This paper investigates distributed optimal teleoperation control for networked mobile manipulators (NMMs) subject to model uncertainties, nonholonomic constraints, and external disturbances. To achieve cost-minimization cooperative control with prescribed transient and steady-state performance, a hierarchical optimization prescribed performance (HOPP) framework is proposed by integrating a reinforcement learning-based optimization estimator (RLOE) with a prescribed performance stability controller (PPSC). In the proposed scheme, the RLOE generates distributed reference trajectories for slave mobile manipulators through local neighbor interactions while minimizing a cooperative performance index. The PPSC is then designed to guarantee bounded tracking errors with prescribed convergence behavior for both the master and slave manipulators. Lyapunov-based analysis is provided to establish the boundedness and convergence properties of the closed-loop system. Rooted in Lyapunov stability theory, the proposed control algorithm is designed to ensure reliability and efficacy. Simulation studies on a teleoperation system of 2-DoF mobile manipulators demonstrate that the proposed method achieves accurate tracking, reduced cooperative cost, and improved transient performance.

This paper addresses the path planning and bipartite formation tracking (BFT) problem for multiple Euler-Lagrange agents (MELAs) subject to actuator faults and input saturation. A fully distributed three-layer framework is proposed. In the decision layer, a safe path-velocity Q-learning (SPV-Q-learning) algorithm is proposed, which ensures the rapid generation of reliable paths by introducing a decay mechanism and safety constraints. In the estimated layer, a time-base generator (TBG)-based observer is introduced to reconstruct the leader’s state using only local interactions, then, all followers acquire the leaders state under the fully distributed estimator. In the local layer, an anti-saturation fault-tolerant law is designed to handle model uncertainties, actuator degradation, and bounded inputs, thereby ensuring high-precision trajectory tracking for all agents. Simulation results demonstrate that the proposed method achieves both efficient path planning and robust BFT performance, significantly improving resilience under actuator faults and input constraints.

In this article, we investigate the task optimization for fixed-time control of intermittent human-robot interaction, where a human operator assists the robot intermittently in selecting the most appropriate Pareto solution. First, as for the Lyapunov fixed-time stability criterion inequality with and without the constant term, we all derive the Lyapunov stability conditions with time-varying exponents and coefficients, providing us with more flexibility and freedom to shape the contour of the convergence near the Lyapunov stable equilibrium. We then use them to propose a hierarchical fixed-time event-triggered optimization (HFTEO) algorithm based on human-oriented scheme, where the so-called human-oriented scheme means that the components constituting task information are known only to the human operator, but not to the robot, which is beneficial to ensure the confidentiality and security of the task. Simulation results are given to show the effectiveness of the proposed Lyapunov stability conditions and algorithm.

Formation control based on path planning is an important and critical research topic in robotics, which focuses on generating collision-free paths for multiagent systems (MASs) from an initial position to a target position while maintaining the desired formation. This article realizes adaptive path planning and formation control for MASs with the presence of lumped uncertainties and saturation input. To achieve this goal, a hierarchical adaptive formation planning and control (HAFPC) framework, including a formation path planning layer and an adaptive formation control layer, is constructed. In the formation path planning layer, the signed-distance-field-based formation path planning (SDF-FPP) algorithm is proposed to find a collision-free continuous trajectory in an unknown environment from the initial position to the target position. Based on this collision-free trajectory, a nonanalytic function that evaluates the shortest distance between this collision-free trajectory and obstacles is computed via the signed distance field (SDF) method. Then, this nonanalytic function will be further processed in the next layer for obstacle avoidance of all agents. In the adaptive formation control layer, the proposed adaptive-offset formation control (AOFC) algorithm converts the nonanalytic function into the adaptive offset functions for all agents and manipulates MASs to achieve adaptive formation control for obstacle avoidance with the presence of lumped uncertainties as well as saturation input. Simulations are presented to validate the proposed architecture.

