Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key challenges in policy learning involves handling complex, long-horizon tasks that require managing extensive sequences of actions and observations with multiple modes. Wavelet analysis offers significant advantages in signal processing, notably in decomposing signals at multiple scales to capture both global trends and fine-grained details. In this work, we introduce a novel wavelet policy learning framework that utilizes wavelet transformations to enhance policy learning. Our approach leverages learnable multi-scale wavelet decomposition to facilitate detailed observation analysis and robust action planning over extended sequences. We detail the design and implementation of our wavelet policy, which incorporates lifting schemes for effective multi-resolution analysis and action generation. This framework is evaluated across multiple complex scenarios, including robotic manipulation, self-driving, and multi-robot collaboration, demonstrating the effectiveness of our method in improving the precision and reliability of the learned policy.

Despite the success of current multimodal learning at scale, its susceptibility to data poisoning attacks poses security concerns in critical applications. Attacker can manipulate model behavior by injecting maliciously crafted yet minute instances into the training set, stealthily mismatching distinct concepts. Recent studies have manifested the vulnerability by poisoning multimodal tasks such as Text-Image Retrieval (TIR) and Visual Question Answering (VQA). However, the current attacking method only rely on random choice of concepts for misassociation and random instance selections for injecting the poisoning noise, which often achieves the suboptimal effect and even risks failure due to the dilution of poisons by the large number of benign instances. This study introduces MP-Nav (Multimodal Poison Navigator), a plug-and-play module designed to evaluate and even enhance data poisoning attacks against multimodal models. MP-Nav operates at both the concept and instance levels, identifying semantically similar concept pairs and selecting robust instances to maximize the attack efficacy. The experiments corroborate MP-Nav can significantly improve the efficacy of state-of-the-art data poisoning attacks such as AtoB and ShadowCast in multimodal tasks, and maintain model utility across diverse datasets. Notably, this study underscores the vulnerabilities of multimodal models and calls for the counterpart defenses.

This paper presents the first worldwide functional prototype omnidirectional multi-rotor aerial vehicle with fixed uni-directional thrusters, with an on-board power source. An optimization algorithm computes the positions and orientations of the propellers in the body frame of the prototype to achieve the omnidirectional capability, while minimizing the platform's weight and the required thrust to hover at any orientation, in addition to other construction requirements. The effect of the aerodynamic interaction between the different propellers is identified experimentally, and the ensuing results are included in the optimization algorithm to avoid such interactions during flight. The prototype's performance is assessed in real experiments demonstrating the decoupling between the forces and moments of the drone, its ability to track concurrently independent positions and orientations, and its ability to hover at a fixed position while rotating.

Autonomous aerial vehicles play a critical role in search and rescue operations, where navigation through cluttered and confined environments is essential. To this end, this paper presents a novel trajectory planning framework for omnidirectional drones that dynamically adjusts tracking velocity based on the platform’s proximity to obstacles, ensuring a balance between safety and efficiency in cluttered and challenging environments. The proposed approach generates a geometric path to the target location. At each waypoint, the minimum distance between the drone’s convex hull and surrounding obstacles is determined, allowing the computation of the velocity constraints. By slowing down near obstacles and accelerating in open spaces, the method enhances both safety and maneuverability. The framework is validated through real-world experiments using the OmniOcta UAV, demonstrating its ability to navigate through constrained spaces. Furthermore, we present an experimental study to investigate key sources of tracking deviations, including propeller dynamics and aerodynamic interactions near obstacles.

