This paper addresses the Bearing-Only Tracking problem of an underwater acoustic source using multiple Autonomous Underwater Vehicles. The vehicles, equipped with Passive Acoustic Monitoring sensors, must communicate to exchange their local target Direction of Arrival estimates through acoustic communication systems, which are typically characterized by low bandwidth and high latencies. The different availability of local measurements compared to those coming from the other vehicles in the team can create situations where one vehicle is forced to generate an estimate based on measurements that are heavily unbalanced toward one source. This negatively impacts the numerical balance of the regressor matrix used for the estimation, potentially leading to biased results. This paper proposes a weighting method to mitigate this effect, validating it through numerical simulations.

Bearing Only Tracking of an underwater moving acoustic source employing multiple moving sensors is addressed in this paper. The vehicles need to communicate to exchange their local measurements coming from Passive Acoustic Monitoring sensors with an inevitable communication latency and packet loss, which has to be to be properly addressed in the algorithm. Additionally, The limited velocity of the sound in water causes the sensors to receive asynchronous data caused by the sound propagation delay. A distributed, iterative, optimization algorithm taking into account all these aspects is proposed and numerically verified on realistic simulations to validate the proposed approach.

Redundancy in robot structures allows the implementation of control algorithms in which it is possible to add secondary control objectives. Those are typically functions to be minimized/maximized and projected onto the null-space of the primary control objectives. As an example, typical metrics to maximize are the robot manipulability or the distance from its mechanical joint limits. Usually, designer's heuristics is used to decide which function eventually to optimize. This paper shows that heuristics may lead to counter-intuitive results such as, for example, reducing the dexterous workspace with respect to, e.g., avoiding optimization at all. A learning algorithm is proposed to allow the robot to dynamically select the function to optimize in a way to increase the overall dexterous workspace with respect to the static, heuristic choice. As a result, the robot will be able to increase its dexterous workspace by selecting the proper lower-priority task via the use of a neural network trained during a proper supervised learning process. A 3-link planar manipulator is used as numerical case study.

Task-priority inverse kinematics is a popular motion control algorithm which efficiently handles redundancy in robot manipulators. It has been recently extended in order to handle also set-based control objectives or inequality constraints. As any local motion planner it is prone to the occurrence of local minima. This work further extends set-based inverse kinematics by adding a motion planner in order to avoid such occurrence. Motion planners are usually computationally heavy especially in their eventual implementation with a task-priority architecture. To reduce this issue, the planner is implemented as a sampling-based algorithm which works in the reduced-dimensionality of the robot workspace applying Cartesian constraints only. The output trajectory is then checked against the inverse kinematics algorithm exploiting the redundancy and verifying the fulfillment of the joint-based task constraints. During the motion, inverse kinematics is then used also in real-time to ensure a reactive behavior to address, e.g., mismatch between the a-priori information and real-time perception acquisition. Also, the motion planner runs in background to adapt to changes in the environment or to accommodate incremental mapping. Comparison with alternative approaches are investigated and discussed. The most promising method is validated first in hundreds of numerical simulations to provide a solid statistical analysis and then experimentally with a Kinova Jaco2 7 DOFs manipulator equipped with an RGB-D sensor. Note to Practitioners—In this work we propose a motion planning algorithm that allows to effectively deploy robot manipulators in partially unstructured environments. It is structured in two layers: a Cartesian space global planner guarantees the feasibility of the trajectory with respect to potential obstacles in the environment, while a reactive local planner checks the feasibility at joint level. This architecture allows to define all the safety constraints both at Cartesian and joint space needed to operate in unstructured environments, while keeping the computation time lower with respect to standard approaches that define all the constraints in an offline global planner. The reduced computation time allows to effectively perform real-time replanning operations when needed, making the robotic system capable of adapting to a dynamic environment and suitable for sharing its workspace with human operators.