This paper presents the hierarchical Q-learning path planning (HQPP) architecture for solving the cooperative tracking control problem of multi-agent systems (MASs) with lumped uncertainties in an unknown environment. The presented architecture consists of three layers, namely, the decision layer, the distributed estimated layer, and the local control layer. Specifically, in the decision layer, we propose the dynamic parameter and trajectory fitting Q-learning (DPTF-Q-learning) algorithm to find a feasible continuous trajectory to the target in an unknown environment. In addition, two dynamic parameters are proposed and introduced into the DPTF-Q-learning algorithm to shorten the required minimum number of steps in the training process. Then, the distributed estimated layer is designed to broadcast the continuous trajectory generated from the decision layer based on the directed communication topology containing a spanning tree. In the local control layer, the cooperative tracking control (CTC) algorithm is proposed to achieve cooperative tracking for MASs in the presence of uncertain dynamics and external disturbances. The sufficient conditions for achieving cooperative tracking control are rigorously derived by employing Lyapunov argument. Finally, numerical simulations are presented to verify the effectiveness of the proposed architecture.Note to Practitioners—This paper is motivated by the need of developing an integrated path planning and control method for cooperative tracking of multi-agent systems in a no-signal environment and without the presence of users. Most related works are limited to separate fields: 1) most existing path planning techniques are only applicable to a single agent and discrete environments, and 2) most existing cooperative tracking algorithms focus on guaranteeing control stability and error convergence without decision-making capabilities. To address the above issues, this work proposes a hierarchical control architecture based on reinforcement learning for multi-agent systems to achieve path planning and cooperative tracking tasks. In addition, multi-agent systems exhibit strong robustness and fault tolerance due to their inherent characteristics, so the above mentioned research can be well applied to post-disaster rescue, intelligent logistics, future war, and so on. Numerical simulations based on Matlab and Python verify the effectiveness of the proposed architecture.

Planning trajectories and trajectory tracking are significant and fundamental tasks for Lagrange-based autonomous ground vehicles. In this paper, a novel unified framework integrating path planning and trajectory tracking is proposed based on deep reinforcement learning for Lagrange-based autonomous ground vehicles considering motion limitation and energy consumption, namely, hierarchical piecewise-trajectory planning (HPP) framework. The framework consists of three layers, namely the path planning layer, the trajectory planning layer, and the local control layer. Firstly, the path planning layer enables the vehicle to find a discrete path from its initial position to its target position. Afterward, the trajectory planning layer ensures that discrete trajectory points are transformed into continuous trajectory functions based on the polynomial curve interpolation method. The adaptive asymptotic acceleration planning algorithm is proposed to satisfy the limitations of maximum velocity and acceleration for vehicles. Finally, the trajectory tracking control algorithm and poweroff trigger mechanism are developed to achieve the following two goals in the local control layer: 1) regulating the vehicle to follow its continuous trajectory curve, 2) switching off the power to save energy when its instantaneous kinetic energy is adequate to supply the energy consumption. Numerous simulation results show that our framework enables autonomous ground vehicles to accomplish integrated path planning and trajectory tracking tasks with the presence of motion limitation. Two extra examples are presented to demonstrate that our method is generalizable in terms of energy savings compared to existing optimization-based methods.

This article studies the secure surrounding formation (SSF) problem of networked marine surface vehicles subject to denial of service (DoS) attacks. A hierarchical control framework is developed for designing the predefined-time fuzzy reinforcement learning controller, which consists of two layers. The distributed resilient estimator is proposed to accurately estimate the trajectory of the leader center in the predefined-time under DoS attacks over digraphs. The fuzzy reinforcement learning local controller is designed to achieve the SSF within the predefined-time. The sufficient conditions for system convergence and stability are derived based on the Lyapunov stability theory. Finally, simulation experiments are conducted to verify the effectiveness of the theoretical results.

This article studies the lag-bipartite formation tracking (LBFT) problem of the networked robotic systems (NRSs) with directed matrix-weighted signed graphs. Unlike the traditional formation tracking problems with only cooperative interactions, solving the LBFT problem implies that: 1) the robots of the NRS are divided into two complementary subgroups according to the signed graph, describing the coexistence of cooperative and antagonistic interactions; 2) the states of each subgroup form a desired geometric pattern asymptotically in the local coordinate; and 3) the geometric center of each subgroup is forced to track the same leader trajectory with different plus-minus signs and a time lag. A new hierarchical control algorithm is designed to address this challenging problem. Based on the Lyapunov stability argument and the property of the matrix-weighted Laplacian, some sufficient criteria are derived for solving the LBFT problem. Finally, simulation examples are proposed to validate the effectiveness of the main results.