This letter proposes a novel scheme for resilient estimation under random sensor failures and colored measurement noise, without requiring hardware-redundant designs or complex adaptive mechanisms. The key insight is to leverage prior constraints to increase information redundancy. Resilience against both disturbances is achieved through a Kalman-Petovello filter, which is derived by whitening the colored noise via measurement differencing and incorporating the prior constraints as pseudo measurements. Theoretical analysis and numerical simulations demonstrate the efficacy of the proposed scheme.

This paper proposes a fractional-order control method for the physical human-robot interaction (pHRI) inspired by the dynamics of muscle contraction-relaxation behavior, which is integrated into a fractional-order sliding mode with variable order and parameters, to achieve fast transient response while guaranteeing high-precision steady-state tracking performance. A sensorless force observer based on fractional calculus is developed to estimate the operator’s behavior, enabling a composite control system that guarantees ultimate boundedness of the closed-loop signals. The deep reinforcement learning-based order and parameter optimization mechanism is synthesized into the unified architecture of the composite control system. The effectiveness of the proposed method is validated through numerical simulations and comparative studies, which demonstrate significant improvements in convergence speed on the sliding manifold while maintaining steady-state precision. Experimental results further confirm the feasibility and practical applicability of the framework in a cylindrical docking scenario via visualreality fusion approach, highlighting its potential for future semiautonomous human-in-loop missions.

This article investigates the collaborative transportation planning and control of tethered multi-rotor unmanned aerial vehicles within intelligent transportation systems. These unmanned aerial vehicles handle heavy-load delivery, including emergency airdrop and aerial assembly of structural components. To ensure algorithm generality, obstacles, loads, and unmanned aerial vehicles are modeled as unions of convex sets. Collision avoidance constraints, originally nondifferentiable due to convex set distances, are exactly reformulated into differentiable forms via strong duality. This leads to a smooth, optimization-based trajectory planning framework with obstacle avoidance. Considering composite disturbances, a robust nonlinear distributed model predictive control strategy based on constraint tightening is developed, ensuring robust feasibility and stability without terminal constraints. Numerical simulations in cluttered environments validate the method’s effectiveness and applicability to next-generation aerial logistics and emergency response in complex terrains.

Motivated by the practical challenges of tethered satellite formations (TSFs), this article focuses on the state estimation problem for systems with limited payload capacity, subject to probabilistic sensor failures and complex soft constraints. As prior knowledge, soft constraints reveal the interdependence of internal states, providing insights into observability preservation under probabilistic sensor failures. Unlike existing approaches, we propose a soft-constrained estimation scheme that fully leverages prior constraint knowledge to preserve observability and enhance performance via a constrained particle filter (CPF). Within the Bayesian framework, the CPF fully leverages the soft constraints to truncate both the prior and posterior distributions. The convergence analysis is also presented. Based on this scheme, we investigate the maximum tolerable sensor failures for TSF. Surprisingly, it is proven thatn-body TSF ($n \ge 3$) with typical configurations can tolerate up ton-1 positioning sensor failures. This proof enables mission designers to sustain system observability even with up ton-1 sensor failures, thereby obviating redundant configurations while ensuring orbital mission reliability. Extensive simulations validate the effectiveness of the proposed scheme and its filter performance.

Autonomous fine manipulation in space for orbital assembly continues to present a critical challenge in the field of aerospace engineering. Under low-gravity conditions, during satellite manipulator operations on free-floating objects, the absence of significant gravitational forces and friction constraints leads to unpredictable relative motions between the manipulator’s end-effectors and objects, degrading the manipulation performance. This study proposes a fine manipulation method for satellite robots with floating platforms, grounded in multimodal perception and an enhanced Action Chunking with Transformer (ACT) architecture that enables multimodal state interaction. By integrating visual, tactile, and depth sensory data, the satellite robot’s space fine manipulation capabilities are substantially improved. From the experimental results generated from simulated environments, the proposed method achieves a success rate exceeding 80% for peg-in-socket insertion tasks, outperforming conventional approaches with a success rate of approximately 45%. Project Website: https://github.com/LSY0528/VDTF-ACT.

Developing general robotic systems capable of manipulating in unstructured environments is a significant challenge, particularly as the tasks involved are typically long-horizon and rich-contact, requiring efficient skill transfer across different task scenarios. To address these challenges, we propose knowledge graph-based skill library construction method. This method hierarchically organizes manipulation knowledge using "task graph" and "scene graph" to represent task-specific and scene-specific information, respectively. Additionally, we introduce "state graph" to facilitate the interaction between high-level task planning and low-level scene information. Building upon this foundation, we further propose a novel hierarchical skill transfer framework based on the skill library and tactile representation, which integrates high-level reasoning for skill transfer and low-level precision for execution. At the task level, we utilize large language models (LLMs) and combine contextual learning with a four-stage chain-of-thought prompting paradigm to achieve subtask sequence transfer. At the motion level, we develop an adaptive trajectory transfer method based on the skill library and the heuristic path planning algorithm. At the physical level, we propose an adaptive contour extraction and posture perception method based on tactile representation. This method dynamically acquires high-precision contour and posture information from visual-tactile images, adjusting parameters such as contact position and posture to ensure the effectiveness of transferred skills in new environments. Experiments demonstrate the skill transfer and adaptability capabilities of the proposed methods across different task scenarios. Project website: https://github.com/MingchaoQi/skill_transfer