Many operations performed by work class Remotely Operated Vehicles (ROVs) require the manipulation of heavy loads. An example is the manipulation and grouting of armour stones. A way to increase the working capabilities of the ROV is to introduce cables among the set of actuators. The cable lengths and tensions are controlled by winches placed on the vehicle. Being similar to a cable-driven parallel robot (CDPR), the resultant system inherits some advantages such as the possibility to generate large forces over a large workspace and the possibility to use CDPR techniques to estimate the pose of the ROV. This paper proposes a complete control architecture for the Hybrid Cable-Thruster actuated ROV (HCT-ROV) and analyzes, in computer simulations, the performances of such a system while it performs real world operations, such as heavy lifting and hovering in presence of water current.

In this paper an experimental study of set-based task-priority kinematic control for a dual-arm mobile robot is developed. The control strategy for the coordination of the two manipulators and the mobile base relies on the definition of a set of elementary tasks to be properly handled depending on their functional role. In particular, the tasks have been grouped into three categories: safety, operational and optimization tasks. The effectiveness of the resulting task hierarchy has been validated through experiments on a Kinova Movo robot, in a domestic use case scenario.

The paper presents a multiple task-priority inverse kinematics algorithm for a dual-arm aerial manipulator. Both tasks defined as equality constraints and inequality constraints are handled by means of a singularity robust method based on the Null-Space based Behavioral control. The proposed schema is constituted by the inverse kinematics control, that receives the desired behavior of the system and outputs the reference values for the motion variables, i.e. the UAV pose and the arm joints position, and a motion control, that computes the vehicle thrusts and the joint torques. The method has been experimentally validated on a system composed by an underactuated aerial hexarotor vehicle equipped with two lightweight 4-DOF manipulators, involved in operations requiring the coordination of the two arms and the vehicle.

Set-Based Multi-Task Priority is a recent framework to handle inverse kinematics for redundant structures. Both equality tasks, i.e., control objectives to be driven to a desired value, and set-bases tasks, i.e., control objectives to be satisfied with a set/range of values can be addressed in a rigorous manner within a priority framework. In addition, optimization tasks, driven by the gradient of a proper function, may be considered as well, usually as lower priority tasks. In this paper the proper design of the tasks, their priority and the use of a Set-Based Multi-Task Priority framework is proposed in order to handle several constraints simultaneously in real-time. It is shown that safety related tasks such as, e.g., joint limits or kinematic singularity, may be properly handled by consider them both at an higher priority as set-based task and at a lower within a proper optimization functional. Experimental results on a 7DOF Jaco2arm with and without the proposed approach show the effectiveness of the proposed method.

Within the European project DexROV the topic of underwater intervention is addressed. In particular, a remote control room is connected through a satellite communication link to surface vessel, which is in turn connected to an UVMS (Underwater Vehicle-Manipulator System) with an umbilical cable. The operator may interact with the system using a joystick or exoskeleton. Since a direct teleoperation is not feasible, a cognitive engine is in charge of handling communication latency or interruptions caused by the satellite link, and the UVMS should have sufficient autonomy in dealing with low level constraints or secondary objectives. To this purpose, a task-priority-based inverse kinematics algorithm has been developed in order to allow the operator to control only the end effector, while the algorithm is in charge of handling both operative and joint-space constraints. This paper describes some preliminary experimental results achieved during the DexROV campaign of July 2017 in Marseilles (France), where most of the components have been successfully integrated and the inverse kinematics nicely run.

In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time computation of the minimum distance between the manipulator and other objects in the environment using depth measurements has been implemented, effectively allowing obstacle avoidance tasks. Experiments with a Jaco2manipulator, operating in an environment where an RGB-D sensor is used for the obstacles detection, show the effectiveness of the developed system.

In this paper we present an architecture for the operation of an assistive robot finally aimed at allowing users with severe motion disabilities to perform manipulation tasks that may help in daily-life operations. The robotic system, based on a lightweight robot manipulator, receives high level commands from the user through a Brain-Computer Interface based on P300 paradigm. The motion of the manipulator is controlled relying on a closed loop inverse kinematic algorithm that simultaneously manages multiple set-based and equality-based tasks. The software architecture is developed relying on widely used frameworks to operate BCIs and robots (namely, BCI2000 for the operation of the BCI and ROS for the control of the manipulator) integrating control, perception and communication modules developed for the application at hand. Preliminary experiments have been conducted to show the potentialities of the developed architecture.