Robotic navigation plays a pivotal role in a wide range of real-world applications. While traditional navigation systems focus on efficiency and obstacle avoidance, their inability to model complex human behaviors in shared spaces has underscored the growing need for socially aware navigation. In this work, we explore a novel paradigm of socially aware robot navigation empowered by large language models (LLMs), and propose HSAC-LLM, a hybrid framework that seamlessly integrates deep reinforcement learning with the reasoning and communication capabilities of LLMs. Unlike prior approaches that passively predict pedestrian trajectories or issue pre-scripted alerts, HSAC-LLM enables bidirectional natural language interaction, allowing robots to proactively engage in dialogue with pedestrians to resolve potential conflicts and negotiate path decisions. Extensive evaluations across 2D simulations, Gazebo environments, and real-world deployments demonstrate that HSAC-LLM consistently outperforms state-of-the-art DRL baselines under our proposed socially aware navigation metric, which covers safety, efficiency, and human comfort. By bridging linguistic reasoning and interactive motion planning, our results highlight the potential of LLM-augmented agents for robust, adaptive, and human-aligned navigation in real-world settings. Project page: https://hsacllm.github.io/.

A Control Barrier Function (CBF)-based motion planning algorithm is proposed. The algorithm explores an unknown environment to reach a target point, providing velocity commands to the robot controller module. CBFs, along with a circulation inequality are used to generate safe paths toward the goal while preventing collisions with obstacles. The proposed global navigation scheme is experimentally verified on a quadruped platform to demonstrate safe, collision-free exploration over long distances.

Zero-Shot Object Goal Navigation (ZS-OGN) enables robots to navigate toward objects of unseen categories without prior training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only partial objects are observed or detailed and functional representations of the environment are lacking. To resolve the above two issues, we propose \textit{Geometric-part and Affordance Maps} (GAMap), a novel method that integrates object parts and affordance attributes for navigation guidance. Our method includes a multi-scale scoring approach to capture geometric-part and affordance attributes of objects at different scales. Comprehensive experiments conducted on the HM3D and Gibson benchmark datasets demonstrate improvements in Success Rates and Success weighted by Path Length, underscoring the efficacy of our geometric-part and affordance-guided navigation approach in enhancing robot autonomy and versatility, without any additional task-specific training or fine-tuning with the semantics of unseen objects and/or the locomotions of the robot.

In this paper, we propose an algorithm for computing a smoothed version of the distance between two objects. As opposed to the traditional Euclidean distance between two objects, which may not be differentiable, this smoothed distance is guaranteed to be differentiable. Differentiability is an important property in many applications, in particular in robotics, in which obstacle-avoidance schemes often rely on the derivative/Jacobian of the distance between two objects. We prove mathematical properties of this smoothed distance and of the algorithm for computing it, and show its applicability in robotics by applying it to a second order kinematic control framework, also proposed in this paper. The control framework using smooth distances was successfully implemented on a 7 DOF manipulator.

The exploration of mutual-benefit cross-domains has shown great potential toward accurate self-supervised depth estimation. In this work, we revisit feature fusion between depth and semantic information and propose an efficient local adaptive attention method for geometric aware representation enhancement. Instead of building global connections or deforming attention across the feature space without restraint, we bound the spatial interaction within a learnable region of interest. In particular, we leverage geometric cues from semantic information to learn local adaptive bounding boxes to guide unsupervised feature aggregation. The local areas preclude most irrelevant reference points from attention space, yielding more selective feature learning and faster convergence. We naturally extend the paradigm into a multi-head and hierarchic way to enable the information distillation in different semantic levels and improve the feature discriminative ability for fine-grained depth estimation. Extensive experiments on the KITTI dataset show that our proposed method establishes a new state-of-the-art in self-supervised monocular depth estimation task, demonstrating the effectiveness of our approach over former Transformer variants.

Virtual reality simulation of robotic-assisted min-imal invasive procedures reveals interesting issues related to the perception, control and manipulation of laparoscopic tools with emphasis given to the pivot trajectories and the Remote-Center-of-Motion (RCM) constrained motion planning. In this paper, the Gazebo simulator under Robot Operating System (ROS) allows the inclusion of hardware-in-the-loop for Minimally Invasive Surgery (MIS) procedures. The RCM constraint is addressed through the transformation of the surgical task space into the robot's taskspace, while addressing the robot's manipulability. Emphasis is given in calculating various geometric paths to be followed by the robot during surgery. Simulations were conducted using the ROS framework and the MoveIt kinematic planner using the RRTConnect path planning algorithm to evaluate the efficacy proposed scheme.