A novel efficient reinforcement learning paradigm combining human knowledge, model-based and model-free methods is presented for optimal robot manipulation control during complex multi-phase robot manipulation tasks, e.g., the peg-in-hole tasks with tight fit and nut-and-bolt assembly. Firstly, human demonstration is conducted to collect the data during successful robot manipulation, and manipulation phase estimation method integrating with human knowledge is presented to obtain the higher-level planning of the multi-phase robot manipulation tasks. Typical robot manipulation tasks can usually be decomposed into three types of phases, namely free motion, discontinuous contact, and continuous contact. For phase with free motion, the motion planning method is utilized for generating smooth trajectory. For phase with discontinuous contact in the axes of interest during the pre-manipulation process, the rule-based model-free method, namely the Policy Gradients with Human-Guided Parameter-based Exploration (PGHGPE) method is utilized. For the manipulation phase with continuous contacts, the model-based method is utilized because of its higher sample efficiency. Finally, the simulation and experimental studies verify the effectiveness of the presented algorithm. Note to Practitioners—The important premise for the future robot assistants is that the robots should have certain ability of complex manipulation skill learning. Complex manipulation tasks can be decomposed into multiple stages, and HRL is a suitable method for solving this kind of problems. However, HRL faces the challenge of low computational efficiency. To this end, efficient manipulation skill learning for complex manipulation tasks via human knowledge, model-based and model-free reinforcement learning methods are presented, which improves the efficiency of the skill learning process to a practical level.

This paper investigates the fixed-time attitude tracking control problem for rigid spacecraft subject to external disturbance and output constraints. First, the state transformed function (STF) technique is employed to convert the constrained spacecraft error dynamics into an unconstrained one. Subsequently, a fixed-time disturbance observer (FXTDO) is designed to estimate and reconstruct the lumped disturbance of unconstrained system. Combined with the developed STF, FXTDO and fixed-time integral terminal sliding mode (FITSM) surface techniques, the proposed fixed-time control law provides zero-error attitude tracking with high control precision and chattering avoidance, while the output constraints are never transgressed. The fixed-time stability of the closed-loop system is conducted via the Lyapunov technique and bi-limit homogeneity theory, and the expression of convergence time is also presented. Simulations illustrate the efficiency of the investigated controller.

As robots grow increasingly intelligent and utilize data from various sensors, relying solely on unimodal data sources is becoming inadequate for their operational needs. Consequently, integrating multimodal data has emerged as a critical area of focus. However, the effective combination of different data modalities poses a considerable challenge, especially in complex and dynamic settings where accurate object recognition and manipulation are essential. In this paper, we introduce a novel framework integrating with Multimodal Information for Grasping Synthesis with vision-language models (MIG) designed to improve robotic grasping capabilities. This framework incorporates visual data, textual information, and human-derived prior knowledge. We start by creating target object masks based on this prior knowledge, which are then used to segregate the target objects from their surroundings in the image. Subsequently, we employ language cues to refine the visual representations of these objects. Finally, our system executes precise grasping actions using visual and textual data synthesis, thus facilitating more effective and contextually aware robotic grasping. We carry out experiments using the OCID-VLG dataset. We observe that our methodology surpasses current state-of-the-art (SOTA) techniques, delivering improvements of 9.91% and 5.70% for top-1 and top-5 predictions in grasp accuracy. Moreover, when apply to the reconstructed Grasp-MultiObject dataset, our approach demonstrates even more substantial enhancements, achieving gains of 17.63% and 22.76% over SOTA methods for top-1 and top-5 predictions, respectively. Note to Practitioners—As robotic systems evolve, the challenge of enabling them to function effectively in complex environments has become increasingly apparent. This paper introduces a solution that integrates multiple sources of data—visual, textual, and human knowledge—to enhance robotic grasping capabilities. The practical problems addressed include the limitations of current unimodal systems that struggle with accurate object recognition and manipulation in dynamic settings, such as warehouses or assembly lines. Our framework, MIG, demonstrates significant improvements in grasp accuracy, making it suitable for tasks where precision is critical. While our results show promise, particularly in controlled experiments, there are limitations to consider. The framework’s performance may vary in unstructured real-world environments due to factors like occlusion or varying lighting conditions. Future work should focus on refining the system for real-time application and exploring additional sensory inputs to enhance robustness. By addressing these challenges, we aim to make this approach more applicable across industries, paving the way for smarter, more adaptable robotic solutions in everyday tasks.

This work focuses on the safety of learning-based control for unknown nonlinear system, considering the stability of learned dynamics and modeling mismatch between the learned dynamics and the true one. A learning-based scheme imposing the stability constraint is proposed in this work for modeling and stable control of unknown nonlinear system. Specifically, a linear representation of unknown nonlinear dynamics is established using the Koopman theory. Then, a deep learning approach is utilized to approximate embedding functions of Koopman operator for unknown system. For the safe manipulation of proposed scheme in the real-world applications, a stable constraint of learned dynamics and Lipschitz constraint of embedding functions are considered for learning a stable model for prediction and control. Moreover, a robust predictive control scheme is adopted to eliminate the effect of modeling mismatch between the learned dynamics and the true one, such that the stabilization of unknown nonlinear system is achieved. Finally, the effectiveness of proposed scheme is demonstrated on the tethered space robot (TSR) with unknown nonlinear dynamics.

Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. While numerous methods exist for producing such estimations, most boil down to solving a synchronous discrete-time fusion problem. However, the asynchronous nature of event cameras and their unique fusion mechanism with inertial sensors remain underexplored. In this article, we introduce a monocular event-inertial odometry method called asynchronous event-inertial odometry (AsynEIO), designed to fuse asynchronous event and inertial data within a unified Gaussian process (GP) regression framework. Our approach incorporates an event-driven front-end that tracks feature trajectories directly from raw event streams at a high temporal resolution. These tracked feature trajectories, along with various inertial factors, are integrated into the same GP regression framework to enable asynchronous fusion. With deriving analytical residual Jacobians and noise models, our method constructs a factor graph that is iteratively optimized and pruned using a sliding-window optimizer. Comparative assessments highlight the performance of different inertial fusion strategies, suggesting optimal choices for varying conditions. Experimental results on both public datasets and our own event-inertial sequences indicate that AsynEIO outperforms existing methods, especially in high-speed and low-illumination scenarios.

This article introduces a reactive methodology tailored for a wide range of practical graph-based path planning applications. In these scenarios, a robot with limited sensor capabilities traverses an undirected graph to optimize metrics related to task duration. This article formalizes these challenges as graph-based persistent path planning problems with temporal logical constraints and proposes a comprehensive persistence planning framework. A novel universal algorithm with quadratic time complexity is designed, striking an optimal balance between accuracy and computational efficiency by establishing a new decision space. Theoretical analysis verifies the algorithm’s convergence and generality, especially for patrol, persistent surveillance, and watchman routing tasks. Moreover, the proposed algorithm is evaluated across various simulation scenarios, demonstrating its effectiveness in addressing complex path planning challenges.

This article presents a passivity-based shared control for a multirobot teleoperation system to perform extravehicular assembly tasks. At the remote site, an eye-in-hand camera is integrated with three manipulators that cooperatively drive a customized tool. A haptic device at the local site allows a human operator to intervene when necessary via bilateral teleoperation. To enable intuitive user control, a homography-based method seamlessly integrates visual servoing and operator input to guide the remote camera. To accommodate different peg geometries and enable adaptive tool actuation, a partial pose synchronization method decouples roll from the other pose dimensions. It allows each manipulator to independently actuate a gripper by rotating its end-effector frame around the roll axis, without interfering with cooperative motion in position, pitch, and yaw. In addition, to minimize undesired internal forces, we formulate and solve a passivity-constrained interaction wrench optimization problem that enhances stability. The proposed control ensures smooth transitions between autonomous and teleoperated modes, supporting flexible human intervention. Theoretical analysis and experimental validation confirm the system’s stability and effectiveness in achieving precise, robust, and responsive multirobot teleoperation for complex space assembly tasks.

This paper researches a novel global terminal sliding mode control(GTSMC) on a tethered satellites system(TSS) under outer disturbances, and the effect of PI/PD compensation in restraining chattering on sliding surface is appended. By taking advantage of the finite-time convergence of traditional terminal sliding surface, the sliding surface with global and terminal sliding motion is proposed, and the convergent time by GTSMC is qualitatively evaluated by the sliding surface. Then the integral/derivative function of the low-pass filtered switching control is appended in GTSMC. By virtue of the accuracy of integral and the damping of derivative, respectively, the persisting on sliding surface is eliminated, such that the chattering effect of the controlled system on the surface is restrained consequently. Finally, simulations of the proposed control on TSS is shown to validate the theoretical analyses.

Recent works have combined monocular event camera and inertial measurement unit to estimate the SE(3) trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

Robotic manipulation holds the potential to replace humans in the execution of tedious or dangerous tasks. However, control-based approaches are not suitable due to the difficulty of formally describing open-world manipulation in reality, and the inefficiency of existing learning methods. Therefore, applying manipulation in a wide range of scenarios presents significant challenges. In this study, we propose a novel framework for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (TactileAIRL), aimed at achieving efficient learning. To enhance the performance of reinforcement learning (RL), we introduce active inference, which integrates model-based techniques and intrinsic curiosity into the RL process. This integration improves the algorithm’s training efficiency and adaptability to sparse rewards. Additionally, we have designed universal tactile static and dynamic features based on vision-based tactile sensors, making our framework scalable to many manipulation tasks learning involving tactile feedback. Simulation results demonstrate that our method achieves significantly high training efficiency in objects pushing tasks. It enables agents to excel in both dense and sparse reward tasks with just few interaction episodes, surpassing the SAC baseline. Furthermore, we conduct physical experiments on a gripper screwing task using our method, which showcases the algorithm’s rapid learning capability and its potential for practical applications.

This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy maintenance. Reasonable and efficient on-orbit self-reconfiguration algorithms play a crucial role in realizing the benefits of distributed spacecraft. This paper adopts the framework of imitation learning combined with reinforcement learning for strategy learning of module handling order. A robot arm motion algorithm is then designed to execute the handling sequence. We achieve the self-reconfiguration handling task by creating a map on the surface of the module, completing the path point planning of the robotic arm using A*. The joint planning of the robotic arm is then accomplished through forward and reverse kinematics. Finally, the results are presented in Unity3D.

