Exploration in sparse reward environments remains a significant challenge in reinforcement learning, particularly in Contextual Markov Decision Processes (CMDPs), where environments differ across episodes. Existing episodic intrinsic motivation methods for CMDPs primarily rely on count-based approaches, which are ineffective in large state spaces, or on similarity-based methods that lack appropriate metrics for state comparison. To address these shortcomings, we propose Episodic Novelty Through Temporal Distance (ETD), a novel approach that introduces temporal distance as a robust metric for state similarity and intrinsic reward computation. By employing contrastive learning, ETD accurately estimates temporal distances and derives intrinsic rewards based on the novelty of states within the current episode. Extensive experiments on various benchmark tasks demonstrate that ETD significantly outperforms state-of-the-art methods, highlighting its effectiveness in enhancing exploration in sparse reward CMDPs.

Fast and efficient collision detection is essential for motion generation in robotics. In this paper, we propose an efficient collision detection framework based on the Signed Distance Field (SDF) of robots, seamlessly integrated with a self-collision detection module. Firstly, we decompose the robot's SDF using forward kinematics and leverage multiple extremely lightweight networks in parallel to efficiently approximate the SDF. Moreover, we introduce support vector machines to integrate the self-collision detection module into the framework, which we refer to as the SDF-SC framework. Using statistical features, our approach unifies the representation of collision distance for both SDF and self-collision detection. During this process, we maintain and utilize the differentiable properties of the framework to optimize collision-free robot trajectories. Finally, we develop a reactive motion controller based on our framework, enabling real-time avoidance of multiple dynamic obstacles. While maintaining high accuracy, our framework achieves inference speeds up to five times faster than previous methods. Experimental results on the Franka robotic arm demonstrate the effectiveness of our approach. Project page: https://sites.google.com/view/icra2025-sdfsc.

In the realm of motorcycle extreme sports, wheel-ground friction significantly influences vehicle safety. This study presents a robust and precise method for identifying motorcycle tire friction by leveraging an advanced observation scheme. The proposed observer combines cascaded flatness-based observer and extended Kalman filter, employs a robust fixed-time exact differentiator to estimate the first- and second-order derivatives of the signal. This approach ensures adaptability to environmental parameter variations while effectively attenuating measurement noise and external disturbances. The robustness and accuracy of the proposed method are validated through simulations on the BikeSim platform, incorporating external shock disturbances and varying road conditions.

To travel in the uneven and multi-obstacle terrain autonomously, an unmanned reverse tricycle vehicle, which mainly includes the steering, active tilting and driving actuators, is designed. A kinematic modeling framework incorporates terrain-induced camber, pitch, and tilting angles to characterize the vehicle’s dynamic behavior. The analytical investigation focuses on caster angle optimization and demonstrates how a multi-link parallelogram chassis architecture achieves kinematic decoupling between steering and tilting functions. To validate the potential of travelling in uneven terrain, the strait forward traveling across the road bank with near ’0’ inclination, active tilting assistant turning on the level surface, and the challenging counter-gradient slope turning experiments are carried. Experimental validation encompasses three critical scenarios: straight line forward traversal across road bank with near-zero lateral inclination, active tilt-assisted turning on planar surfaces, and counter-gradient slope turning. These trials substantiate the vehicle’s capability to maintain stability while executing complex maneuvers across uneven terrain.

Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a high-level capture planner for mobile manipulators (MM) that calculates the optimal capture point and joint configuration. Next, the pre-catching (PRC) planner ensures the robot reaches the target joint configuration as quickly as possible. To learn compliant catching strategies, we propose a network that leverages the strengths of LSTM for capturing temporal dependencies and positional encoding for spatial context (P-LSTM). This network is designed to effectively learn compliant strategies from human demonstrations. Following this, the post-catching (POC) planner tracks the compliant sequence output by the P-LSTM while avoiding potential collisions due to structural differences between humans and robots. We validate the CCMM framework through both simulated and real-world ball-catching scenarios, achieving a success rate of 98.70% in simulation, 92.59% in real-world tests, and a 28.7% reduction in impact torques. The open source code will be released for the reference of the community1.

Drifting is an advanced driving technique where the wheeled robot’s tire-ground interaction breaks the common non-holonomic pure rolling constraint. This allows high-maneuverability tasks like quick cornering, and steady-state drifting control enhances motion stability under lateral slip conditions. While drifting has been successfully achieved in four-wheeled robot systems, its application to single-track two-wheeled (STTW) robots, such as unmanned motorcycles or bicycles, has not been thoroughly studied. To bridge this gap, this paper extends the drifting equilibrium theory to STTW robots and reveals the mechanism behind the steady-state drifting maneuver. Notably, the counter-steering drifting technique used by skilled motorcyclists is explained through this theory. In addition, an analytical algorithm based on intrinsic geometry and kinematics relationships is proposed, reducing the computation time by four orders of magnitude while maintaining less than 6% error compared to numerical methods. Based on equilibrium analysis, a model predictive controller (MPC) is designed to achieve steady-state drifting and equilibrium points transition, with its effectiveness and robustness validated through simulations.

Imitation learning by watching humans offers a promising path to learning general-purpose robot skills with intuitive task specifications. While prior approaches for video-conditioned imitation learning directly extract skill embeddings from unstructured human videos and follow demonstrations step-by-step, such paradigm usually falls short in generalizing to unseen long-horizon tasks with a single human prompt video due to the significant embodiment and environment gap. To this end, our key insight is to infer local intentions from videos in order to retrieve robot skill memories from prior experience, and conversely select the feasible video clip to follow based on robot observations. Motivated by this, we introduce AdaMimic, a hierarchical imitation learning method that learns the bidirectional mapping of cross-domain sensorimotor skills and derives skill-based policy conditioned on adaptable latent plans. To enable generalization to unseen tasks given cross-domain human videos, AdaMimic leverages task-agnostic play data for interaction-aware skill embedding extraction and video-robot trajectory pairs for semantic and temporal human-to-robot skill alignment. In addition, our method exploits a skill adapter for robot-to-human alignment to adaptively align the robot with the skill intentions. We systematically evaluate AdaMimic on both simulated and real-world kitchen domains, demonstrating AdaMimic’s superiority over prior imitation learning methods in generalizing to novel long-horizon tasks with a single human prompt video.

This paper studies the problem of multi-agent trajectory prediction in crowded unknown environments. A novel energy function optimization-based framework is proposed to generate prediction trajectories. Firstly, a new energy function is designed for easier optimization. Secondly, an online optimization pipeline for calculating parameters and agents’ velocities is developed. In this pipeline, we first design an efficient group division method based on Frechet distance to classify agents online. Then the strategy on decoupling the optimization of velocities and critical parameters in the energy function is developed, where the slap swarm algorithm and gradient descent algorithms are integrated to solve the optimization problems more efficiently. Thirdly, we propose a similarity-based resample evaluation algorithm to predict agents’ optimal goals, defined as the target-moving headings of agents, which effectively extracts hidden information in observed states and avoids learning agents’ destinations via the training dataset in advance. Experiments and comparison studies verify the advantages of the proposed method in terms of prediction accuracy and speed. Note to Practitioners—Autonomous robots and vehicles are rapidly integrated into social life and industry, and the scenarios that robots work with multiple people or other moving objects in a crowded environment such as streets and factories will be quite common. One of the most important problems for the robot to solve is the real-time and accurate trajectory prediction of multiple agents around itself to ensure safe navigation. However, existing methods either require prior information to train models or critical parameters in advance or have insufficient prediction accuracy, which are not suitable for robot safe navigation in real applications. In this paper, we investigate the real-time multi-agent trajectory prediction problem for a robot in crowded unknown environments. To obtain the accurate predicted trajectories in real-time, we propose a new energy function optimization-based framework to forecast multi-agent trajectories in crowded unknown scenarios. This framework utilizes the observed data to infer unknown information without the dataset and optimizes the trajectories very efficiently, which can be adopted for robot motion planning and navigation in real-world environments.

Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction only in expectation, which is inadequate for safety-critical decision problems. Since a constraint satisfied in expectation remains a high probability of exceeding the cost threshold, solving constrained RL problems with high probabilities of satisfaction is critical for RL safety. In this work, we consider the safety criterion as a constraint on the conditional value-at-risk (CVaR) of cumulative costs, and propose the CVaR-constrained policy optimization algorithm (CVaR-CPO) to maximize the expected return while ensuring agents pay attention to the upper tail of constraint costs. According to the bound on the CVaR-related performance between two policies, we first reformulate the CVaR-constrained problem in augmented state space using the state extension procedure and the trust-region method. CVaR-CPO then derives the optimal update policy by applying the Lagrangian method to the constrained optimization problem. In addition, CVaR-CPO utilizes the distribution of constraint costs to provide an efficient quantile-based estimation of the CVaR-related value function. We conduct experiments on constrained control tasks to show that the proposed method can produce behaviors that satisfy safety constraints, and achieve comparable performance to most safe RL (SRL) methods.