Recent object tracking methods depend upon deep networks or convoluted architectures. Most of those trackers can hardly meet real-time processing requirements on mobile platforms with limited computing resources. In this work, we introduce the Siamese Transformer Pyramid Network (SiamTPN), which inherits the advantages from both CNN and Transformer architectures. Specifically, we exploit the inherent feature pyramid of a lightweight network (ShuffleNetV2) and reinforce it with a Transformer to construct a robust target-specific appearance model. A centralized architecture with lateral cross attention is developed for building augmented high-level feature maps. To avoid the computation and memory intensity while fusing pyramid representations with the Transformer, we further introduce the pooling attention module, which significantly reduces memory and time complexity while improving the robustness. Comprehensive experiments on both aerial and prevalent tracking benchmarks achieve competitive results while operating at high speed, demonstrating the effectiveness of SiamTPN. Moreover, our fastest variant tracker operates over 30 Hz on a single CPU-core and obtaining an AUC score of 58.1% on the LaSOT dataset. Source codes are available at https://github.com/RISC-NYUAD/SiamTPNTracker

This paper deals with predicting future 3D motions of 3D object scans from the previous two consecutive frames. Previous methods mostly focus on sparse motion prediction in the form of skeletons. While in this paper, we focus on predicting dense 3D motions in the form of 3D point clouds. To approach this problem, we propose a self-supervised approach that leverages the power of the deep neural network to learn a continuous flow function of 3D point clouds that can predict temporally consistent future motions and naturally bring out the correspondences among consecutive point clouds at the same time. More specifically, in our approach, to eliminate the unsolved and challenging process of defining a discrete point convolution on 3D point cloud sequences to encode spatial and temporal information, we introduce a learnable latent code to represent the temporal-aware shape descriptor, which is optimized during the model training. Moreover, a temporally consistent motion Morpher is proposed to learn a continuous flow field which deforms a 3D scan from the current frame to the next frame. We perform extensive experiments on D-FAUST, SCAPE, and TOSCA benchmark data sets. The results demonstrate that our approach is capable of handling temporally inconsistent input and produces consistent future 3D motion while requiring no ground truth supervision.

The visual tracking problem of Unmanned Aerial Vehicles (UAVs) with a Pan-Tilt-Zoom (PTZ) camera system is the subject of this article. Given the background of an image acquired by a PTZ-camera system, a border encompassing a moving object is computed relying on optical flow and the histogram of oriented gradients. Deep Learning (DL) algorithms are trained off-line to decide on the existence of a UAV within this border. Particularly, the ResNet-50 model was trained using a collected data set with more than 50,000 registered positive images. Having identified a UAV, a visual servoing scheme is employed to adjust the PTZ-parameters in order for the border of a detected UAV to span as large as possible the cameras Field of View. The advocated servoing scheme is robust enough against the UAVs rapid maneuvers. Experimental studies are offered to highlight the efficiency of the suggested scheme.

Spatial cognition, as gained through the sense of vision, is one of the most important capabilities of human beings. However, for the visually impaired (VI), lack of this perceptual capability poses great challenges in their life. Therefore, we have designed Point-to-Tell-and-Touch, a wearable system with an ergonomic human-machine interface, for assisting the VI with active environmental exploration, with a particular focus on spatial intelligence and navigation to objects of interest in an alien environment. Our key idea is to link visual signals, as decoded synthetically, to the VI's proprioception for more intelligible guidance, in addition to vision-to-audio assistance, i.e., finger pose, as indicated by pointing, is used as “proprioceptive laser pointer” to target an object in that line of sight. The whole system consists of two features, Point-to-Tell and Point-to-Touch, both of which can work independently or cooperatively. The Point-to-Tell feature contains a camera with a novel one-stage neural network tailored for blind-centered object detection and recognition, and a headphone telling the VI the semantic label and distance from the pointed object. the Point-to-Touch, the second feature, leverages a vibrating wrist band to create a haptic feedback tool that supplements the initial vectorial guidance provided by the first stage (hand pose being direction and the distance being the extent, offered through audio cues). Both platform features utilize proprioception or joint position sense. Through hand pose, the VI end user knows where he or she is pointing relative to their egocentric coordinate system and we are able to use this foundation to build spatial intelligence. Our successful indoor experiments demonstrate the proposed system to be effective and reliable in helping the VI gain spatial cognition and explore the world in a more intuitive way.