This paper presents the design, control, and experimental validation of a novel fully-actuated aerial robot for physically interactive tasks, named Tilt-Hex. We show how the Tilt-Hex, a tilted-propeller hexarotor is able to control the full pose (position and orientation independently) using a geometric control, and to exert a full-wrench (force and torque independently) with a rigidly attached end-effector using an admittance control paradigm. An outer loop control governs the desired admittance behavior and an inner loop based on geometric control ensures pose tracking. The interaction forces are estimated by a momentum based observer. Control and observation are made possible by a precise control and measurement of the speed of each propeller. An extensive experimental campaign shows that the Tilt-Hex is able to outperform the classical underactuated multi-rotors in terms of stability, accuracy and dexterity and represent one of the best choice at date for tasks requiring aerial physical interaction.

In this paper a three layer control architecture for multiple aerial robotic manipulators is presented. The top layer, on the basis of the desired mission, determines the end-effector desired trajectory for each manipulator, while the middle layer is in charge of computing the motion references in order to track such end-effectors trajectories coming from the upper layer. Finally the bottom layer is a low level motion controller, which tracks the motion references. The overall mission is decomposed in a set of elementary behaviors which are combined together, through the Null Space-based Behavioral (NSB) approach, into more complex compounds behaviors. The proposed framework has been tested conducting an experimental campaign.

In this paper, an impedance control scheme for aerial robotic manipulators is proposed, with the aim of reducing the end-effector interaction forces with the environment. The proposed control has a multi-level architecture, in detail the outer loop is composed by a trajectory generator and an impedance filter that modifies the trajectory to achieve a complaint behaviour in the end-effector space; a middle loop is used to generate the joint space variables through an inverse kinematic algorithm; finally the inner loop is aimed at ensuring the motion tracking. The proposed control architecture has been experimentally tested.

This work tackles the problem of controlling an Unmanned Aerial Vehicle equipped with a robotic Manipulator and it has been developed within the framework of the EU-funded ARCAS (Aerial Robotics Cooperative Assembly System) project. A behavioral control, based on the Null Space-based Behavioral (NSB) paradigm, is proposed to tackle the coordination between the arm and vehicle motions. To this aim, a set of basic functionalities (called elementary behaviors) are designed and combined in a priority order to attain complex tasks (called compound behaviors). The proposed controller has been experimentally validated on a multirotor aircraft with an attached 6 Degree of Freedoms manipulator. Two experimental case studies, involving several compound behaviors, have been reported and the results show the effectiveness of the approach.

In this paper, a decentralized control strategy for networked multi-robot systems that allows the tracking of the team centroid and the relative formation is presented. The proposed solution consists of a distributed observer-controller scheme where, based only on local information, each robot estimates the collective state and tracks the two assigned control variables. We provide a formal stability analysis of the observer-controller scheme and we relate convergence properties to the topology of the connectivity graph. Experiments are presented to validate the approach.

Coverage of a given area by means of coordinated autonomous robots is a mission required in several applications such as, for example, patrolling, monitoring or environmental sampling. From a mathematical perspective, this can often be modeled as the need to estimate a scalar field, eventually time varying as in the security applications. In this paper, the problem is addressed for the challenging underwater scenario, where localization and communication pose additional constraints. The solution exploits the appealing properties of the Voronoi partition of a convex set within a probabilistic framework. In addition, the algorithm is totally distributed and characterized by a strong engineering perspective allowing the handling of asynchronous communication or possible loss or adjunct of vehicles. Beyond the test in dozen of numerical case studies, the algorithm has been validated by a challenging underwater test in 3 dimension involving two Autonomous Underwater Vehicles (AUVs). The experiments were run in the La Spezia harbor, in Italy, in February 2012 as demo of the European project Co3AUVs.

In this paper, an adaptive trajectory tracking controller for quadrotor MAVs is presented. The controller exploits the common assumption of a faster orientation dynamics w.r.t. the translational one, and is able to asymptotically compensate for parametric uncertainties (e.g., displaced center of mass), as well as external disturbances (e.g., wind). The good performance of the proposed controller is then demonstrated by means of an extensive experimental evaluation performed with a commercially-available quadrotor MAV.