Space teleoperation significantly extends human reach in space missions. However, traditional approaches are constrained by factors, such as the reliance on accurate dynamic models and the risk of operator fatigue during prolonged tasks. Additionally, while data-driven intelligent approaches reduce the need for prior knowledge, they have yet to adequately address the time delay issues inherent in these systems. To overcome these challenges, we introduce the belief state actor-critic (BSAC) method, the first deep reinforcement learning approach tailored for space teleoperation capture tasks within a bilateral control framework. We first establish a generalized agent-based architecture for space teleoperation, shifting decision-making from human operators to autonomous agents. Following a comprehensive analysis of the time delay challenges, we propose the BSAC algorithm, which integrates state augmentation and belief state techniques to mitigate the effects of delays in teleoperated Markov decision processes. Extensive experiments are conducted on the MuJoCo simulation platform, modeling a real hardware system across various scenarios. The learned policies are then successfully transferred and validated in a real-world setup, demonstrating the effectiveness and robustness of BSAC. In summary, our results support the feasibility of agent-based frameworks capable of overcoming time delay challenges in space teleoperation.

A cable-driven segmented manipulator (CDSM) has considerable potential in narrow space operations because it has a slender and light body with flexible mobility. However, the existing CDSM segment driving mechanisms are coupled to each other. The driving distance of the rear segment cable is superimposed with that of the front segment cable, which renders the cables’ drive distance inconsistent. Moreover, the system kinematics, dynamics, and control become extremely complex. In this article, a novel decoupling driving mechanism is proposed to solve the coupling problem, simplifying the modeling and control of the CDSM. The routing of the driving cable is designed based on the characteristics of the symmetrical offset (i.e., the same magnitude but opposite in direction) of the cable length applicable to joints with one and two degrees of freedom. By modifying the direction of the driving cable in the middle of the proximal segment, the driving cable length of the distal segment is unaffected by the change of the angle of the front segment. Moreover, to increase the drive stroke, a multiturn winding mechanism is designed, reducing the volume and mass of the driving box. Accordingly, an improved forward and backward reaching inverse kinematics is proposed for CDSM based on virtual joints. Compared with the Jacobian pseudo-inverse method, the computational efficiency is improved. Finally, the proposed mechanisms and methods are verified via a CDSM prototype. The results indicate that the proposed manipulator compared with typical manipulators has larger movement range, higher end velocity, and guaranteed accuracy due to the proposed decoupled driving and fast kinematics resolution.

Among the remarkable successes of Reinforcement Learning (RL), self-play algorithms have played a crucial role in solving competitive games. However, current self-play RL methods commonly optimize the agent to maximize the expected win-rates against its current or historical copies, resulting in a limited strategy style and a tendency to get stuck in local optima. To address this limitation, it is important to improve the diversity of policies, allowing the agent to break stalemates and enhance its robustness when facing with different opponents. In this paper, we present a novel perspective to promote diversity by considering that agents could have diverse risk preferences in the face of uncertainty. To achieve this, we introduce a novel reinforcement learning algorithm called Risk-sensitive Proximal Policy Optimization (RPPO), which smoothly interpolates between worst-case and best-case policy learning, enabling policy learning with desired risk preferences. Furthermore, by seamlessly integrating RPPO with population-based self-play, agents in the population optimize dynamic risk-sensitive objectives using experiences gained from playing against diverse opponents. Our empirical results demonstrate that our method achieves comparable or superior performance in competitive games and, importantly, leads to the emergence of diverse behavioral modes. Code is available at https://github.com/Jackory/RPBT.

Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algorithms are model-free, limiting sample efficiency and hindering their applicability in more challenging scenarios. In contrast, model-based reinforcement learning (MBRL), particularly algorithms integrating planning, such as MuZero, has demonstrated superhuman performance with limited data in many tasks. Hence, we aim to boost the sample efficiency of MARL by adopting model-based approaches. However, incorporating planning and search methods into multi-agent systems poses significant challenges. The expansive action space of multi-agent systems often necessitates leveraging the nearly-independent property of agents to accelerate learning. To tackle this issue, we propose the MAZero algorithm, which combines a centralized model with Monte Carlo Tree Search (MCTS) for policy search. We design an ingenious network structure to facilitate distributed execution and parameter sharing. To enhance search efficiency in deterministic environments with sizable action spaces, we introduce two novel techniques: Optimistic Search Lambda (OS($\lambda$)) and Advantage-Weighted Policy Optimization (AWPO). Extensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in terms of both sample and computational efficiency.

It is difficult to guarantee an empty living environment to prevent unexpected contact between the object being manipulated by the robot and unplanned obstacles. In this paper, we propose a planar compliant contact control method for planar manipulation to cope with unexpected contact. We first use sheet gel as a multi-dimensional passive elastic element and combine it with a two-finger gripper to design a multi-dimensional elastic gripper. Subsequently, we explore the lumped parameter model for the force-displacement relationship of gel deformation and combine the model with the high impedance motion of robots to design an elastic interaction controller. The controller not only actively adjusts the deformation of the gel to provide the desired contact force and torque depending on contact, but also performs avoidance by following the surface of obstacles. Finally, we design and deploy several planar compliant contact experiments to validate the proposed method and demonstrate the unexpected contact response in human-robot co-packing. The results show that our method enables the robot to remain compliant in the face of unexpected contact caused by unplanned obstacles, which provides a guarantee for safe manipulation. Physics experiments can be viewed in the attached video.

In recent years, there has been a growing demand for robotic manipulators to perform tasks in various unstructured environments and situations requiring precision and force control. However, traditional robotic arms have limitations in fully leveraging their advantages in such scenarios. To address this demand, we have designed a cable-driven serpentine manipulator (CDSM) that combines force and precision motion control. This control method allows for precise manipulation of forces and torques at the end-effector, particularly in applications like electric vehicle charging and narrow-space exploration. It also enables independent control in multiple configurations. We achieve force-position hybrid control in task space, ensuring accurate control of end-effector force while achieving precise position control in other directions. Additionally, we implement joint angle closed-loop control in joint space to reduce the impact of cable elasticity deformation and friction on joint motion accuracy. Finally, servo control is applied at the lowest motor level. This paper investigates the modeling, sensing, and control of CDSM within a unified framework of hybrid motion/force control. Through experiments and simulations, we demonstrate the high accuracy and practicality of this control method in various scenarios.

Retractable worm robots possess hyper-flexibility, allowing them to work in confined spaces that are difficult for humans. However, the spatial locomotion control of these robots remains challenging due to the robots’ large degrees of freedom. To address this challenge, we propose a phase synthesis (PS) scheme for retractable worm robots. The scheme combines an undulating gait inspired by caterpillars with three-dimensional movement commands. We first introduce the kinematics model and real-world prototype of our retractable worm robot, called RW-Robot, and then we introduce footstep phases to express the timing of segments’ spatial movement. According to the length of movement periods, we classify the movement into short-term movements and long-term movements and compress their patterns in the frequency domain. Our PS scheme aligns the patterns according to the footstep phases to generate new gaits of spatial locomotion. We evaluate the scheme in real-world experiments, including steering and climbing a slope. The experimental results indicate that our scheme allows the RW-Robot to perform flexible spatial locomotion from simple user input.

Rigid robotic arms have strong load capacity and mature control schemes, but they are often not suitable for working in narrow spaces with multiple obstacles. Flexible robotic arms can bend more freely, but they have weaker load capacity and are not suitable for fine manipulation. In view of this, inspired by the biological structure of the Nycticorax Violaceus, a novel scalable flexible robotic arm for addressing complex spatial fault-tolerant manipulation requirements is proposed in this paper. The robotic arm can be concealed within a shell at the end of a rigid arm without affecting the normal use of the rigid arm. When needed, it can extend from the shell of the rigid arm, serving as a flexible auxiliary operating end attached to the rigid arm base for large curvature bending. This paper presents the structural design of a flexible arm and provides kinematic and dynamic models for the flexible arm extension operation. Through a case study, the kinematic and dynamic models proposed in this paper are validated.