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.

This article focuses on the utilization of a mobile robot as data mule for collecting and transferring data from a wireless sensor system (WSN). Each static node within the WSN has its data generation rate resulting in an imposed inter-visit duration due to its hardware limitations. The mobile element/robot approaches the nodes, collects their stored data, and transfers these to a depot station. In the adopted scenario, the mobile robot assumes prior knowledge of the nodes' locations and the corresponding trajectories are extracted by solving a combinatorial optimization problem that resembles that of Travelling Salesman Subset-tour Problem (TSSP). The resulting Mobile Element Scheduling (MES) scheme accounts for: the traveling distances between the static nodes, the maximum inter-visit duration for each node to avoid buffer overflow, the visiting/service time at each node and the energy consumption of the mobile robot. The presented simulation studies indicate the effectiveness of the overall optimization concept.

This article presents the design and experimental evaluation of a prototype robotic platform for minimally invasive surgical procedures. The platform utilizes a hybrid actuation scheme, consisting of a 5 Degree-of-Freedom (DoF) servo-actuated manipulator for extra-operative and pivoting motion and a 4 DoF shape memory alloy actuated probe at the distal end, for intra-operative dexterity. The architecture targets thoracic and abdominal operations, with low interaction forces at the probe's end-effector. The system, runs under the Robot Operating System framework for easier deployment and development. Additional accompanying software is developed to aid the surgeon during deployment. Specifically, a Graphical User Interface employing modules controls for online parameter reconfiguration, operation mode switching while custom viewports for stereo imaging are implemented. Teleoperation is feasible with the integration of a haptic device. In-vitro evaluation of the robot is presented, to assess the maneuvering efficiency and further potential exploitation of the design.

This paper proposes a methodology for visual tracking of a dynamic generalized subject within an unknown map, by relying on its perception as a separate entity which can be distinguished spatially and visually from its environment. To this purpose, a 3D-representation of the visible scenery is examined, and the subject is spatially identified by its externally viewed hull via a mesh-connection algorithm aided by visual cues, and visually identified by distinct feature tracking based on an incrementally built list of key-aspects. These two processes operate in closed-loop, and employing a set of assumptions regarding the subject's structural/temporal invariance the tracking health state can be determined. This work additionally presents the framework for the deployment of this scheme for autonomous aerial robotic subject tracking, employing the dynamic subject/environment distinction to obtain knowledge of the environment structure, and collision-free trajectory generation algorithms to achieve mobile tracking.

In this paper, the area coverage problem of non-convex environments by a group of mobile robots is addressed. Each robot is equipped with a sensing device modeled through a range-limited visibility field. The network is assumed to be homogeneous in terms of nodes' sensing capabilities and general characteristics. A gradient-ascent control law is proposed, based on visibility-based Voronoi diagrams, leading the network to the optimal final state in terms of total area coverage. The provided simulation studies illustrate the results derived by the application of the proposed control scheme and validate its effectiveness.

The issue of efficient large force and moment exertion with Unmanned Aerial Vehicles (UAVs) is the subject of this paper. Inspiration is drawn from the vision of UAVs that are capable of autonomously executing industrial activities, or effectively reconfiguring their environment via forceful interaction. Therein, the technical shortcomings of the potential utilization of conventional underactuated UAV platform designs are examined, in terms of operational effectiveness-versus-safety. The innovative implementation of the direct thrust-vectoring authority of tiltrotor UAV types for forceful interaction is proposed, and its associated technical contributions are analyzed. A methodology is developed for controlled forward thrust force and rotating moment exertion, while ensuring safe operation near the hovering attitude pose. A large force-requiring scenario is assembled, consisting of a realistically-sized object laid on solid ground, regarded as a path-hindering obstacle to be forcefully removed by the UAV via pushing manipulation. To this purpose, a high-end autonomous tiltrotor UAV is employed in order to achieve this environment modification task, relying on a properly synthesized control structure.

