaneously provides estimates of the self-motion of a skid-steered mobile robot and detects a moving obstacle estimating its velocity, exclusively based on laser scanner measurements. We assume an environment of linear segments and since the vehicle is skid-steered we make no use of its encoder readings. The constraints/assumptions are discussed and extensive experimental results are presented.

In this paper we incorporate dipolar potential fields used for nonholonomic navigation into a novel potential function designed for multi-robot navigation. The derived navigation function is suitable for navigation of multiple nonholonomic vehicles. A properly designed discontinuous feedback control law is applied to steer the nonholonomic vehicles. The derived closed form control scheme provides robust navigation with guaranteed collision avoidance and global convergence properties, as well as fast feedback, rendering the methodology particularly suitable for real time implementation. Collision avoidance and global convergence properties are verified through non-trivial computer simulations.

We apply a novel motion planning and control methodology, which is based on a non-smooth navigation function, to a point mobile robot moving amongst moving obstacles. The chattering introduced by the discontinuous potential field is suppressed using non-smooth backstepping. The combined controller guarantees global asymptotic convergence and collision avoidance. This controller is particularly suitable for real time implementation on systems with limited computational resources. The effectiveness of the proposed scheme is verified through computer simulations.

This paper presents the first motion planning methodology applicable to articulated, nonpoint nonholonomic robots with guaranteed collision avoidance and convergence properties. It is based on a new class of nonsmooth Lyapunov functions and a novel extension of the navigation function method to account for nonpoint articulated robots. The dipolar inverse Lyapunov functions introduced are appropriate for nonholonomic control and offer superior performance characteristics compared to existing tools. The new potential field technique uses diffeomorphic transformations and exploits the resulting point-world topology. The combined approach is applied to the problem of handling deformable material by multiple nonholonomic mobile manipulators in an obstacle environment to yield a centralized coordinating control law. Simulation results verify asymptotic convergence of the robots, obstacle avoidance, boundedness of object deformations, and singularity avoidance for the manipulators.

The paper presents a robust gesture segmentation and recognition scheme in real images using statistical pattern recognition techniques, like data clustering and linear regression. Specifically, a hierarchical clustering algorithm is adopted because it does not require the exact number of sought clusters. Thus the proposed gesture recognition scheme is capable of coping with gestures having a variable number of extended fingers, a common situation in many practical applications like the expanded user-machine interface and the automatic deaf-mute sign language translation. For the mathematical modeling of clusters, a linear regression scheme is used. While in other cases linear regression is a simplification made for time saving, in this case it also ensures representation accuracy due to the geometry of the human hand being mostly composed of linear segments. Statistical linear modeling enables the handling of points with extreme values in comparison to the rest (outliers). As a result, the suggested algorithm is not affected by pixels that have been mistakenly selected by the image processing algorithms.

Presents a method of performing integrator backstepping in systems that are discontinuous, either due to their inherent structure or because of the applied control input. The proposed technique is applied to the stabilization problem of the dynamic system of a nonholonomic mobile robot. Simulation studies indicate that the methodology can also help alleviate the problem of chattering that is commonly associated with discontinuous nonholonomic controllers.

We have developed a laser scanner based simultaneous localization and map building method, specifically addressing the divergence problem of the classical extended Kalman filters (EKF) based simultaneous localization and map building (SLAM) algorithms. Our method utilizes two EKFs. The first is used to estimate the orientations of the robot and the obstacles, and the second estimates the positions of the robot and of the obstacles. Experimental results are also presented to verify our arguments.

We extend the navigation function methodology, established for single robot navigation, to the case of multiple robots. Appropriate expressions for the robot potential functions guarantee global convergence. The derived closed form navigation function provides a robust navigation scheme, suitable for real time implementation. The collision avoidance and global convergence properties are verified through simulations.

A gesture segmentation and classification methodology without restrictions regarding the presence of other objects is presented. The aim is to provide a framework in which application of the deformable templates method does not lead to difficult minimization problems, requiring simulated annealing solution procedures. Experiments with natural images of unknown gestures of various sizes, positions and directions have been conducted.

The paper presents a new position/force controller, based on the philosophy of the parallel approach. The controller exploits the reaction compensation action of the inverse dynamics position controller and achieves superior transient performance. It incorporates a velocity dependent damping term. Stability is established and conditions for the control parameters are derived. Performance of the proposed controller is verified through computer simulations.

This paper presents a motion planner and nonholonomic controller for a mobile robot, with global collision avoidance and convergence properties. An appropriately designed (dipolar) potential field is combined with discontinuous state feedback. A new class of Lyapunov functions is introduced and used for nonholonomic navigation. The obstacle avoidance and global asymptotic stability properties are verified through simulations.