Collaborative path planning for robot swarms in complex, unknown environments without external positioning is a challenging problem. This requires robots to find safe directions based on real-time environmental observations, and to efficiently transfer and fuse these observations within the swarm. This study presents a filtering method based on Fast Fourier Transform (FFT) to address these two issues. We treat sensors’ environmental observations as a digital sampling process. Then, we design two different types of filters for safe direction extraction, as well as for the compression and reconstruction of environmental data. The reconstructed data is mapped to probabilistic domain, achieving efficient fusion of swarm observations and planning decision. The computation time is only on the order of microseconds, and the transmission data in communication systems is in bit-level. The performance of our algorithm in sensor data processing was validated in real world experiments, and the effectiveness in swarm path optimization was demonstrated through extensive simulations.

Single-track two-wheeled robots with the excellent characteristics of lightweight, trafficability and maneuverability exhibit great potential in goods transportation, field patrol and exploration in narrow environments. Motion planning as an indispensable component acts an essential role in autonomous navigation systems of single-track two-wheeled robots. This paper presents an efficient and robust motion planning framework for this type of robot in unknown and unstructured environments. Firstly, an initial trajectory is generated without regard to obstacles. Then, we obtain the lattice graph to search a feasible path based on the simplified kinematic bicycle model. The collision penalty function is immediately established to push the initial trajectory far away from obstacles as long as it collides with obstacles. The time allocation can be lengthened if the trajectory cannot ensure dynamical feasibility. Simulation experiments demonstrate the proposed motion planning has robustness and high-performance.

The cable-driven redundant manipulator (CDRM) possesses remarkable flexibility and holds substantial potential for application in constrained environments. To ensure both the smooth movement of the end-effector during delicate operations and the safety of interactions with the surrounding environment, real-time sensing of forces acting on both the end and links is imperative. Current in situ sensor-based methods face limitations in their applicability to CDRMs due to size and load capacity constraints. Moreover, these methods fall short in measuring contact force and its location along the entire arm. In this article, we introduce an ex situ sensing approach for capturing the six-dimensional (6-D) force at the end and the contact force on the linkages of a CDRM. First, a multispace recursive dynamic model of the CDRM is established using the Newton–Euler method. This model establishes mapping relationships among cable tensions, joint torques, and operational forces at the end-effector. Then, a simplified dynamic model for the recursive subsystem is derived based on joint motion transmission relationships and recursive equations. This model decouples the dynamic equations and provides a versatile force-sensing model. It enables the realization of 6-D force/torque sensing at the end-effector, as well as the determination of the magnitude and location of external forces acting on the links. Finally, compliant controllers are designed based on different external force-sensing methods to cater to diverse operational requirements. Experimental validation of the proposed methods is conducted on a CDRM prototype. The results demonstrate that the accuracy of end-effector force sensing exceeds 95%, torque sensing surpasses 90%, and the positioning error of the link's contact force sensing is less than 20 mm. Furthermore, the compliance controllers exhibit excellent smoothness in tasks involving human–robot interaction.

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the reinforcement learning (RL) agent's behavior as the environment changes over its lifetime while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, that is, dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process (CRP) prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of stability, overall performance, and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this article proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.

Guidance robots that can guide people and avoid various obstacles, could potentially be owned by more visually impaired people at a fairly low cost. Most of the previous guidance robots for the visually impaired ignored the human response behavior and comfort, treating the human as an appendage dragged by the robot, which can lead to imprecise guidance of the human and sudden changes in the traction force experienced by the human. In this paper, we propose a novel quadruped guidance robot system with a comfort-based concept. We design a controllable traction device that can adjust the length and force between human and robot to ensure comfort. To allow the human to be guided safely and comfortably to the target position in complex environments, our proposed human motion planner can plan the traction force with the force-based human motion model. To track the planned force, we also propose a robot motion planner that can generate the specific robot motion command and design the force control device. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios. (Video11Video demonstration: https://youtu.be/gd-RcYOqGuo.)

This paper presents an efficient and safe method to avoid static and dynamic obstacles based on LiDAR. First, point cloud is used to generate a real-time local grid map for obstacle detection. Then, obstacles are clustered by DBSCAN algorithm and enclosed with minimum bounding ellipses (MBEs). In addition, data association is conducted to match each MBE with the obstacle in the current frame. Considering MBE as an observation, Kalman filter (KF) is used to estimate and predict the motion state of the obstacle. In this way, the trajectory of each obstacle in the forward time domain can be parameterized as a set of ellipses. Due to the uncertainty of the MBE, the semi-major and semi-minor axes of the parameterized ellipse are extended to ensure safety. We extend the traditional Control Barrier Function (CBF) and propose Dynamic Control Barrier Function (D-CBF). We combine D-CBF with Model Predictive Control (MPC) to implement safety-critical dynamic obstacle avoidance. Experiments in simulated and real scenarios are conducted to verify the effectiveness of our algorithm. The source code is released for the reference of the community11Code: https://github.com/jianzhuozhuTHU/MPC-D-CBF..

Aiming to intercept non-cooperative targets when global positioning signals are lost or jammed, an Extended State Observer (ESO) based disturbance compensation guidance law for the unmanned surface vessel (USV) is developed. This interception guidance law addresses the scenario where the relative velocity and target maneuver acceleration cannot be directly measured. To circumvent nonlinearities and parameter coupling, a simplified relative interception dynamics model is proposed. Furthermore, an extended state observer is designed to estimate the unmeasured state and the unknown disturbance. Utilizing these estimations, an ESO-based disturbance compensation guidance law (ESO-DCGL) is developed, which can compensate for target maneuvers and track the desired trajectory. Simulation results demonstrate the effectiveness of this guidance law in intercepting non-cooperative targets with high maneuverability.

For the roll control of an unmanned motorcycle equipped with twin control moment gyros (CMGs), the optimal allocation between the steering motor and CMGs when encountering strong disturbance is rarely investigated in existing studies. In this paper, based on an extended state observer (ESO) and a novel control allocator (CA), a robust control scheme ESO-CA is proposed for the unmanned motorcycle to resist disturbances. The ESO enables the estimation and compensation of the lumped disturbance, which incorporates unmodeled dynamics, parameter perturbations and external disturbances. Based on a novel optimization formulation that considers the integral saturations of actuators, the control allocation method is developed to minimize total energy consumption and reduce steering chattering. Two comparative numerical simulations and one physical experiment are provided to demonstrate the effectiveness of the proposed control scheme.

In our previous work, we proposed the extended PID (EPID) controller, which is a state-space extension of traditional PID control. Compared to PID control, EPID is more suitable for multi-input-multi-output (MIMO) and higher-order systems. In this paper, we further extend EPID to nonminimum phase systems and investigate its performance limitation. EPID uses feedback of all state tracking errors. But for nonminimum phase systems, the reference trajectories for the internal states are unknown (assuming we do not have the system model), making us decide to take out the internal states from the integral part to avoid an unbounded input, which results in a slightly different controller form. Besides, in previous study, we found an important property of EPID is that it can achieve accurate tracking/rejecting for time-varying references/disturbances by using a high integral gain. However, when applied to nonminimum phase systems, we found that the integral gain cannot be set too high, otherwise the closed-loop system will be unstable, which indicates an inherent performance limitation. To verify this, simulation results are provided by applying EPID to a hypersonic vehicle model and a cart pole system.

The powerful learning ability of deep neural networks enables reinforcement learning (RL) agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume identically and independently distributed (i.i.d.) inputs, which unfortunately does not hold in the general RL paradigm where the training data are temporally correlated and nonstationary. This issue may lead to the phenomenon of "catastrophic interference" and the collapse in performance. In this article, we present interference-aware deep Q-learning (IQ) to mitigate catastrophic interference in single-task deep RL. Specifically, we resort to online clustering to achieve on-the-fly context division, together with a multihead network and a knowledge distillation regularization term for preserving the policy of learned contexts. Built upon deep Q networks (DQNs), IQ consistently boosts the stability and performance when compared to existing methods, verified with extensive experiments on classic control and Atari tasks. The code is publicly available at https://github.com/ Sweety-dm/Interference-aware-Deep-Q-learning.

The application requirements of the tendon-sheath mechanism in the field of precision machinery are becoming increasingly extensive. However, the contact friction between the tendon and sheath seriously affects the transmission accuracy. In the case of unavoidable friction, optimizing the tendon transmission path to reduce tension loss and elastic deformation has become an important research direction. In this article, the influence law of the tendon transmission path on the tension and displacement transmission is obtained using the two parameters related to the curvature of the transmission path: total bending angle and equivalent tendon length. Then, based on the optimal control theory and minimum principle, the different transmission path solutions of the minimum tension loss, the minimum tendon deformation, and the coupling of tension and displacement are obtained; the numerical optimization method verifies the correctness of the proposed theory. Finally, an optimal design of a tendon-constrained synchronous rotation mechanism for the manipulator is carried out, and the linkage performance is greatly improved by optimizing the transmission path.