This article examines the coordination problem of the nodes' motion in a heterogeneous anisotropic mobile sensor network for area coverage purposes. The mobile agents are assumed to have non-uniform with varying scaling sensing ability around themselves. The nodes' sensor footprint is allowed to be any arbitrary compact planar set, while the coordination scheme accounts for rotation of the latter. The domain sensed by the swarm is partitioned via the proposed distributed scheme that differentiates for standard Voronoi-alike distance-based metrics. The distributed cooperative scheme developed manages to lead the group towards an area-optimal configuration via proper control of the movement and rotation of each sensing node. Numerical results are provided in order to indicate the efficiency of the proposed technique.

The experimental translational hovering control of a Tri-TiltRotor Unmanned Aerial Vehicle is the subject of this paper. This novel UAV is developed to possess the capability to perform autonomous conversion between the Vertical Take-Off and Landing, and the Fixed-Wing flight modes. Via this design's implemented features however, the capability for additional control authority on the UAV's longitudinal translational motion arises: The rotor-tilting servos are utilized in performing thrust vectoring of the main rotors, thus exploiting their fast response characteristics in directly providing translation-controlling forces. The system's hovering translation is handled by a Model Predictive Control scheme, following the aforementioned actuation principles. While performing experimental studies of the overall controlled system's efficiency, the advantageous effects of this novel control authority are clearly noted. Additionally, in this article the considerations and requirements for operational autonomy-related on-board-only state estimation are addressed.

The area coverage problem of non-convex environments by a group of mobile agents is examined is this article. The network is consisted of mobile sensing nodes whose sensing pattern is uniform based on the geodesic metric. The space under consideration is partitioned based on the geodesic distance among the nodes (geodesic Voronoi diagram), resulting in compact Voronoi sets. A distributed coordination scheme is proposed, that leads the network towards its optimal state (in area-wise terms) in a monotonic manner. Unlike previous results that rely on the Euclidean distance and/or the Voronoi partitioning of the convex hull of the environment, the control law results in motions of the mobile nodes in the interior of the environment. Simulation results further indicate the efficiency of the proposed approach.

In this article, a robot motion planning scheme for 3D-terrains is developed. Given the terrain profile and various obstacles on it, a navigation function is created. A geodesic based shortest path algorithm is developed to find the optimal lengthwise path towards the goal position. The path is then converted into a continuous smooth trajectory via an optimization scheme relying on a Bézier curve parametrization that satisfies the robot's kinodynamic constraints. The efficacy of the proposed method is tested in various simulation studies.

The optimal trajectory-tracking control of a Tri-TiltRotor Unmanned Aerial Vehicle is the subject of this paper. This specific UAV design possesses the capability to control the orientation of its main rotors, thus enabling operation in both the Vertical Take-Off and Landing as well as the Fixed-Wing flight mode configuration. The translational controller developed is based on a Linear-Quadratic tracking scheme. Additionally to the proposed controller, the newly introduced capability for rotor-tilting, and thus thrust vectoring, as provided by this design is proposed for its utilization in the control of the longitudinal degree-of-freedom of the UAV. Simulation and experimental results are presented, demonstrating both the overall proposed controller's efficiency, as well as the clear advantage gained by the aforementioned proposed strategy, with regard to the controlled system's longitudinal control performance.

In this article, the design, system modeling and control of a new hybrid type of Unmanned Aerial Vehicle (UAV) is presented. Based on the flight principles of the Dos Samara UAV, a new vehicle that combines the capability of hovering, like a helicopter, and high speed-increased endurance flying, like a fixed-wing aircraft, is designed. The nonlinear dynamics model of the aircraft operating in helicopter mode is derived and linearized around hovering operation. Based on this model an LQ-controller is designed. The performance of the overall system is examined in simulation studies.