This paper proposes a novel hierarchical reinforcement learning (HRL) framework of complex manipulation tasks which integrates the human prior knowledge. The framework involves the following steps:$ \textbf{1)} $The manipulation process is divided into several stages based on human prior knowledge.$ \textbf{2)} $Transition conditions between stages are determined in the form of “if-then” rules.$ \textbf{3)} $The key features of each stage are selected, and the corresponding control policies are designed via human knowledge.$ \textbf{4)} $The policy gradient with parameter-based exploration (PGPE) method is employed to optimize the policy parameters because it does not require the policy to be derivable for the parameters. To increase the convergence speed of this framework, importance sampling and adaptive adjustment of exploration variance are employed to improve it.$ \textbf{5)} $On this basis, to facilitate the transfer of simulation results to practical experiments, half sim-to-real method is presented, which fully utilizes the simulation results, and the differences between simulation and experimental environments are considered. Simulation and experimental studies show that our framework can deal with the peg-hole-insertion task with a high quality in less than 1600 episodes and can safely adapt the skill into the practical scene with little iterations, which verify the efficiency of the presented method.Note to Practitioners—Intelligent robots will become the right assistants of human beings in the future, especially in various areas of complex manipulation occasions. The important premise is that the robots should have certain ability of complex manipulation skill learning. Complex manipulation tasks can be decomposed into multiple stages, and HRL is a suitable and efficient method for solving this kind of problems. This paper proposes a novel HRL framework which can better integrate the human prior knowledge. In addition, improved PGPE method is proposed to obtain the optimized policy parameters more quickly. More importantly, a novel half sim-to-real transfer method is presented to better integrate the simulation and experiment results. This provides a common paradigm, which leverages the simulation results to reduce the interaction between robot and practical environment and then utilizes the experiment results to further optimize the policy parameters.

This study investigates the formation planning problem of tethered multirotor unmanned aerial vehicle (UAV) cooperative transportation with unknown payload and cable length. Normally, the transportation formation and trajectory are given in advance or designed based on the coupled system model. It is challenging to dynamically generate flexible formations in response to changing environments when the payload and cable length are unknown. This paper proposes an online formation planning method for multirotor UAVs. First, by analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. Then, desired trajectories/formations that can reduce the cost functions are generated by using the admittance model. Next, an estimation-based formation tracking control is designed, which ensures that multirotor UAVs follow the desired trajectories/formations. Finally, numerical simulations and experiments are conducted to demonstrate the effectiveness of the proposed method.Note to Practitioners—This paper is motivated by the formation planning problem of tethered multirotor UAV cooperative transportation. In industry and production applications, a team of multirotor UAVs has a larger load capacity than a single one. Nevertheless, the formation planning of the tethered cooperative transportation is challenging, especially when the payload and cable length are unknown. Rather than give a predefined formation or trajectory, this paper suggests an online formation planning method for multirotor UAVs in case of unknown payload and cable length. The method is implemented through the following three parts: 1) By analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. 2) By using the admittance model, we generate desired trajectories/formations that can minimize the proposed cost function. 3) By estimating cable tension, we design formation tracking control laws for multirotor UAVs to follow the desired trajectories/formations. The proposed formation planning method does not rely on the knowledge of the payload and length of cables, which makes it can be easily applied to extensive industry, production, and military practice. Finally, numerical simulations and experiments are conducted to demonstrate the feasibility of the proposed method.

The structural complexity, material diversity, and defect concealment in industrial detection scenes pose challenges of robustness, multi-information, and effectiveness to optical imaging systems. Partially blurred images due to the limited depth of field (DoF) of industrial imaging systems, shadow occlusions due to simple illumination conditions, and material and texture interference due to multiple compositions have become key issues affecting imaging quality in complex scenes. This paper proposes a systematic scheme fusing the DoF expansion approach, light source optimization, and polarization information (DLP-Fusion) to comprehensively improve imaging quality. Herein, a DoF fusion algorithm and a liquid zoom lens are used to increase the DoF from 2.5 mm to 40 mm. Moreover, a combination of ring light and freely rotatable strip light sources is introduced to improve the uniformity and robustness of the illumination, resulting in an average enhancement of 56.46% in the contrast of the target features. Furthermore, a polarization selection fusion network (PSFNet) is constructed to achieve flare suppression and complex material characterization, with the image naturalness improving by 32.05%. The experimental results with diverse scenes demonstrate that DLP-Fusion considerably improves the DoF range, image uniformity, and target feature contrast. DLP-Fusion exhibits remarkable robustness in various environments and was seamlessly deployed in real-world industrial settings with good performance. This paradigm may open a path toward intelligent imaging systems for sophisticated applications, including multimaterial detection and target recognition under harsh conditions.