Continuum robots are typically slender and flexible with infinite freedoms in theory, which poses a challenge for their control and application. The shape reconstruction of continuum robots is vital to realize closed-loop control. This paper proposes a novel general real-time shape reconstruction framework of continuum robots based on the piecewise polynomial curvature (PPC) kinematics model. We illustrate the coupling between orientation and position at any given location of the continuum robots. Further, the coupling relation could be bridged by the PPC kinematics. Therefore, we propose to estimate the shape through multi-attitude solving, using the off-the-shelf orientation sensors, e.g., IMUs, mounted on certain locations. The approach gives a valuable framework to real-time shape reconstruction of continuum robots, which is general, accurate and convenient. The accuracy of our approach is verified in the experiments of distinct physical prototypes.

Large-area and high-precision tactile sensing information can not only improve the stability of robot grasping but also compensate for the lack of visual information in specific environments such as turbid underwater, dimness, and smoke. In this paper, we devise a universal jamming gripper with high-quality tactile sensing capability. The gripper adopts the particle jamming mechanism for grasping, and simultaneously uses a built-in camera to detect the deformation of its surface to obtain tactile information. To make the inside of the gripper transparent, glass beads and liquid with the same refractive index are applied as the internal filling. Besides, special treatments are taken to improve the tactile perception resolution of the gripper. The design perfectly merges visual-based tactile sensing into the traditional universal jamming gripper without changing its original gripping performance, making it possible for simultaneous grasping and sensing. To verify the tactile perception and grasping ability of the gripper in specific environments, we design two underwater experiments for grasping and pipe leak detection based on tactile information. Both have achieved a success rate not less than 95%, which demonstrates the effectiveness of the proposed gripper for manipulation in low visibility environments.

In this paper, we propose a novel real-time 6D object pose estimation framework based on Point-wise Attention Keypoints Voting Network (PAV-Net). Compared with previous methods that use all features indiscriminately, we evaluate and integrate the visible points features before estimation to deal with the unstructured and uneven properties of point-wise features. Specifically, we first locate the object roughly by object detection and transfer the captured point cloud coordinates to the local center. Then we extract point-wise features from RGB images and point clouds respectively and perform semantic segmentation. Finally, the point-wise features are screened and integrated with the help of the attention keypoints voting to predict the accurate keypoint coordinates, and the 6D object pose can be obtained within keypoints fitting. The proposed method can effectively avoid external interference and improve the efficiency of influential point features utilization by point-wise attention voting so that the framework only needs a simple feature extraction network support to have better real-time performance. Extensive experiments confirm this conclusion and show that the performance of proposed framework on LineMOD and YCB-Video datasets is superior to other real-time pose estimation methods at the same speed.

Autonomous navigation of ground robots has been widely used in indoor structured 2D environments, but there are still many challenges in outdoor 3D unstructured environments, especially in rough, uneven terrains. This paper proposed a plane-fitting based uneven terrain navigation framework (PUTN) to solve this problem. The implementation of PUTN is divided into three steps. First, based on Rapidly-exploring Random Trees (RRT), an improved sample-based algorithm called Plane Fitting RRT*(PF- RRT*) is proposed to obtain a sparse trajectory. Each sampling point corresponds to a custom traversability index and a fitted plane on the point cloud. These planes are connected in series to form a traversable “strip”. Second, Gaussian Process Regression is used to generate traversability of the dense trajectory interpolated from the sparse trajectory, and the sampling tree is used as the training set. Finally, local planning is performed using nonlinear model predictive control (NMPC). By adding the traversability index and uncertainty to the cost function, and adding obstacles generated by the real-time point cloud to the constraint function, a safe motion planning algorithm with smooth speed and strong robustness is available. Experiments in real scenarios are conducted to verify the effectiveness of the method. The source code is released for the reference of the community11Source code: https://github.com/jianzhuozhuTHU/putn..

Keeping balance is one of the most important tasks of a motorcycle. The steady-state manifold is proposed in this paper to explore the inherent dynamics and the balance properties of a riderless motorcycle. The dynamic and kinematic characteristics are analyzed based on the manifold and are validated by simulation. Comparing to traditional control method, the usefulness of the manifold in control is shown through the design of a novel control strategy. Furthermore, based on the analysis and the simulation, the potential applications of the manifold for control and planning are summarized.

Finger dexterity and tactile perception are key capabilities for humans to manipulate objects within hand, as well as robots. Inspired by the thumb-forefinger dexterous manipulative movement, we devised a novel robotic finger with an active rotational tactile sensor (i.e. TacRot), and mounted the finger on a parallel-jaw gripper. By processing the high-resolution images of the vision-based tactile sensor, we achieved depth reconstruction of the surface and localization of the contact area. To improve gripping flexibility and stability, we applied a self-adaptive grasping strategy with real-time contact detection feedback, which performed 94% success rate in experiment. Based on the rotational actuator at the fingertip, we proposed two in-hand manipulation primitives: (1) pivot: fingertips co-rotating for object reorientation; (2) twist: fingertips contra-rotating for object spin. The primitives are theoretically analyzed and experimentally verified in two practical tasks: pivoting a paper cup under vertical constraints and twisting a screw with spin angle estimation. Our design and experiments demonstrate a feasible way to enhance the active tactile manipulation ability for common parallel-jaw grippers.

The Unmanned Motorcycle (UM) is a robotic system aiming to autonomously drive in off-road trail environments, keep the tracked routes under surveillance, and provide help within its ability. One core component of the UM is its vision module that utilizes deep learning techniques to get pixel-accurate trail understanding. By learning valuable features from the off-road trail datasets, the UM is expected to understand traversable areas in a large variety of off-road trail scenes. However, currently, there is no publicly available dataset on the off-road trail scene dataset for UMs, which motivates us to conduct a comprehensive dataset generation and collection. In this paper, we build four virtual worlds to generate Synthetic Off-road Trail (SORT) dataset. The dataset seeks to positively influence the development of data-driven trail segmentation for UMs, which we hope other UM researchers will use the dataset and contribute to it. By this method, the dataset can be easily regenerated and tailored for other robot platforms. The whole workflow of tackling the scene parsing task of the UM is provided, and we hope our work can inspire the robot perception research community and help push the usage of realistic visual simulation to develop learning-based algorithms in their research fields. Multimedia material of our dataset is available at https://www.youtube.com/watch?v=9biV7fKxKRo&list=PLxSoUS6AFfb-KlqzxpqlMfKoF9qur4h0p.

Cooperative planning is one of the critical problems in the field of multi-agent system gaming. This work focuses on cooperative planning when each agent has only a local observation range and local communication. We propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling method. Two main contributions of this paper are based on the comparisons with previous work: (1) we realize feasible and dynamic adjacent information fusion using GraphSAGE (i.e., Graph SAmple and aggreGatE), which is the first time this method has been used to deal with the cooperative planning problem, and (2) a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation, to obtain an effective and stable training process in our model. Experimental results demonstrate the good performance of our proposed method.

A cable-driven segmented manipulator (CDSM) has superior dexterity for operations in confined space due to its light-slender body and redundant degree of freedoms (DOFs). However, its inverse kinematics resolving and configuration planning are very challenging due to the complex structure and strict constraints. In this article, we propose a two-layer geometric iteration (TLGI) method for inverse kinematics resolving and configuration-constrained Cartesian path planning. The computation efficiency is largely improved and singularities are avoided. First, the end-effector attitude is decomposed into a direction vector and a rotation angle. The former and the end-effector position are combined into state variables of the inner layer, and the latter is treated separately as the state variable of the outer layer. Then, the TLGI method enables to rapidly reach the desired 6-DOF pose by two-layer iterations, i.e., the inner and outer loop iteration. Second, during the inner loop iteration, the CDSM is modeled as an equivalent articulated arm whose end-effector position and direction is the same as that of CDSM, but its links length and joint angles depend on the current configuration of CDSM. Then, the efficient forward and backward reaching inverse kinematics (FABRIKs) method is extended to apply on CDSM so that it can fast reach the inner state variables. During the outer loop iteration, three different rotation cases, i.e., the rotating around the end, root, and both end and root, are designed to switch automatically to reach the outer state variable iteratively. Moreover, by parameterizing geometric constraints of the environment, a TLGI-based configuration-pose simultaneous planning method is also put forward to efficiently achieve additional configuration constraints for operations of CDSM in confined space. Finally, the proposed method is verified by both the simulations and experiments.