In this article a novel performance improvement scheme is being presented for the problem of designing a trajectory tracking controller for non-holonomic mobile robots with differential drive. Based on the robot kinematic equations, an error dynamics controller is being utilized for allowing the robot to follow an a priori defined reference path, with a desired velocity profile. The main novelty of this article stems from the utilization of a gradient based adaptive scheme that is able to adapt the controller's gain ruling the rising and settling time of the robot and up to now has been ad-hoc selected. The proposed adaptation scheme is based on the robot's path tracking errors and is able to provide an on-line adjustment for the performance improvement, independently of the selected path type. Multiple experimental test cases, including the movement of the robot on various path profiles, prove the efficacy of the proposed scheme.

In this paper the design and the experimental verification of a Constrained Finite Time Optimal (CFTO) control scheme for the attitude control of an Unmanned Quadrotor Helicopter (UqH) subject to wind gusts is being presented. In the proposed design the UqH has been modeled by a set of Piecewise Affine (PWA) linear equations while the wind gusts effects are embedded in the system model description as the affine terms. In this approach the switching among the PWA model descriptions are ruled by the rate of the rotation angles. In the design of the stabilizing CFTO-controller both the magnitude of external disturbances (worst case applied wind gust), and the mechanical constraints of the UqH such as maximum thrust in the rotors and UqH's angles rate are taken under consideration in order to design an off-line controller that could rapidly be applied to a UqH in a form of a look-up table. The proposed control scheme is applied in experimental studies and multiple test-cases are presented that prove the efficiency of the proposed scheme.

The control of robotic systems actuated by Shape Memory Alloy (SMA) wires is still an open issue. The goal of this work focuses on the development of a competent control system for a humanlike, 2-DOF, SMA actuated finger. We pursue this objective through a visual-servoing scheme and the implementation of classical decoupled PID controllers for the joint angles. Experimental results prove the efficacy and usability of the suggested scheme.

In this paper, an adaptive input shaping technique is proposed and implemented on a three-dimensional overhead crane with hoisting mechanism. The main goal is the maximum possible suppression of the oscillations of the payload induced during both the trolley motion and the hoisting of the load. Standard input shaping techniques cannot take into account the hoisting contrary to the adaptive version, where the shaper's parameters are reconfigured in each loop according to the updated linearized model, dependent on the current rope length. Simulation results show the efficacy of the proposed controller compared to standard shapers when applied to this kind of time-varying systems.

No abstract available.

An integrated system for a power-aware robotic-centric exploratory lunar mission is the subject of this article. The robots communicate with a base station with a flexible protocol that can automatically change its attributes from multi to single hopping strategies according to the QoS of the entire network. The base station is responsible for the tracking of the robots and their re-charging. The robots' recharging is achieved via an optical-to-electrical energy procedure where a laser beam charges the photovoltaic cells attached at each robot. The experimental studies on a swarm of robots reveal the intricacies involved in a typical robotic-centric lunar exploration mission.

In this article, a set membership (SM) identification technique is tailored to detect faults in microelectromechanical systems. The SM-identifier estimates an orthotope which contains the system's parameter vector. Based on this orthotope, the system's output interval is predicted. If the actual output is outside of this interval, then a fault is detected. Utilization of this scheme can discriminate mechanical-component faults from electronic component variations frequently encountered in MEMS. For testing the suggested algorithm's performance in simulation studies, an interface between classical control-software (MATLAB) and circuit emulation (HSPICE) is developed

The nonlinear dynamic Takagi-Sugeno fuzzy model with offset terms is analyzed as a perturbed linear system. A sufficient criterion for the robust stability of this nominal system against nonlinear perturbations guarantees quadratic stability of the fuzzy model. The criterion accepts a convex programming formulation of reduced computational cost compared to the common Lyapunov matrix approach. Parametric robust control techniques suggest synthesis tools for stabilization of the fuzzy system. Application examples on fuzzy models of nonlinear plants advocate the efficiency of the method. The examples demonstrate reduced conservatism compared to norm-based criteria.