A coordination mechanism to achieve the sampling task of static or dynamic fields by means of a system composed by multiple mobile robots is addressed in this paper. The problem is the estimation of a scalar field. To this aim in a probabilistic framework a solution is proposed that takes into account several constraints. The attention is focused on the vehicles motion generation and the developed strategy is designed for multiple, autonomous and distributed robots. It makes use of the Voronoi tessellation's properties to automatically distribute the vehicles' motion and of the Null-Space-Behavioral control to handle eventually conflicting motion tasks (as reaching a given point while avoiding obstacles). The algorithm can be tailored based on the communication and computational capabilities of the robots. A discussion and possible counterexamples of the applications of existing approaches are provided in the paper. Numerical simulations illustrate the results.

This paper describes a decentralized coordination strategy for multi robot patrolling missions. To this effect, the theory of Gaussian Processes (usually used for estimation purposes) is suitably adapted to tackle the problem of harbour patrolling. The introduction of a time varying dependency in the probabilistic formulation (thus allowing for the sampled field to be dynamic, i.e., changing in time) makes the proposed solution suitable for the type of mission considered. Moreover, the advantages of Voronoi tessellations are exploited to automatically distribute the vehicles over the environment. The resulting algorithm takes into account several constraints and can be tailored based on the communication and computational capabilities of the robots, thus making it suitable for heterogeneous systems. Numerical simulations and experiments involving three autonomous marine surface vehicles in a harbour scenario at the Parque Expo site in Lisbon are discussed.

In this paper a decentralized controller-observer scheme for centroid tracking with a multi-robot system is presented. The key idea is to develop, for each robot, an observer of the collective system's state; each local observer is updated by only using information of the state of the robot and of its neighbors. The local observers' estimations are then used by the individual robots to cooperatively track an assigned time-varying reference for the weighted centroid. Convergence of the scheme is proven for both fixed and switching communication topologies, as well as for directed and undirected communication graphs. Numerical simulations relative to different case studies are illustrated to validate the approach.

This paper addresses the problem of observability of the relative motion of two AUVs equipped with velocity and depth sensors, and inter-vehicle ranging devices. We start by exploiting nonlinear observability concepts to analyze, using observability rank conditions, some types of relative AUV motions. Because rank conditions only provide binary information regarding observability, we then derive a specific observability index (metric) and study its dependence on the types of relative motions executed by the vehicles. In particular, it is shown that the degradation of observability depends on the range and angle between the relative velocity and position vectors. The problem addressed bears affinity with that of single beacon localization. For this reason, the results derived are validated experimentally in a equivalent, single beacon navigation scenario.

The paper studies the observability properties of the relative localization of two Autonomous Underwater Vehicles (AUVs) equipped with depth sensors, linear/angular velocity sensors, and communication devices with range measurement. The conditions that ensure observability of the linearized model and locally weak observability of the nonlinear system are derived. An Extended Kalman Filter is then designed aimed at estimating the relative position between two AUVs. Simulations in 3D and reconstruction from experimental data in 2D provide a numerical validation of the analysis.

Coordinated control of marine vehicles poses challenging problems, among them the possibility to use the vehicles, in addition to their nominal mission, also to achieve a relative localization task. To the purpose, the use of a surface, GPS-equipped, vehicle and one or several underwater vehicles may be envisaged. The latters can communicate among them by acoustic modems; those devices can also be used as ranging measurement units thus providing an additional information that, together with the common sensor equipment for marine vehicles, might be used for relative localization. This paper investigates how the vehicles' movements can be commanded in order to help the relative localization by a proper analysis of the system observability and a corresponding proper definition of the vehicles movements; critical situations for the relative observability, that corresponds to common movements, are then avoided. Numerical simulations on the mathematical model of the Fòlaga hybrid underwater vehicle confirm the effectiveness of the proposed coordinated behavioral approach.