The attitude control of an underactuated reusable launch vehicle (RLV) in the reentry phase involving nonminimum phase problem and control input constraints is investigated in this article. To address the nonminimum phase problem, an approach combining output redefinition and robust backstepping is proposed, where a synthetic output is constructed using the combination of the original output and the internal states to obtain stable zero dynamics, and then robust backstepping is performed on the new output. Besides, the ideal internal dynamics are obtained by using optimal bounded inversion, which are incorporated into the controller as the reference trajectories for the internal states to improve the output tracking accuracy. To cope with the control input constraints, a simple and useful anti-windup strategy is proposed by using feedback error clipping, which is shown to be very effective in mitigating control input saturation. Numerical simulations are given to validate the effectiveness of the proposed method.

Safe driving and stable paths are essential for the autonomous vehicle navigation, that large and fast steering angle should be avoided. This paper addresses a local path generation problem for autonomous vehicles while the least maximum curvature and the shortest length are obtained with limited curvature rate. The properties of the novel feasible paths based on continuous curvature turns are investigated. And a simple and fast computational method is presented to solve the problem by iterative procedure. The simulation in this paper shows that, compared with recent researches, the performance of least maximum curvature and shortest length is obtained by the novel local path planner, and the driving behavior is closer to the human driver operation.

The traditional master-slave teleoperation relies on human expertise without correction mechanisms, resulting in excessive physical and mental workloads. To address these issues, a co-pilot-in-the-loop control framework is investigated for cooperative teleoperation. A deep deterministic policy gradient (DDPG) based agent is realised to effectively restore the master operators' intents without prior knowledge on time delay. The proposed framework allows for introducing an operator (i.e., copilot) to generate commands at the slave side, whose weights are optimally assigned online through DDPG-based arbitration, thereby enhancing the command robustness in the case of possible human operational errors. With the help of interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy identification, force feedback can be reconstructed at the master side without a sense of delay, thus ensuring the telepresence performance in the force-sensor-free scenarios. Two experimental applications validate the effectiveness of the proposed framework.

To date, soft robots have been increasingly designed and analyzed, especially, Soft Continuum Manipulators (SCMs). Due to dexterous deformability, their Inverse Kinematics (IK) is still difficult to solve. Cyclic Coordinate Descent (CCD) algorithm is one of the classical optimization algorithms to solve IK of rigid manipulators with prismatic or rotational joints. However, it cannot be directly extrapolated to SCMs with configuration space parameters such as center arc, bending angle, and torsion angle. Here, we modified the CCD algorithm from a new view and proposed several tricks to set constraints. Numerical and experimental results show that the soft-CCD algorithm can quickly and accurately generate solutions for IK of SCMs. This study provides the necessary kinematic foundation for fault tolerance, obstacle avoidance, trajectory planning, and other further explorations of SCMs.

Although computer vision has the advantages of long detection distance and large amount of information, it also has certain limitations for complex scenes such as dimness, reflections, and smoke. In order to solve the problem of robot grasping in these scenes, we designed a novel gripper that can search, identify and grasp objects based on tactile information. The gripper can effectively grasp the objects in real life, and can sense the shape and posture of the objects through the touch. We proposed a lifting finger structure that allows the gripper to switch between sensing and grasping modes. We applied visual-tactile detection methods to obtain tactile information and propose a feature extraction algorithm based on U-net. We designed a method of grasping the center of mass of the object contour, and the success rate of the grasping can reach 85%. In addition, we also designed experiments to show the feasibility of object searching and grasping by tactile information when visual information is not available.

Rollover is a potential dangerous factor for mobile robots to accomplish a task. However, to our best knowledge, there still lacks a systematic research on the rollover mechanism and active rollover prevention control of mobile robots in the literature. This paper aims to propose a general control framework for rollover prevention of high-speed wheeled mobile robots. First, the lateral dynamics of the robot is modelled and the Load Transfer Ratio (LTR) is used as an index to measure the rollover level. Second, an optimal algorithm-based reference governor (RG) is developed, by which the wheel speed command that satisfies the constraint is induced, retaining the actual LTR within the threshold safety value. In addition, an integral sliding mode (ISM) wheel speed tracking controller is proposed. Lastly, simulations results show that for both trajectory tracking and path following cases, the controlled robot avoids possible rollover successfully.

Symmetry in running was observed by Marc Raibert and was applied to simplify the control of dynamic legged systems. In this paper, we show that symmetry also exists in biped walking and investigate it using two simplified 2D models, that are, the inverted pendulum (IP) model and the linear inverted pendulum (LIP) model, both leading to similar conclusions. To characterize the symmetry in biped walking, the concept of acceleration factor is proposed. Symmetry occurs when the acceleration factor is zero, which results in an unchanged mid-stance velocity. And an important property of symmetry is that the n-step reachable region and the n-step controllable region are exactly the same. This means that if we can achieve speed B from A in n steps, then we can also achieve speed A from B in n steps. Symmetry in walking helps us to better understand human walking and also provides an intuitive way to control robotic walking. As an example, we propose a feedforward controller and a feedback controller, respectively, which can regulate the walking speed very effectively. This work provides us some new insights to view biped walking.

Accurate tracking control of nonminimum phase systems relies on the calculation of the ideal internal dynamics (IID). Traditional IID calculation methods fail when applied to nonminimum phase nonhyperbolic systems (systems with nonhyperbolic zero dynamics). Recently, we propose the optimal bounded inversion method for IID calculation, which obtains IID by solving a trajectory optimization problem. In this paper, we extend our previous result and show that optimal bounded inversion can also deal with nonminimum phase nonhyperbolic systems. More than that, it is also possible to achieve different control goals by setting different cost functions. Particularly, three cases are investigated in this paper. The first uses minimal initial value deviation as the cost function, resulting in "T-IID" which can achieve accurate output tracking. The second applies minimal terminal value as the cost function, resulting in "S-IID" which leads to a final rest for the system. The last combines "T-IID" and "S-IID" to achieve a compound goal. The effectiveness is verified through Matlab simulations of a two-cart inverted-pendulum system.

This article considers admissibility analysis and robust${H_\infty }$control for continuous-time Takagi–Sugeno fuzzy descriptor systems with norm-bounded uncertainties in all parametric matrices. The system under consideration contains singular derivative matrices and different membership functions, thus it generalizes other related forms. First, uncertainties in derivative matrices are divided into two cases, i.e., one is expressed by a constant matrix left multiplied by an invertible uncertain matrix and the other is produced by its dual form. Then, admissible conditions and${H_\infty }$performance for the system with the first case of uncertainties are derived based on a new augmented system. As for the second case, the admissibility analysis is converted into the first case by an equivalent companion system then solved as well. All conditions are cast into strict linear matrix inequalities. Besides, due to the introduction of a new nonquadratic fuzzy Lyapunov function and slack decision variables, the proposed methods are less conservative than related ones. Finally, simulation examples are provided to illustrate improvements and effectiveness of the main results.

Limited by the operation time window and working space, space teleoperation tasks need to be completed within an expected time while ensuring that the end effector meets the physical constraints. Meanwhile, the interaction with unknown environments would cause uncertainty in the closed-loop system, which brings great challenges to the control design. To solve the above problems, the control performance issue for a class of space teleoperation systems subject to multiple constraints and interaction uncertainties is investigated in this article. The force interaction with the human operator/space environment is represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems, where the uncertain equivalent mass and damping parameters can be effectively described and captured by IT2 membership functions. In order to reduce the communication burden and satisfy the constraints of settling time, transient-state performance and operating space, a time-varying threshold event-triggered control scheme together with exponential-type Lyapunov function is developed for the first time. We show that, with the proposed controller, the synchronization tracking errors are guaranteed to converge to a user-defined residual set within preassigned settling time, and never exceed the prescribed range despite unknown control direction and actuator faults, which solves the long-standing constraint issue with more flexibility due to the fact that the related constraints can be arbitrarily specific within the physically available range. Moreover, the convergence set is only dependent on fewer user-defined parameters rather than approximation errors, which provides an effective analysis technique to deal with the difficulty that the convergence accuracy is difficult to calculate quantitatively in the presence of unknown disturbance. Detailed simulation results are provided to show the effectiveness and merit of the proposed control strategy.