In this article, a novel stochastic optimal control method is developed for robot manipulator interacting with a time-varying uncertain environment. The unknown environment model is described as a nonlinear system with time-varying parameters as well as stochastic information, which is learned via the Gaussian process regression (GPR) method as the external dynamics. Integrating the learned external dynamics as well as the stochastic uncertainties, the complete interaction system dynamics are obtained. Then the iterative linear quadratic Gaussian with learned external dynamics (ILQG-LEDs) method is presented to obtain the optimal manipulation control parameters, namely, the feedforward force, the reference trajectory, as well as the impedance parameters, subject to time-varying environment dynamics. The comparative simulation studies verify the advantages of the presented method, and the experimental studies of the peg-hole-insertion task prove that this method can deal with complex manipulation tasks.

In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.

This paper addresses the problem of road network cooperative persistent surveillance algorithm suitable for online planning. A diffusion-based reactive cooperative path planing approach (DRCP) is designed. Compared with existing related methods, DRCP uses reactive architecture, probability independent decision strategy, and implicit cooperation technique, which achieve the fast computation, stable result, and accurate response. The characteristics of DRCP is to create a new exclusive cognition model for each unmanned ground vehicle (UGV), which allows each UGV to refer to only one virtual variable to achieve: 1) Global state estimation based on a limited cognition space. 2) Avoidance of local optimum. 3) Global cooperation with coordinators. Theoretical analysis proves that DRCP may quickly plan paths for a UGV group with any sensor models meeting specific conditions. Numerical simulation shows the accuracy and stability of DRCP. The robustness to unstructured disturbances and rapid response demonstrated in outdoor experiment indicate the ability/potential of the approach for online planning.

Most available grasp detection methods tend to directly predict grasp configurations with deep neural networks, where all features are equally extracted and utilized, leading to the relative restriction of truly useful grasping features. Inspired by the observed three-section structure pattern revealed by human-labeled graspable rectangles, we first design a structure prior attention (SPA) module which uses two-dimensional encoding to enhance the local patterns and utilizes self-attention mechanism to reallocate distribution of grasping-specific features. Then, the proposed SPA module is integrated with fundamental feature extraction modules and residual connection to achieve the implicit and explicit feature fusion, which further serves as the building block of our proposed Unet-like grasp detection network. It takes RGBD images as input and outputs image-size feature maps, from which the grasp configurations can be determined. Extensive comparative experiments on the five public datasets prove our method’s superiority to other approaches in detection accuracy, achieving 99.2%, 96.1%, 98.0%, 86.7%, and 92.6% on the Cornell, Jacquard, Clutter, VMRD, and GraspNet datasets. With visual evaluation metrics and user study, the quality maps generated by our method possess more concentrative distribution of high-confidence grasps and clearer discrimination with backgrounds. In addition, its effectiveness is also verified by robotic grasping under real-world scenario, leading to higher success rate.

This article considers the implementation of an observer-based logarithmic control scheme for physical human-robot interaction, which is a typical Lagrangian system. The novelty lies in using a switching term in the logarithmic sliding mode observer to describe the operator’s behavior without any sensors, and applying adaptive parameters in the logarithmic sliding mode controller (SMC) to practically stabilize reaching the sliding surface using chattering-free nonsingular reaching law in finite time. A reset mechanism is synthesized into the control system to enhance the transient response. The motion on the sliding surface is analyzed from the perspective of practical finite-time stability, from which both the convergence regions of the tracking and estimate errors can be determined. The numerical and experimental results verify the effectiveness and advantage of the proposed reset logarithmic SMC and observer for human-robot interaction compared to the existing linear SMC and terminal SMC. With regards to settling time and rising time, the superiority of transient performance in experimental results is coincident with the stability analysis.

In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system’s output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.

Nowadays the robot manipulation skills are usually learned by human demonstration via trajectory-level learning, which somewhat lacks robustness and generalization. In this paper, we propose a novel contact state level learning method for robot manipulation skill acquisition via human demonstration. The robot-environment contact states are described via environment dynamics modelling and geometric constraints modelling for flexible contact and rigid contact cases, respectively. During human demonstration process, the robot-environment interaction force, the robot position, and velocity data are collected. After that, the environment dynamics and geometric constraints modelling methods are presented to determine the contact state changes during the robot manipulation process. Then the robot manipulator learns the contact state information rather than specific manipulation trajectory. On this basis, the manipulation control law using active exploration method is presented to control the robot during the button pressing process and peg-hole-insertion process, respectively. Finally, the performance of the presented methodology has been verified via experimental studies. Note to Practitioners—Intelligent robots will become the right assistants of human beings in the future, especially in various areas of manipulation occasions. The important premise of realizing this vision is that the robots should have certain ability of manipulation skill learning. A lot of research has been carried out in this field, many of which are focusing on trajectory level manipulation skill learning and reproduction. Other than the trajectory level learning, human beings can learn many other higher levels of manipulation skills, such as the contact state level and semantic level learning, which makes the learning results more robust and general. In this paper, the contact state estimation and learning method via environment dynamics and geometric constraints modelling is presented to learn the robot manipulation skill based on the contact state transition conditions. In this way, the robot needs less data in the skill learning process, and the trajectory level learning is avoided. After learning the contact state level manipulation skill, the lower trajectory level command is autonomously generated. Experiments on button pressing and peg-hole-insertion tasks by KUKA iiwa robot have obtained very good results. Other than the button pressing and peg-hole-insertion tasks, the presented methodology can be applied to many other manipulation tasks, as long as there are contact state changes in the manipulation process. The work of this paper lays a foundation for the robot learning of higher-level manipulation skills.