Differential-drive mobile robots are usually equipped with video-cameras for navigation purposes. In order to ensure proper operational capabilities of such systems, several calibration steps are required to estimate the following quantities: the video-camera intrinsic and extrinsic parameters, the relative pose between the camera and the vehicle frame and, finally, the odometric parameters of the vehicle. In this paper the simultaneous estimation of the above mentioned quantities is achieved by a systematic and effective calibration procedure that does not require any iterative step. The calibration procedure needs only on-board measurements given by the wheels encoders, the camera and a number of properly taken camera snapshots of a set of known landmarks. Numerical simulations and experimental results with a mobile robot Khepera III equipped with a low-cost camera confirm the effectiveness of the proposed technique.

This paper proposes a multiple robot control algorithm to approach the problem of patrolling an open or closed line. The algorithm is fully decentralized, i.e., no communication occurs between robots or with a central station. Robots behave according only to their sensing and computing capabilities to ensure high scalability and robustness towards robots' fault. The patrolling algorithm is designed in the framework of behavioral control and it is based on the concept of Action: an higher level of abstraction with respect to the behaviors. Each Action is obtained by combining more elementary behaviors in the Null-Space-Behavioral framework. A Finite-State-Automata is designed as supervisor in charge of selecting the appropriate action. The approach has been validated in simulation as well as experimentally with a patrol of 3 Pioneer robots available at the Distributed Intelligence Laboratory of the University of Tennessee.

Standard kinematics prioritized task based motion control solutions do not take into account the physical limitations in terms of maximum actuator speed of robots. In this paper, a prioritized task based kinematics control solution is presented that, under given conditions on the kind of concurrent tasks to be pursued, guarantees task error stability and convergence. Moreover the joint velocities are guaranteed to be bounded by a desired threshold. As for other a null-space projection techniques known in the literature, joint speed commands are computed in such a way that lower priority tasks do not interfere with higher priority ones in the assumption that joint speeds can be arbitrarily large: in addition, if joint speeds are to be bounded by a desired value, joint velocity commands are limited by dynamically chosen values depending on the task priority. As a result, joint velocities are always bounded such that, if necessary, higher priority tasks are executed first.

Stability analysis of priority-based kinematic control algorithms for redundant robotic systems is approached in this paper. Starting from the classical applications in position control of manipulators, the kinematic-based approaches have lately been applied to, e.g., visual servoing and quadruped or multirobot coordination control. A common approach consists in the definition of several tasks properly combined in priority. In this paper, by resorting to a Lyapunov-based stability discussion for several prioritized inverse kinematics algorithms, sufficient conditions for the control gains and the tasks' design are given for the regulation problem. Two case studies show the practical implementation of the results.

A wide number of robotic applications makes use of priority-based kinematic control algorithms for redundant systems. Starting from the classical applications in position control of manipulators, the kinematic-based approaches were lately applied to, e.g., visual servoing or multi-robot coordination control. The basic approach consists in the definition of several tasks properly combined in priority. A rigorous stability analysis that ensures the possibility to effectively achieve the defined tasks, however, is missing. In this paper, by resorting to a Lyapunov-based stability discussion for the prioritized inverse kinematics algorithms, an effective condition is given to verify that the tasks are properly matched; moreover, minimum bound for the control gains are determined.

In this paper the flocking problem for a multi-robot system, consisting in making the robots of a team grouping together, is addressed. The flocking is achieved resorting to the Null-Space-based Behavioral (NSB) control by defining very simple behaviors for each robot of the team and by properly arranging these behaviors in priority. The NSB algorithm, making the robots using only local information, successfully achieves the flocking with or without a rendez-vous point and in eventual presence of obstacles. Extensive simulations and experiments using differential-drive mobile robots prove the effectiveness of the proposed algorithm.

In this paper, two experimental case studies performed with a multi-robot system made of 6 Khepera II mobile robots are presented. The experiments, performed at the laboratory LAI (Laboratorio di Automazione Industriale) of the Universita degli Studi di Cassino, are aimed at testing the performances and the robustness of a behavior-based technique, namely the null-space-based behavioral control (NSB), while executing different kinds of missions. In particular, the NSB approach, based on an inverse kinematic technique inherited by industrial manipulator applications, has been developed to control a generic team of autonomous vehicles and it has been implemented on a centralized architecture to control, at a kinematic level, a platoon of autonomous mobile robots.