A frequency-shaped implicit force control scheme for flexible link manipulators is considered in this article. The frequency shaping dependence is included to eliminate deleterious effects associated with control and observation spillover. The control effort is comprised of both a feedforward and feedback term. The feedback component regulates the joint coordinate error perturbations through the minimization of a linear quadratic, frequency-shaped cost functional. The feedforward component provides the torque required to compensate the underlying rigid arm dynamics along a prespecified reference trajectory. Numerical simulations are performed on a two link rigid-flexible manipulator to demonstrate the effectiveness of the proposed method.

Dynamics of multi-link flexible manipulators are highly nonlinear. Furthermore, the vibrational frequencies of these manipulators are configuration-dependent. Therefore, any feedforward or feedback algorithm has to deal with these frequency variations. In this paper, an inner-loop nonlinear controller based on feedback linearization of O(1) dynamics derived from an asymptotic expansion is utilized. It is shown that this control scheme significantly reduces the frequency variations due to the geometric configuration of the arm and cancels some of the nonlinearities due to Coriolis and centripetal effects. The advocated control law is compared and contrasted to an independent joint-based PD controller. However, since the aforementioned controllers are joint-based control schemes, significant vibrations are still induced at the end-effector. To this end, these control schemes are augmented with an input preshaper for vibration suppression. The objective is to preshape the reference input signals so that a vibration free output is achieved. The input preshaping scheme is shown to be effective when the plant dynamics are linear and time-invariant. These assumptions do not hold for the multi-link flexible manipulators as alluded to above. Application of an inner-loop nonlinear control to cancel some of the nonlinearities and to reduce configuration dependence of structural frequencies enhances the performance of the advocated input preshaping scheme or any other outer-loop linear control design. Experimental and simulation results for a two-link flexible manipulator are provided to validate the effectiveness of the advocated controllers.>

The problem of endpoint position control for a planar manipulator which has two very flexible links is considered. Discussions on system identification techniques are presented relative to the laboratory apparatus under consideration. The resulting models are used in static and dynamic fixed-controller designs, as well as in a self-tuning controller design for the case in which the manipulator carries an unknown payload at the endpoint of the second link. Experimental results are presented to illustrate the effectiveness of the control and system identification.>

The authors present experimental results on endpoint position control of a single-link, very flexible robot arm carrying an unknown, varying payload. The control objective is to maintain endpoint position accuracy in the presence of flexure effects after rapid movement to a rigid-body slew-angle commanded position. Fast, simple, and efficient frequency-domain schemes are used for online controller gain adjustment within an effective scheduling framework. Only endpoint acceleration measurements and motor shaft angle measurements are utilized in relatively simple control laws, where the appropriate gains have been scheduled in accordance with modal frequency information corresponding to a varying, unknown payload.>

A systematic algorithm is presented for the generation of the kinematic and dynamic equations of multilink rigid and/or flexible manipulators. The MACSYMA symbolic algebraic manipulation language is utilized to implement this algorithm, and an optimum code in terms of memory space is generated. Kinematic equations are derived using homogeneous transformation matrices, and the dynamic equations are obtained subsequently using the Euler-Lagrange formulation. The advantages of this algorithm and simulation results for control implementation are presented.>

This paper presents a straightforward control strategy applied to an N-link manipulator holding an unknown load and driving its end effector along a prespecified trajectory. The control is constituted into two primary components. The non-adaptive component is derived from the inverse problem technique while the adaptive component is computed via the application of sensitivity analysis applied to the completes centralized dynamic model of the manipulator. The result is a robust adaptive controller which tunes its parameters at specified time instants and can withstand all expected variations of the payload. The control synthesis is illustrated by simulations in a 2-link planar manipulator holding an unknown load.