This article proposes an adaptive neural-network control scheme for a rigid manipulator with input saturation, full-order state constraint, and unmodeled dynamics. An adaptive law is presented to reduce the adverse effect arising from input saturation based on a multiply operation solution, and the adaptive law is capable of converging to the specified ratio of the desired input to the saturation boundary while the closed-loop system stabilizes. The neural network is implemented to approximate the unmodeled dynamics. Moreover, the barrier Lyapunov function methodology is utilized to guarantee the assumption that the control system works to constrain the input and full-order states. It is proved that all states of the closed-loop system are uniformly ultimately bounded with the presented constraints under input saturation. Simulation results verify the stability analyses on input saturation and full-order state constraint, which are coincident with the preset boundaries.

The tethered formation system has been widely studied due to its extensive use in aerospace engineering, such as Earth observation, orbital location, and deep space exploration. The deployment of such a multitethered system is a problem because of the oscillations and complex formation maintenance caused by the space tether's elasticity and flexibility. In this article, a triangle tethered formation system is modeled, and an exact stable condition for the system's maintaining is carefully analyzed, which is given as the desired trajectories; then, a new control scheme is designed for its spinning deployment and stable maintenance. In the proposed scheme, a novel second-order sliding mode controller is given with a designed nonsingular sliding-variable. Based on the theoretical proof, the addressed sliding variable from the arbitrary initial condition can converge to the manifold in finite time, and then sliding to the equilibrium in finite time as well. The simulation results show that compared with classic second sliding-mode control, the proposed scheme can speed up the convergence of the states and sliding variables.

In a space telerobot system (STS), effectiveness of the control method in eliminating the time delay’s influences is advisable to be verified under the real circumstance. However, it is difficult and costly for many scholars to obtain confidential information that would allow them to establish an STS. It may be feasible, using some existing results, to model the time delay as close to reality as possible, and to then program a simulation system to generate the simulated time delay, thus verifying validity. In this article, time-delay modeling and simulation problems for relay communication-based STS are first studied. The time delay in relay communication-based STS consists of both processing and communication time delays; the latter is divided into ground and ground-space parts. By extending the available results, processing and ground communication time delays are modeled with the probability distribution function modeling approach. An optimal communication link identification and minimum time-delay realization (OCLIMTDR) method is proposed to model the ground-space communication time delay. In this method, the novel point–vector–sphere (PVS) algorithm serves to judge link connectivity. The PVS algorithm is based on geometric theory, which gives the OCLIMTDR method good extensibility and renders it suitable for any relay communication network in theory. All three parts of the time-delay models are integrated to form the loop time-delay model of the STS. Subsequently, a time-delay simulation system is created by programming the loop time-delay model. Finally, the correctness of the simulation system is further authenticated based on simulations and some prior knowledge.

This article addresses the stabilization control issue for the postcapture tethered system by tethered space robot (TSR). Due to the physical characteristics of space tether by nature, there exists no control inputs on the in-plane/out-of-plane channels of the tether; therefore, the postcapture tethered system is a typical multiinput and multioutput underactuated system. In this article, we propose an energy-based controller for the underactuated system subject to input saturation and the nonnegativity constraint of the tether tension. First, we give the dynamic model of the postcapture tethered system, with consideration of the three attitude angles of the postcapture combination, the in-plane/out-of-plane angles, and the tether length. Second, we list the analysis process of the system’s equilibrium points. Third, we give the detailed controller design process, and verify the stability of the system by invoking the Lyapunov techniques and the extended Barbalat’s lemma. Finally, numerical simulations and comparison results with the hierarchical sliding mode controller are conducted to validate the performance improvement of the developed control strategy.

Road Network Persistent Surveillance Problem (RPSP) involves path planning for an unmanned ground vehicle (UGV) with detection ability to timely detect the events randomly occurred. The road network is formed by edges and weighted viewpoints, where the UGV must move along the edges. The existing method based on cognitive architecture is inadequate in terms of real-time and effective decision making. To improve the computation efficiency and accuracy of solving RPSP, a new algorithm called Pheromone-Diffusion-based Conscientious Reactive Persistent Surveillance (PD-CRPS) is proposed in this paper. Considering the detection ability of UGV, the monitoring weight, the surveillance effect, and the topology of the road network, a model of pheromone release and diffusion is established to estimate the global uncertainty through the local information around the UGV. The local optimum avoidance technology based on pheromone is designed, and the reactive architecture is used to design the payoff function of decision making. The worst-case computational time complexity of the PD-CRPS is far less than the existing cognitive architecture method. Simulation results show that the PD-CRPS can not only efficiently plan the path of UGV in the road networks with different topologies and observation obstacles, but also improve the calculation accuracy.