One important problem in autonomous robot navigation is the effective following of an unknown path traced in the environment in compliance with the kinematic limits of the vehicle, i.e., bounded linear and angular velocities and accelerations. In this case, the motion planning must be implemented in real-time and must be robust with respect to the geometric characteristics of the unknown path, namely curvature and sharpness. To achieve good tracking capability, this paper proposes a path following approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The input to the fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path. To validate the proposed algorithm two completely different experiments have been run: in the first experiment, the vehicle has to perform a lane-following task acquiring lane information in real-time using an onboard camera; in the second, the motion of the vehicle is obtained assigning in real-time a given time law. The obtained results show the effectiveness of the proposed method

In this paper the control of autonomous underwater vehicles in 6 degrees of freedom is analyzed in a comparison study among several controllers. At steady state the vehicle needs to compensate for two dynamic effects, the ocean current and the restoring forces; the appropriateness of the adaptive/integral action designed with respect to those persistent effects is discussed. Moreover, for each controller an adaptive/integral PD+gravity-compensation-like version is derived and eventually modified so as to achieve null steady state error under modeling uncertainty and presence of ocean current. Numerical simulations are presented to better illustrate the controllers' behavior

In this paper a calibration technique aimed at identifying the odometric parameters of differential-drive mobile robots is proposed. The proposed algorithm is based on two successive least-squares estimations based on the continuous-time kinematic equations of motion; the time-discretization error, thus, is avoided. The use of the least-squares technique is made possible by observing a linear mapping between the unknowns and the measurements and it is not the results of a linearization. Another advantage of the proposed technique is that no predefined path is required, i.e., the robot can move under a preferred navigation algorithm. The basic technique makes use of video-camera measurements and absolute position readings of the wheels' encoders, the use of different sensors and measurements of the wheels velocities is also discussed. Experimental results with the mobile robot Khepera II confirm the effectiveness of the proposed technique

In this paper, an approach to control the motion of a platoon of autonomous vehicles is presented. The proposed technique is based on the definition of suitable task functions that are handled in the framework of singularity-robust task-priority inverse kinematics. The algorithm is implemented by a two-stage control architecture such that intervehicle communication is not required. The effectiveness of the approach is investigated by means of numerical simulation case studies

This paper develops an algorithm for odometry calibration of differential-drive mobile robots. As a first step, the kinematic equations are written so as to underline linearity in a suitable set of unknown parameters; then, the least-squares method is applied to estimate them. The wide literature on the least-squares formulation can thus be exploited; suitability of the data can be numerically verified. The proposed technique has been implemented on the Khepera II mobile robot; the obtained results confirm the effectiveness of the proposed calibration method also in comparison to other existing approaches.

This paper investigates the implementation of a wireless mobile ad-hoc network to guarantee that an autonomously driven mobile vehicle remains connected to a limited-coverage base antenna during its motion. To the purpose, the use of a platoon of mobile robots is proposed to carry a number of repeater antennas; these must be suitably moved to dynamically ensure a multi-hop communication link to the vehicle that extends outside the area covered by the sole base antenna. Self configuration of the robots' platoon is then achieved by a singularity-robust task-priority inverse kinematics algorithm via the definition of suitable task functions. The obtained simulation results show the effectiveness of the proposed approach.

For a mobile robot, odometry calibration consists of the identification of a set of kinematic parameters that allow reconstructing the vehicle's absolute position and orientation starting from the wheels' encoder measurements. This paper develops a systematic method for odometry calibration of differential-drive mobile robots. As a first step, the kinematic equations are written so as to underline linearity in a suitable set of unknown parameters; thus, the least-squares method can be applied to estimate them. A major advantage of the adopted formulation is that it provides a quantitative measure of the optimality of a test motion; this can be exploited to drive guidelines on the choice of the test trajectories and to evaluate accuracy of a solution. The proposed technique has been experimentally validated on two different mobile robots and, in one case, compared with other existing approaches; the obtained results confirm the effectiveness of the proposed calibration method.