Grasping unstructured objects and sensing the contact force are two vital issues for grippers. However, it is still difficult for most existing grippers to realize these two functions simultaneously. In this article, we revise the traditional fin-ray finger by inserting a series of rigid nodes into the compliant structure and develop an adaptive two-finger gripper. This design linearizes the gripper's deformation-force relationship and enables an intrinsic force sensing ability without any tactile sensor. Experimental results show that the finger has high accuracy in sensing the external force applied at its middle part (average error less than 3%) but much larger errors appear near its two ends. Further experiments indicate that the gripper functions well in sensing the total grasping force (average error less than 8%). Although larger errors are observed in estimating the force distribution at each node, the variation tendency of the sensed force coincides well with the ground truth. Experiments are also carried out on grasping free-form objects and performing pick-and-place operations to further prove the gripper's adaptive grasping and intrinsic force sensing abilities.

This paper considers the admissibility and robust stabilization via state feedback for continuous-time T-S fuzzy descriptor systems (TSFDS) with a class of uncertainties. First, the admissibility of the nominal system without uncertainties is investigated. An equivalent augmented system is presented to deal with different and singular derivative matrices. Then, admissible conditions for the open-loop and close-loop systems are both derived based on a non-quadratic fuzzy Lyapunov function. Second, the admissibility and robust stabilization of TSFDS with uncertainties in all matrices are investigated. The uncertainty in each derivative matrix is equivalently expressed by a constant matrix left multiplied by an invertible uncertain matrix so that a similar augmented system can be constructed. Then admissible conditions are derived. This paper generalizes existing related results since we consider a wider class of TSFDS with different derivative matrices and different membership functions in each subsystem. All conditions are expressed as strict linear matrix inequalities (LMIs). Finally, a simulation example is provided to show effectiveness of the proposed results.

This paper is concerned with interval type-2 (IT2) fuzzy control design for a class of nonlinear space teleoperation systems with external disturbances and time-varying delays. IT2 fuzzy model based (FMB) control design with exponential-type Barrier Lyapunov function (EBLF) is presented to address state constraints, communication burden from ground stations to satellites (space-robot), and uncertain human/environment interaction parameters in a unified event-triggered control structure. We show that, with the proposed adaptive event-triggered control scheme, the exponential convergence performance of the synchronization tracking errors is guaranteed, while the prescribed constraint requirement is satisfied. Simulation results are provided to validate the effectiveness of the proposed controller.

In this paper, we investigate the problem of balance and tracking controller design for an autonomous bicycle subject to unmodeled dynamics, unknown parameters, and unmeasured states. A composite deep learning based control strategy is proposed, comprising of active disturbance rejection control (ADRC) and Deep Deterministic Policy Gradient (DDPG). Different from most conventional approaches that fail to consider path information and depend critically on exact dynamics, the proposed control scheme uses the DDPG algorithm to learn a virtual control action and then employ ADRC to handle uncertainties and stabilize the bicycle. Extensive simulations are conducted to assess the performance of the composite learning method. The results indicate that the bicycle controlled by our method can follow along a predetermined trajectory while maintaining balance.

Most researches on balance control of the bicycle robots are for the situation that the bicycle robot is with constant forward velocity and constant feedback gain, but are not appropriate to be employed for time varying forward velocity situation. In this paper, the nonlinear Euler-Lagrange model of the bicycle robot and the simplified nonlinear descriptor state space model are firstly deduced. A polynomial controller, rather than a constant gain feedback one, is proposed, which constituting the nonlinear closed-loop descriptor system. The sufficient condition on examining the stability of closed-loop system, together with one alternative method on designing a polynomial controller utilizing the SOSTool, are then proposed. By this work, the polynomial controller is broadened to be applied on nonlinear descriptor system, and on the balance and forward control of bicycle robot with time varying forward velocity. One numerical example shows the capacity of the control scheme proposed in this paper.

This paper proposes a multi-task control strategy for a quadruped robot named THU-QUAD II. The mechanical design of the robot ensures a wide range of motion for all joints, which allows it to stand and walk like a mammal as well as sprawl to the ground and crawl like a reptile. Five basic leg configurations are defined for the robot, including four mammal-type configurations with bidirectional knees and one sprawling-type configuration. A multi-task control framework is developed by combining configuration selection and gait planning. According to the locomotion environments, the robot can nimbly switch between different configurations, which gives it more flexibility when facing different tasks. For the mammal-type configuration, a parametric climbing gait is designed to traverse structural terrain. For the sprawling-type configuration, a crawling gait is designed to achieve robust locomotion on uneven terrain. Simulations and experiments show that the robot is capable to move on multiple challenging terrains, including doorsills, stairs, slopes, sand and stones. This paper demonstrates that even some challenging locomotion tasks can be achieved in a rather simple way without using complicated control algorithms, which suggests us to rethink about the leg configurations in designing quadruped robots.

The continuum robot has attracted more attention for its flexibility. Continuum robot kinematics models are the basis for further perception, planning, and control. The design and research of continuum robots are usually based on the assumption of piecewise constant curvature (PCC). However, due to the influence of friction, etc., the actual motion of the continuum robot is approximate piecewise constant curvature (APCC). To address this, we present a kinematic equivalent model for continuum robots, i.e. APCC 2L-5R. Using classical rigid linkages to replace the original model in kinematic, the APCC 2L-5R model effectively reduces complexity and improves numerical stability. Furthermore, based on the model, the configuration self-estimation of the continuum robot is realized by monocular cameras installed at the end of each approximate constant curvature segment. The potential of APCC 2L-5R in perception, planning, and control of continuum robots remains to be explored.

This paper focuses on analyzing and comparing the performance of two robust fault detection (FD) criteria for discrete-time linear parameter varying (LPV) systems with bounded uncertainties, namely the state estimation error-based criterion and the classical residual-based criterion. First, a new FD criterion for the detection of multiple multiplicative actuator faults is proposed by testing consistency between the state estimation errors and the healthy state estimation error sets on-line. Then, a guaranteed FD condition is established based on set-separation of healthy and faulty invariant sets of state estimation error. Moreover, the generalized minimum detectable fault (MDF) for multiple actuator faults is defined and computed in order to characterize the performance of the two FD criteria. Finally, a proof is provided to compare the conservatism of the FD criterion using state estimation errors with the classical one based on residuals. At the end of this paper, a numerical example is used to illustrate the effectiveness of the obtained results.

Traversing at a high speed while maintaining stability is important for the application of quadruped robots. Prior works mainly concentrated on optimizing the stability margin of quadruped robots when walking through a variety of terrains. However, the problem of improving quadruped robots' walking velocity with static gait is less concerned in their works. In this paper, the static gait planning problem is considered under the assumption that a set of irregular footholds on the rough terrain is given, and two approaches are proposed to improve the walking speed. The first one is a distance optimization algorithm, which can minimize the moving distance of the center of gravity (COG) in the stance phases based on the stability and the kinematic constraint. The other is a velocity optimization algorithm, which enables the body and the feet to move at the highest velocity with the joint angular velocity limit. The joint application of these two optimization algorithms significantly improves the walking speed of the quadruped robot. Simulation results in V-REP are presented to demonstrate the effectiveness of the proposed approaches in improving the walking speed. Compared with the traditional gait planning techniques, one that moves the robot with the optimal stability margin, and the other that moves the robot without optimizing the velocity, our algorithms increase the average walking velocity by 81.6% and 32.8%, respectively.

In this article, we present an adaptive prescribed-time control method for a class of nonlinear telerobotic systems with actuator faults and position error constraints. Extended from prescribed-time stability, practically prescribed-time stability (PPTS) is proposed for the first time aiming at stability analysis and control synthesis of nonlinear systems with disturbance and uncertainty. We show that, under the control scheme in the framework of PPTS, the system states are guaranteed to converge to a user-defined set (physically realizable) within user-defined settling time (physically realizable). Based on PPTS, an adaptive fault-tolerant controller is developed by integrating a novel exponential-type barrier Lyapunov function. Rigorous stability analysis based on back-stepping approach proves that, under the proposed control strategy, synchronization errors converge to a user-defined residual-set within predefined settling time and never exceed the prescribed range. Universal performance indexes, including the settling time, residual-set, accuracy, and overshoot, can be user-defined and only dependent on fewer user-defined parameters. Simulation results illustrate the effectiveness of the developed control scheme.