This article proposes a novel modeling and path planning framework to tackle a new problem, named road-network persistent surveillance problem (RPSP), in which the occurrence location and probability of the events are unknown. To capture such events, an unmanned ground vehicle (UGV) with certain detection ability must move along the road-network with different monitoring priorities to persistently monitor them. First, we presented an event-oriented modeling method to formulate the new problem. The monitoring effect of the road-network is quantified by uncertainty, which takes the detection ability of the sensor, the detection interval, and the monitoring weight into account. Based on the proposed model, we then designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road-network without traversing them. The simulation results and analysis demonstrate the feasibility and superiority of the proposed approach.Note to Practitioners—This article was motivated by developing an effective solution for unmanned ground vehicle (UGV) to execute road-network persistent surveillance tasks under outdoor circumstances. To the best of our knowledge, there is no other consideration dealing with this problem. To capture spatiotemporal random events, we proposed a new approach that enables UGV to persistently monitor an area with road-network performing in the weighted mode. Distinguished from the traditional path planning methods in the literature, the proposed approach considered the detection ability of the UGV. Consequently, the so-called “area-to-area” planning approach does not require traversing all the viewpoints. The approach can be used in road-network with any topology. Moreover, the difficulty in modeling road-network from the actual environment is degraded, and compared with the existing patrolling methods, the monitoring efficiency of the proposed approach is improved obviously.

This article proposes a hybrid control scheme synthesizing fuzzy approximate Q-iteration algorithm and discrete-time terminal-like sliding mode control for deploying tethered space robot, which is modeled as a deterministic Markov decision process. The existence of a switching condition allows FQ-iteration algorithm and terminal-like sliding surface constituting an optimal sliding mode control, and the fuzzy logic approximation is employed to improve the efficiency of optimization. Under arbitrary switching, the sliding mode reaching law works to compress the contraction of sliding surface variable. Simulation results verify the analyses on contraction of fuzzy approximate Q-iteration for optimal sliding mode control, the stability of reduced-order system yielded by the proposed discrete-time terminal-like sliding surface, and existence of switching condition.

The use of maneuverable tethered space net robot (TSNR) is a promising solution for active space debris capture and removal due to its large envelop and easy capture method. However, the flexibility and elasticity of the underactuated net present a new challenge to the control scheme. In this article, a fuzzy-based sliding-mode control is proposed and applied to the TSNR. The main contribution is that an adaptive super-twisting sliding-mode control (ASTSMC) is investigated with a novel adaption law based on a fuzzy estimator, eliminating the need for a derivative of uncertainty. The key advantage of the proposed scheme is that the passive adaption law of traditional ASTSMC is improved to an active law, and complex oscillations can be directly estimated and suppressed. The dynamics equations of the TSNR are first derived, and the control problem of the system is specified. For the unmeasurable and boundary-unknown uncertainties, the adaptive fuzzy logic scheme is employed to approximate the complex uncertainties. Stability analysis and approximation convergence of the proposed control scheme are then verified via Lyapunov stability analysis. Finally, numerical simulations on the TSNR are provided to confirm the effectiveness and robustness of the proposed scheme.

This article focuses on the topic of fixed-time consensus for second-order multiagent systems (MASs) with disturbances. Based on an integration of the nominal control part and discontinuous integral sliding control part or continuous super-twisting-like control part, some control protocols are presented to achieve consensus tracking in the fixed time. A distributed integral sliding mode (ISM) and a fixed-time convergent command filter are introduced. The performance of nominal dynamics is dominated by the nominal control part which ensures the fixed-time convergence in the ISM surface. The discontinuous sliding mode or continuous super-twisting-like part related to the ISM is utilized to compensate disturbances within fixed convergence time. In this article, we have designed the continuous fixed-time consensus tracking controllers which can eliminate the chattering phenomenon and simultaneously guarantee the convergence precision. Independent of any initial state values, the restrictive bound of the convergence time is estimated. Some fair comparisons are performed to demonstrate the merits of the proposed strategies.

UAVs face many challenges in autonomous obstacle avoidance in large outdoor scenarios, specifically the long communication distance from ground stations. The computing power of onboard computers is limited, and the unknown obstacles cannot be accurately detected. In this paper, an autonomous obstacle avoidance scheme based on the fusion of millimeter wave radar and monocular camera is proposed. The visual detection is designed to detect unknown obstacles which is more robust than traditional algorithms. Then extended Kalman filter (EKF) data fusion is used to build exact real 3D coordinates of the obstacles. Finally, an efficient path planning algorithm is used to obtain the path to avoid obstacles. Based on the theoretical design, an experimental platform is built to verify the UAV autonomous obstacle avoidance scheme proposed in this paper. The experiment results show the proposed scheme cannot only detect different kinds of unknown obstacles, but can also take up very little computing resources to run on an onboard computer. The outdoor flight experiment shows the feasibility of the proposed scheme.

The postcapture of a partially failed spacecraft by space manipulators will bring a mutation in the dynamics of the combination. Both the inertia properties and the thruster configuration matrix will change significantly. The unknown dynamics of the partially failed spacecraft also cause a tremendous technical challenge for attitude takeover control. Accordingly, this paper describes a novel reconfigurable control system for postcapture attitude takeover of a partially failed spacecraft with parametric uncertainties, whose fuel has been exhausted or whose actuators have partial malfunctions. First, the reconfigurable control law is designed by command filtering adaptive back-stepping control to guarantee the system performance and global asymptotic stability considering inertia parametric uncertainties. Second, the thrusters are reconstituted, without changing the thruster physical configuration. Finally, the thrusters' forces can be redistributed by the dynamic control reallocation method based on constrained quadratic programing. Numerical simulations validate the feasibility of the proposed approach for postcapture attitude takeover control of a partially failed spacecraft with parametric uncertainties. Note to Practitioners-This paper presents a methodology for a partially failed spacecraft with parametric uncertainties. It focuses on solving the attitude takeover control of the partially failed spacecraft perfectly, while considering the position and speed constraints of the actuator and avoiding the plume impact to the spacecraft. The proposed command filtering adaptive back-stepping control can be used for spacecraft's thruster reconfiguration considering the parametric uncertainties. Furthermore, the dynamic control reallocation method is particularly useful for future applications that include spacecraft with redundant actuators.