In this paper the fault tolerance capabilities of a kinematic control technique for platoons of autonomous vehicles are investigated. The proposed technique is based on the definition of suitable task functions that are handled in the framework of singularity-robust task-priority inverse kinematics algorithms. The control scheme is tested in simulation in a mission requiring 8 vehicles to entrap a moving target in presence of obstacles to be avoided; the mission is accomplished despite the failure of one vehicle that, once stopped, becomes an obstacle to be avoided itself.

In this paper an observer-based fault diagnosis (FD) approach for autonomous underwater vehicles (AUVs), subject to actuator faults (i.e., faults affecting the propulsion system and/or the control surfaces), is proposed. A diagnostic observer is developed based on the available dynamic model of the AUV. Compensation of unknown dynamics, uncertainties and disturbances is achieved through the adoption of a class of neural interpolators (support vector machines, SVMs) trained off line. On the other hand, interpolation of unknown actuator faults is performed by adopting a radial basis function (RBF) network, whose weights are adaptively tuned on line. The effectiveness of the approach is tested in a simulation case study developed for the NPS AUV II (PHOENIX) vehicle.

A novel adaptive control law for the end-effector tracking problem of underwater vehicle-manipulator systems (UVMSs) is presented in this paper. By exploiting the serial-chain kinematic structure of the UVMS, the overall control problem is decomposed in a set of elementary control problems, each of them formulated with respect to a single rigid body in the system. The proposed approach results in a modular control scheme which simplifies application to UVMSs with a large number of links, reduces the required computational burden, and allows efficient implementation on distributed computing architectures. Furthermore, the occurrence of kinematic and representation singularities is overcome, respectively, by expressing the control law in body-fixed coordinates and representing the attitude via the unit quaternion. To show the effectiveness of the proposed control strategy, a simulation case study is developed for a vehicle in spatial motion carrying a six-degree-of-freedom manipulator.

In this paper a strategy to control the motion of platoon of autonomous vehicles is presented. The proposed technique is based on the definition of suitable task functions that are handled in the framework of singularity-robust task-priority inverse kinematics. The algorithm is implemented by two-stage control architecture such that inter-vehicle communication is not required. The possible definition of several task functions of interest is discussed and the effectiveness of the approach is investigated by means of numerical simulation case studies.

The problem of redundancy resolution and motion coordination between the vehicle and the manipulator in underwater vehicle-manipulator systems (UVMSs) is addressed in this paper. UVMSs usually possess more degrees of freedom than those required to perform end-effector tasks; therefore, they are redundant systems and kinematic control techniques can be applied aimed at achieving additional control objectives besides tracking of the end-effector trajectory. In this paper, a task-priority inverse kinematics approach to redundancy resolution is merged with a fuzzy technique to manage the vehicle-arm coordination. The fuzzy technique is used both to distribute the motion between vehicle and manipulator and to handle multiple secondary tasks. Numerical case studies are developed to demonstrate effectiveness of the proposed technique.

The presence of joint velocity and acceleration limits must be taken into account by the inverse kinematics of robot manipulators, so as to avoid incorrect task execution when these are violated. To solve this problem, a novel algorithmic approach to kinematic control is presented in this paper, which guarantees that the joint variables do not overtake their limits. The proposed technique is based on a new second-order inverse kinematics algorithm, which enables the handling of velocity and acceleration constraints while tracking the desired end-effector path. The goal is achieved by suitably slowing down the task-space trajectory via a time warp when joints limits are encountered. The proposed method is designed for online applications, i.e., the desired trajectory is not known in advance, and requires a light computational burden. The application of the proposed approach is finally illustrated in experiments implemented on a six-degree-of-freedom industrial robot manipulator.