A nonholonomic motion planner for mobile manipulators moving in cluttered environments is presented. The approach is based on a discontinuous feedback law under the influence of a special potential field. Convergence is shown via Lyapunov's direct method. Utilizing redundancy, the methodology allows the system to perform secondary, configuration dependent, objectives such as singularity avoidance. It introduces an efficient feedback scheme for real time navigation of nonholonomic systems.

A manipulated deformable object is viewed as an underactuated mechanical system. In this context controllability issues are discussed and results on the nature of the constraints and the controllability properties of an important class of deformable objects being modeled with finite elements are stated. For this class of deformable objects the results permit to circumvent the usual procedure of calculating Lie brackets to establish a base for the associated Lie algebra, and answers the question of determining the kind of constraints imposed on the system in a straightforward algebraic way. Inequality constraints associated to material strength limitations are also included.

We extend our previous work (1997, 1998) on steering a robotic snake and we now address the problems of (i) stabilization about an arbitrary configuration and (ii) tracking an arbitrary trajectory. For stabilization we apply three different algorithms for the kinematic model derived in the previous work. Comparisons of the three feedback stabilizing controls are made. Trajectory tracking is firstly addressed under the notion of flat systems. In a second approach we make use of a dynamic state feedback in order to achieve dynamic decoupling of our system.

In this paper a new kind of robotic mechanism is proposed to be used for inspection tasks in complex setups of industrial plants. The shape and motion capabilities of biological snakes are adopted as generic design guidelines. We propose a multi-articulated mobile robot, with a body consisting of repeating modules, capable for both moving efficiently and reaching points inside complicated or unstructured areas, where human personnel cannot reach or work properly. An analysis of the basic design along with most of the component specifications is presented. Finally, we present its forward kinematics, as a first step towards controlling it.

The increasing demand for robotic applications in unstructured environments is motivating the need for dextrous end-effecters which can cope with the wide variety of tasks and objects encountered in these environments. A new anthropomorphic robot hand (Anthrobot) which was designed for these environments is described. In this paper the problem of developing the kinematics and unit consistent hybrid position/force control of the Anthrobot fingers is considered as the first step toward its utilization for either teleoperation or autonomous operation. A dynamic model of the fingers is identified and used to develop the control laws. The experimental results of applying the control to the Anthrobot are discussed.

We treat the problem of navigation of a wheeled nonholonomic vehicle in a cluttered environment by: (i) finding, at every instant, a new collision free reference position using potential fields, and (ii) tracking this using nonholonomic tracking. Thus, our feedback solution can be applied in real time and guarantees both convergence to a final position and collision avoidance. The issue of final orientation accommodation is resolved for a number of practical configurations by using a fictitious obstacle around the goal state.

A formulation that makes possible the integration of collision prediction and avoidance stages for mobile robots moving in general terrains containing moving obstacles is presented. A dynamic model of the mobile robot and the dynamic constraints are derived. Collision avoidance is guaranteed if the distance between the robot and a moving obstacle is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. A feedback control is developed and local asymptotic stability is proved if the velocity of the moving obstacle is bounded. Simulation results verify the value of the proposed strategy.>

A control strategy for real-time collision avoidance of a mobile robot in an environment containing moving obstacles is proposed. Objects, including the robot, are modelled as convex polyhedra. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from offline planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Consistency with the nominal plan is desirable. The process is formulated as an optimization problem and a close to optimal solution is obtained.>

A Petri net model of the coordination level of an intelligent mobile robot system (IMRS) is presented. The purpose of this model is to specify the integration of the individual efforts on path planning, supervisory motion control, and vision system that are necessary for the autonomous operation of a mobile robot in a structured dynamic environment. This is achieved by analytically modeling the various units of the system as Petri net transducers and explicitly representing the task precedence and information dependence among them. The model can be used to simulate the task processing and evaluate the efficiency of operations and the responsibility of decisions in the coordination level of the intelligent mobile robot system. Some simulation results of the task processing and learning are presented.>

A simple method of trajectory generation of robot manipulators is presented. It is based on an optimal control problem formulation. The jerk, the third derivative of position, of the desired trajectory, adversely affect the efficiency of the control algorithms and therefore should be minimized. assuming joint position, velocity and acceleration to be constrained, a cost criterion containing jerk is considered. Initially, the simple environment without obstacles and constrained by the physical limitations of the joint angles only is examined. For practical reasons, the free execution time has been used to handle the velocity and acceleration constraints instead of the complete bounded state variable formulation. The problem of minimizing the jerk along an arbitrary Cartesian trajectory is formulated and given analytical solution, making this method useful for real-world environments containing obstacles.>