With many degrees of freedom (DOFs), a hyper-redundant manipulator has superior dexterity and flexible manipulation ability. However, its inverse kinematics and configuration planning are very challenging. With the increase in the number of DOFs, the corresponding computation load or training set will be much larger for traditional methods (such as the generalized inverse method and the artificial neural network method). In this paper, a segmented geometry method is proposed for a spatial hyper-redundant manipulator to solve the above problems. Similar to the human arm, the hyper-redundant manipulator is segmented into three sections from geometry, i.e., shoulder, elbow, and wrist. Then, its kinematics can be solved separately according to the segmentation, which reduces the complexity of the solution and simplifies the computation of the inverse kinematics. Furthermore, the configuration is parameterized by several parameters, i.e., the arm-angle, space arc parameters, and desired direction vector. The shoulder has proximal four DOFs, which is redundant for positioning the elbow and avoiding the joint limit. The arm-angle parameter is defined to solve the redundancy. The wrist consists of the distal two DOFs, and its joints are determined to match the desired direction vector of the end-effector. All the other joints (except for the joints belonging to shoulder and wrist) compose the elbow. These joint angles are solved by using space arc-based method. The configuration planning for avoiding joint limit, obstacles, and inspecting narrow pipeline are detailed for practical applications. Finally, circular trajectory tracking and pipeline inspection are, respectively, simulated and experimented on a 20-DOFs hyper-redundant manipulator. The results show that the proposed method can give solutions of the three-dimensional-pose-determining problem and the configuration-planning problem. The computation of the inverse kinematics is simplified for real-time control. It can also be applied to other spatial hyper-redundant manipulators with similar serial configurations.

Continuum robots composed of elastic backbones have a broad application prospect in the narrow and restricted environment because they overcome the disadvantages of traditional articulated robots, such as being bulky and inflexible. Statics plays an important role in the planning and control of the continuum robot composed of the elastic backbone. Pseudo-Rigid Body (PRB) theory has shown great potential in the description of flexible body statics. The PRB 3R model accurately describes the large deformation of the flexible body and has high computational efficiency. However, PRB 3R models mostly focus on the planar static modeling, and there are few applications in three-dimensional (3D) statics. In this paper, a 3D static modeling method of cable-driven continuum robot based on PRB 3R theory is proposed. By introducing the equilibrium constraint equations of resultant force/moment and bending plane normal of the elastic backbone, the state of the continuum robot is determined. The 3D static equations established by the proposed method take into account the comprehensive effects of the elastic force, external force, gravity and friction. A static verification experiment system of the cable-driven continuum robot is designed to verify the proposed method. The accuracy of the proposed method is verified by comparison with experimental data. The maximum position error between simulation and experimental results is 7.6%.

In this paper, a Takagi-Sugeno (T-S) fuzzy descriptor approach for control of a two-link free-floating space manipulator (FFSM) is proposed. The T-S fuzzy descriptor model of the FFSM is first derived from its nonlinear dynamic model, which makes more sense in reality since it avoids the use of joint acceleration measurement and the inversion of inertia matrix. And some nonlinear terms are considered as uncertainties to balance the complexity and accuracy of the model. Then a robust controller based on the Lyapunov stability theory is designed and reformulated as a linear matrix inequality (LMI) optimization problem which can be efficiently solved with the solver SeduMi. Finally, simulation results are carried out with the SimMechanics to demonstrate the effectiveness of the proposed approach.

In a multiarm space robotic system, one or more manipulators can be used to stabilize the base through counteracting the disturbance caused by other manipulators performing on-orbital tasks. However, singularities are inevitably present in the traditional methods based on differential kinematics solutions. In this paper, we propose a singularity-free trajectory planning method to simultaneously keep the attitude and centroid position of the base stabilized in inertial space; the balance arms are also designed. First, we derive the coupling motion equations of a free-floating multiarm space robotic system. Then, the singularity problems are theoretically analyzed, and the theoretical basis for singularity-free trajectory planning is established. Second, we decompose the six degrees of freedom pose (attitude and position) stabilization problem into two 3DOF subproblems related to attitude and position balancing. We then design two robotic arms: 1) a position balance arm and 2) an attitude balance arm, to maintain the base centroid position and attitude, respectively. Third, we plan the coordinated trajectories of the two balance arms according to holonomic and nonholonomic constraints. As long as the desired motion is not beyond its balance ability, the reasonable joint variables can always be determined without encountering a singularity problem. Finally, the proposed methods are verified using simulations of typical on-orbital missions, including joint trajectory tracking and target capturing.

This paper proposes a robust fault detection and isolation (FDI) approach that combines active and passive robust FDI approaches. Standard active FDI approaches obtain robustness by using the unknown input observer (UIO) to decouple unknown inputs from residuals. Differently, standard passive FDI approaches achieve robustness by using the set theory to bound the effect of uncertain factors (disturbances and noises). In this paper, we combine the UIO-based and the set-based approaches to produce a mixed robust FDI, which can mitigate the disadvantages and exert the advantages of the two robust FDI approaches. In order to emphasize the role of set theory, the UIO design based on the set theory is named as the set-theoretic UIO (SUIO). A quadrotor subsystem is used to illustrate the effectiveness of the proposed FDI approach.

Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.

In far range proximity of GEO on-orbit service, a space robot cannot reach desired position exactly by using two impulses C-W guidance and control law. To overcome this problem, a multiple impulses C-W guidance and control law with mid-correction is proposed. The guidance problem is transformed to nonlinear programming with constraints according to the error of final position. The fuel optimal solution is gotten by Particle Swarm Optimization algorithm with constraints. Firstly, the model of multiple impulses guidance is produced based on C-W law. Secondly, the detail of fuel optimal solution algorithm is presented by using PSO. Thirdly, numerical simulations are studied to verify the PSO algorithm of multiple impulses guidance under different conditions. The results show that this method is feasible and effective.

In final approach of rendezvous between a space robot and a non-cooperative target, due to the light and the folds of heat cladding materials on the target surface, the edge of target cannot be accurately extracted by classic Canny algorithm and we are unable to complete measurement of relative position and attitude. To solve this problem, the measurement method of relative position and attitude between two non-cooperative spacecrafts based on graph cut and edge information algorithm is proposed. A circular feature of the target is chosen as the recognition and measurement object. Firstly, the edge of the circular feature on the target is accurately extracted by graph cut and edge information algorithm. Secondly, the edges are ellipse fitting. Lastly, the relative position and attitude of target is obtained by fitting ellipse parameters of binocular cameras. The simulation results show that the precision of this method is better than Canny algorithm's and it can better meet the mission requirements.

Robotic systems are expected to play an increasingly important role in future space activities with the development of space technology. The robotic on-orbital service, whose key is the capturing technology, becomes research hot in recent years. This paper focuses on the guidance of a robot manipulator to capture a spinning satellite with unknown dynamics parameters. In capturing a spinning satellite, a reference trajectory for control of the manipulator is generated with time delay due to the processing time of the target motion estimator and the manipulator controller. Consequently, the control system shows a poor performance and the end-effector sometimes fails to capture the target satellite. To solve this problem, the motion characteristics and motion prediction of the spinning satellite is analyzed Firstly, and using Unscented Kaiman Filter (UKF) to predict its movement. Then, a method of autonomous path planning of a free-floating space robot for target capturing is proposed, which is based on motion prediction and speed compensation. Finally, a ground experiment system is set up based on the concept of dynamic emulation and kinematic equivalence. With the experiment system, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm.

Autonomous target capturing is the key for space robot to perform on-orbital servicing tasks. To meet the requirement of complex and long-term task, large flexible appendages, such as solar paddles and antenna reflectors are usually mounted on the base of a space robot. Due to the structure vibration, it is very challenging to capture a free-floating target satellite. In this paper, we derived the kinematics equations and proposed the autonomous target capturing method for free-floating flexible-base space robots. The kinematics equation established the mapping from the base velocities, joint rates and elastic motion to the end-effector velocities. Based on this equation, we designed resolved motion rate control with vibration compensation for the space manipulator. Another contribution of this paper is that we modeled the dynamic coupling between the rigid movement of the end-effector and the flexible vibration of the solar paddles. Based on this model, we analyzed the coupling effect which was very important for the design of the manipulator and determining the trajectory planning and control strategy. At last, a simulation system was created and simulation studies of the proposed methods were carried out. The simulation results verify the proposed methods.

A path-following method for fixed-wing unmanned aerial vehicles (UAVs) is presented in this paper. This method consists of an outer guidance loop and an inner control loop. The guidance law relies on the idea of tracking a virtual target. The motion of the virtual target is explicitly specified. The main advantage of this guidance law is that it considers the maneuvering ability of the aircraft. The aircraft can asymptotically approach the defined path with smooth movements. Meanwhile, the aircraft can anticipate the upcoming transition of the flight path. Moreover, the inner adaptive flight control loop based on attractive manifolds can follow the command generated by the outer guidance loop. This adaptive control law introduces a first-order filter to avoid solving the partial differential equation in the immersion and invariance adaptive control. The performance of the proposed path-following method is validated by the numerical simulation.