This paper presents an experimental study concerning the application of a real-time motion planning algorithm to a unicycle-like mobile robot. The desired trajectory to be followed by the mobile robot in presence of bounds on the linear/angular velocities and accelerations is available online. Moreover, with the respect to the kinematic constraints, the desired path has to be kept as long as possible. The implemented algorithm is based on a discrete-time kinematic control which implements a warping of the time law based on the definition of a virtual time. In addition, a fuzzy inference system handles the additional information given by the difference between the virtual and real time in order to exploit the knowledge in advance of the desired path. The experimental results confirm the effectiveness of the adopted algorithm.

A new approach to the fault-accommodating allocation of thruster forces of an autonomous underwater vehicle (AUV) is investigated in this paper. This paper presents a framework that exploits the excess number of thrusters to accommodate thruster faults during operation. First, a redundancy resolution scheme is presented that considers the presence of an excess number of thrusters along with any thruster faults and determines the reference thruster forces to produce the desired motion. This framework is then extended to incorporate a dynamic state feedback technique to generate reference thruster forces that are within the saturation limit of each thruster. Results from both computer simulations and experiments are provided to demonstrate the viability of the proposed scheme.

An adaptive control law for autonomous underwater vehicles (AUVs) is presented. Usually, the control laws are designed with respect to a mathematical model expressed either in the Earth-fixed frame or in the vehicle-fixed frame. These two approaches, however, do not take into account the different origin of the effects that can affect the steady state errors: namely, the restoring generalized forces and the ocean current. With the use of a suitable adaptive action those effects can be properly taken into account as is shown in the paper.

The interaction of underwater vehicle-manipulator systems (UVMSs) with the environment is affected by several design constraints such as uncertainty in the model knowledge, presence of hydrodynamic effects, kinematic redundancy of the system, and poor performance of vehicle's actuating system. The paper presents an external force control scheme for UVMSs that does not require dynamic compensation; however, it can benefit from the knowledge of part of the dynamic model. The possible occurrence of loss of contact due to vehicle movement during the task, is also taken into account. A numerical case study shows the application of the proposed technique in a given task.

An approach to the allocation of thruster forces of an autonomous underwater vehicle (AUV) is investigated. Generally, the number of thrusters in an AUV is more than what is minimally required to produce the desired motion. We investigate how to exploit the excess number of thrusters to accommodate thruster faults during operation. First, a redundancy resolution scheme is presented that takes into account the presence of excess number of thrusters along with any thruster faults and determines the reference thruster forces to produce the desired motion. These reference thruster forces are utilized in the thruster controller to generate the required motion. This approach resolves the thruster redundancy in the Cartesian space and allows the AUV to track the task-space trajectories with asymptotic reduction of the task-space errors. Results from both computer simulations and experiments are provided to demonstrate the viability of the proposed scheme. The paper is built upon the preliminary concept proposed earlier by Podder and Sarkar (1999).

A navigation system for real-time path planning and obstacle avoidance of an autonomous underwater vehicle is presented. The vehicle is designed to accomplishing two missions: pre-deployment survey of sea bottom, and visual inspection of pipelines. In the first mission the navigation system must be able to track a predefined path while avoiding the unplanned occurrence of obstacles. In the second mission the navigation system must track a pipeline by locally reconstructing its location from visual information; also in this case, the unplanned occurrence of obstacles must be handled. Furthermore, the navigation system must properly take into account the presence of ocean current and some drastic constraints due to sensor and actuator characteristics.

An explicit force control scheme for underwater vehicle-manipulator systems is presented. Several major problems of underwater robotics are taken into account; namely, uncertainty in the model knowledge, presence of hydrodynamic effects, kinematic redundancy of the system, and poor performance of the vehicle's actuating system. The possible occurrence of loss of contact due to vehicle movement during the task is also discussed. Extensive dynamic simulations prove the effectiveness of the proposed control algorithm.

An underwater vehicle-manipulator system (UVMS) usually has more degrees of freedom than those required to attain given end-effector postures. Therefore, the UVMS is a redundant system and kinematic control algorithms can be applied aimed at achieving additional control objectives such as energy savings or increase of system manipulability. This paper presents a task-priority inverse kinematics approach to redundancy resolution for a UVMS. Three case studies are developed to demonstrate the effectiveness of the technique in different applications.