In this paper, a novel fuzzy-observer-based hybrid force/position control method is investigated for a bimanual teleoperation system in the presence of dynamics uncertainties, random network-induced time delays, and multiple sampling rates of remote control signals and local measured data. The system structure consists of two pairs of position observers and contact force/torque estimators. The position observers are designed based on Takagi-Sugeno fuzzy inference rules to estimate the delayed remote state with low sampling rates. The force/torque estimators are designed for estimating the coupled item of uncertain dynamics and contact forces without acceleration information. By adding a compensatory item based on an auxiliary model, the force estimation and motion-tracking errors caused by varying dynamics uncertainties decrease, which is certified by two comparative force estimation techniques. The stability condition for the closed-loop system is also proved by the linear matrix inequality method based on the Lyapunov function. Finally, two simulations verify the effectiveness of the proposed method. The results indicate that the proposed method enables a better motion synchronization effect in soft-handling environment.

Facing the new challenges of the spacecraft developing, the concept of cellular space robot (CSR) for both space-craft system construction and on-orbit operation is presented in this paper. The system description and design principles are introduced to ensure the flexibility of the system. And dynamics model for takeover control is developed. After that, the regression models for the parameter identification are deduced based on the dynamics model. An interaction model identification algorithm is presented to solve the parameter identification problem for the distributed cells. Besides, the interactive model identification is validated by simulations. The simulations show that the interactive model identification method can achieve the consensus and convergence.

We present a drift-free pose estimation scheme for rigid body and its supporting platform by fusing only two gyroscopes and the relative complementary measurements. The fusion design not only provides robust relative attitude estimation between the rigid body and the platform, but also is capable of identifying partial global absolute attitudes without capturing any absolute attitude information. The pose estimation is built on a special design of the coupled kinematic model with the relative measurements between the rigid body and its supporting platform. We compare the fusion design with an alternative kinematic model and the posterior Cramer-Rao bound analyses are presented to show the completely different estimation performances. An extended Kalman filter (EKF) implementation of the fusion design is presented for the bicycle riding application.

Since the Tethered Space Robot (TSR) has been a research focus as an application of the space tether, a wide range of problems arise in the different phases of the capture mission. In this paper, we propose a new control scheme for the retrieval of passive space debris after captured by a TSR. Under this control scheme, target can be retrieved rapidly, and both of oscillations of tether and target are converged well. First, we derive the equations of attitude motions for the compound system when the passive target satellite is captured by Tethered Space Robot, where the base satellite (chaser) and the space debris (target) are modeled as rigid bodies and the attachment points of the tether are offset from the centers of mass of the two bodies. Then based on the specifics of equations, we divide the retrieval into two phases, and set a threshold for the retrieval. In different phases, different priorities are presented, and different control schemes are given. Finally, the simulation results are shown to prove that the target satellite could be retrieved rapidly and smoothly in a small oscillation, and the oscillation of tether is totally converged at the end of retrieval.

Human gait is a kind of dynamic biometrical feature which is complex and difficult to imitate, it is unique and more secure than static features such as password, fingerprint and facial feature. Analyzing people walking patterns, their "step-prints", can lead to the recognition of personal identity. In this paper, we propose to design, build, calibrate, analyze, and use wearable intelligent shoes; then focus on classifying the wearers into authorized ones and unauthorized ones by modeling their individual gait performance. Firstly the intelligent shoes for collecting and modeling human gait to measure an unprecedented number of parameters relevant to gait are presented. Then we introduce cascade neural networks with node-decoupled extended Kalman filtering (CNN-NDEKF) from the paper by Nechyba and Xu (1997) to apply for modeling and classifier generation. Finally, the experimental results of learning algorithms and comparison are described and verify that the proposed method is valid and useful for human identification.

In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.

Any motion of robotic manipulator will disturb its base in space due to the dynamic coupling. Such a disturbance will produce the serious impact between the manipulator hand and the object. Moreover, the disturbance will affect the communication with the ground and power supply for the space robot. On the other hand, compensating the disturbance using the attitude control system will consume large fuel which is limit in space. Therefore, a novel approach based on genetic algorithms (GA) is developed to find a global optimal path of a space robotic manipulator in joint space in order to minimize the disturbance to the base of space robot. The planning procedure is performed with respect to all constraints, such as joint angle constraints, joint velocity constraints, joint angular acceleration and torque constraints, and so on. We use GA to search the optimal joint inter-knot parameters in order to realize the minimum disturbance. These joint inter-knot parameters mainly include joint angle and joint angular velocities. We use an illustrative example to verify that GA-based optimal path planning method has satisfactory performance and real significance in engineering

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.