Autonomous rendezvous in close range requires adequate ground simulations due to its significant difficulties and risks. In this paper, a novel semi-physical simulation system for binocular vision guided rendezvous is established. In this system, virtual three-dimensional models of the spacecrafts and the scene are created using computer graphic technology. Accordingly, images of the binocular cameras on board chaser (servicer) spacecraft are generated and displayed on the liquid crystal displays (LCDs). As the physical component in the simulation loop, two industrial cameras photograph the virtual images on the LCDs so that real camera noise is involved. In order to perform the closed-loop simulation, image acquisition, image processing, pose measurement, chaser guidance, navigation and control, and the system's dynamic motion are conducted. Through the combination of “virtual environment” and “physical environment”, the simulation system can successfully demonstrate binocular vision guided rendezvous. Simulation data is capable to verify the key algorithms during close range rendezvous. Changing the object model and dynamic model, this system can be applied to other vision-related researches.

Robotic systems are expected to play an increasingly important role in future space activities with the development of space technology. One broad area of application is in the servicing, construction, and maintenance of satellites and large space structures in orbit. Fine manipulation technology is very important for space robot to perform there tasks, since it must ensure safe and reliable interaction with objects or environment. In order to assure the task is accomplished successfully, ground experimentations are required for verifying key planning and control algorithms before the space robot is launched. In this paper, based on the concept of a hybrid approach combining the mathematical model with the physical model, a ground experiment system is set up, which is composed of two industrial robots, global and hand-eye visual equipments, six-axis force/momentum sensors, guide rail and four computers. Many control approaches of fine manipulation, such as compliance control, impedance control, hybrid force/position control, intelligent control, and so on, can be verified using this system. As an example, contour curves tracking experiment based on compliance control strategy is performed. Experiment results show that the ground system is very useful for verifying dexterous manipulation technology of space robot.

Space robotic system is expected to play an increasingly important role in repairing GEO (geostationary orbit) satellites in the future. To perform the servicing mission, the robotic system is firstly required to approach and dock with the target autonomously, for which the measurement of relative pose is the key. It is a challenging task since the existing GEO satellites are generally non-cooperative, i.e. no artificial mark is mounted to aid the measurement. In this paper, a method based on binocular stereo vision is proposed to estimate the pose of a GEO satellite in the final approach phase. It directly takes the natural circular feature on the GEO satellite as the recognized object. Correspondingly, an image processing and pose measurement algorithm is presented to determine the relative position and orientation of the target. This algorithm provides a closed-form solution using simple mathematics, therefore, it is suitable to space applications where the computation capability of the on-board processor is very limited. In addition, it effectively solves the orientation-duality problem for circular feature, requiring neither specific motions of the camera nor a priori knowledge about the radius of the circle. Computer simulations verify the proposed method.

Due to failure of mechanisms to deploy, some large communication satellites lost their ability and resulted in huge economic cost. A space robotic system is expected to perform the on-orbit repairing mission. It is a tremendous challenge to navigate a space robot in final approach since the targets are generally non-cooperative. Rectangle features, which are common in the configuration of a satellite, can be chosen as the recognized objects. However, these characters are very large. Limited by the FOV (field of view), a monocular camera can not supply enough information of the rectangles. In this paper, a method based on monocular camera is proposed to determine the attitude of a large non-cooperative target using a partial rectangle. Firstly, the relationship of a rectangle and circular points by camera is derived and fused. Secondly, the attitude measurement algorithm is acquired from the constraint and a virtual rectangle is reconstructed. Lastly, the algorithm is verified by mathematic simulations which are very close to reality. The results show the validity and flexibility of the proposed method.

The failures of GEO (Geostationary Orbit) spacecrafts will result in large economic cost and other bad impacts. In this paper, we propose a space robotic servicing concept, and present the design of the corresponding system. The system consists of a 7-DOF redundant manipulator, a 2-DOF docking mechanism, a set of stereo vision and general subsystems of a spacecraft platform. This system can serve most existing GEO satellites, not requiring specially designed objects for grappling and measuring on the target. The serving tasks include: (a) visual inspecting; (b) target tracking, approaching and docking; (c) ORUs (Orbital Replacement Units) replacement; (d) un-deployed mechanism deploying; (e) extending satellites lifespan by replacing its own controller. As an example, the servicing mission of a malfunctioned GEO satellite with three severe mechanical failures is presented and simulated. The results show the validity and flexibility of the proposed system.

Space target may move in various modes, such as free-floating, tumbling, and so on. In order to capture a space target, autonomous trajectory planning algorithm is studied. The base of the space robot is free-floating for the purpose of safety and saving the fuel. Firstly, the target feature is extracted based on the measured information via the hand-eye camera. Then the target pose (position and orientation) and velocity (linear velocity and angular velocity) relative to the end-effector are estimated using Kalman filtering technology. Thirdly, an autonomous trajectory planning approach is proposed for capturing an space target with unknown motion. Lastly, a semi-physical simulation system is established to verify the planning algorithm. The simulation results show that the algorithm is valid

The Chinese experimental space system for on-orbit robotistic services (CESSORS) is to be developed by Shenzhen Space Technology Center. CESSORS consists of two satellites and a mounted robotic manipulator. In this paper, the design of the two satellites and the manipulator is described in detail, as well as the target detecting system and the ground teleoperation system. The planned missions include calibration of robot manipulator, teleoperation of the manipulator, coordinated control, on-orbit robotistic services, on-orbit target chasing and approaching, and flying around inspection. This paper introduces the components of CESSORS, and shows the on-orbit missions

In this paper, a novel testing method for on-ground test of space manipulator is introduced. The configurations of the testbed and the manipulator are described in detail. To accomplish the test of three-dimensional movement, a modified testbed using air-bearings is designed. By adopting the two-step testing method, which is presented in this paper, the three-dimensional positioning accuracy and the three-dimensional attitude accuracy of the space manipulator can be measured. The main idea of this method is to divide a three-dimensional movement into two planar movements, which can be performed on the testbed. The equations of data processing are deduced. The analysis shows that the mentioned testing method is practical for the research of space manipulators

In this article, we discuss the dynamics characteristics of contact and impact when the hand of space manipulator captures the free-flying target (FFT). We establish the dynamics model of contact and impact between a space manipulator and FFT. The pre-impact, post-impact effect and condition of the space robot system and the FFT system are analyzed when there are any differences between the speed of the end-effector of the space manipulator and that of the contact and impact point on the surface of FFT. We present the relationship between the speed varieties of the space base and that of the FFT. If the impact force is kept constant, the speed varieties of the space base are different when the space robot system is at different configuration. Those methods can be used to analyze the contact and impact problem of the space robot.

We are developing a novel robot concept called the wearable robot. Wearable robots are mobile information devices capable of supporting remote communication and intelligent interaction between networked entities. In this paper, we explore the possible functions of such a robotic network and will present a distributed network architecture based on service components. In order to support the interaction and communication between the components in the wearable robot system, we have developed an intelligent network architecture. This service-based architecture involves three major mechanisms. The first mechanism involves the use of a task coordinator service such that the execution of the services can be managed using a priority queue. The second mechanism enables the system to automatically push the required service proxy to the client intelligently based on certain system-related conditions. In the third mechanism, we allow the system to automatically deliver services based on contextual information. Using a fuzzy-logic-based decision making system, the matching service can determine whether the service should be automatically delivered utilizing the information provided by the service, client, lookup service, and context sensors. An application scenario has been implemented to demonstrate the feasibility of this distributed service-based robot architecture. The architecture is implemented as extensions to the Jini network model.

In this paper, we discuss the problem of how a free-floating space manipulator (SM) can be mapped to a conventional, fixed-base manipulator which preserves its dynamic and kinematic properties, and thus is called dynamically equivalent manipulator (DEM). The DEM concept allows one to use a conventional manipulator system to simulate a free-floating space manipulator connected to a space station, spacecraft, or satellite, without complicated experimental set-ups. This paper presents the theoretical development of the DEM concept, and demonstrates its dynamic and kinematic equivalence to the SM.

We propose the concept of the dynamically equivalent manipulator (DEM) of a free-floating space manipulator (SM) system. The dynamically equivalent manipulator can be physically built and used as an experimental testbed for the study of the dynamic performance and task execution of space robots. As it is a fixed-base manipulator, there is no need to resort to complex mechanisms to simulate the space environment. In this paper, we discuss two important issues associated with the DEM concept. First, we demonstrate the property of conservation of angular momentum and verify the validity of the DEM under free-flying conditions (i.e., when the SM base attitude is controlled via reaction wheels). Next, we investigate the effect of model uncertainty in the space manipulator and how it maps as errors in the parameters of the DEM. We derive explicit expressions for the error mapping and present a case study.