In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently merged with static point clouds to construct the forward-time-domain (FTD) map. By combining control barrier functions (CBFs) with nonlinear model predictive control, the proposed algorithm enables the robot to effectively avoid both static and dynamic obstacles. The CBF constraints are formulated based on risk points identified through collision detection between the predicted future states and the FTD map. Experimental results from both simulated and real-world scenarios demonstrate the efficacy of the proposed algorithm in complex environments. In simulation experiments, the proposed algorithm is compared with two baseline approaches, showing superior performance in terms of safety and robustness in obstacle avoidance. The source code is released for the reference of the robotics community.

This article presents rigidity-based formation maneuvering for a group of nonholonomic mobile robots subject to limited sensing capability, where the performance bounds are introduced to constrain the distance and angle errors. The time-varying and asymmetric performance constraints can prescribe the transient and steady-state performance of the closed-loop systems, which further specify collision avoidance and connectivity maintenance among neighboring robots and avoid the controller singularity issue. To satisfy the constraint requirements and fixed-time convergence, universal barrier Lyapunov functions are incorporated with control design such that angle errors are fixed-time stable and distance errors can converge to a small neighborhood around zero in fixed time. Under the proposed control protocol, all robots can track the desired time-varying velocity while generating and maintaining the predefined formation defined by a minimally and infinitesimally rigid graph. Simulation and experiment studies are carried out to illustrate the effectiveness of the proposed control protocol.

Limited computational resource is one of the features of the embedded system including unmanned marine surface vehicle (MSV). Under this practical constraint, we aim to develop a distributed formation control algorithm for a group of uncertain MSVs, whose computational burden is low. The uncertainty and disturbance estimator (UDE) is employed to compensate for the modeling uncertainties, the unknown environmental disturbances, and the unavailable derivative of the virtual control inputs, such that the formation errors converge asymptotically to zero. Meanwhile, the prescribed performance control technique is applied to ensure that the convergence rates of the formation errors are faster than predefined values. In addition, collision avoidance and connectivity preservation among the neighboring vehicles are guaranteed, while the obstacle avoidance is also ensured. As no parameter update law is required to calculate, the computational burden is reduced significantly. The effectiveness of the proposed UDE-based formation control algorithm is validated through comparative simulation.

In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.

This article presents an online reinforcement learning (RL) algorithm to learn the distributed optimal containment control solution for underactuated surface vehicles subject to modeling uncertainties and unknown ocean disturbances. First, the request of exact knowledge of vehicle system dynamics is avoided by constructing an adaptive neural network identifier. The unknown lumped disturbances are estimated by disturbance observers. Second, an actor-critic-based RL algorithm is developed to release the persistence of excitation condition. Third, based on RL algorithm and backstepping procedure, we design adaptive finite-time optimal containment controllers such that the containment errors converge into a small neighborhood around zero in finite time and satisfy the predefined performance specifications. Finally, simulation studies on underactuated surface vehicles are provided to verify the effectiveness of the presented optimal control algorithm.

This article studies the vision-based tracking control problem for a nonholonomic multirobot formation system with uncertain dynamic models and visibility constraints. A fixed onboard vision sensor that provides the relative distance and bearing angle is subject to limited range and angle of view due to limited sensing capability. The constraint resulting from collision avoidance is also taken into account for safe operations of the formation system. Furthermore, the preselected specifications on transient and steady-state performance are provided by considering the time-varying and asymmetric constraint requirements on formation tracking errors for each robot. To address the constraint problems, we incorporate a novel barrier Lyapunov function into controller design and analysis. Based on the recursive adaptive backstepping procedure and neural-network approximation, we develop a vision-based formation tracking control protocol such that formation tracking errors can converge into a small neighborhood of the origin in finite time while meeting the requirements of visibility and performance constraints. The proposed protocol is decentralized in the sense that the control action on each robot only depends on the local relative information, without the need for explicit network communication. Moreover, the control protocol could extend to an unconstrained multirobot system. Both simulation and experimental results show the effectiveness of the control protocol.

In this paper, for the line-of-sight (LOS) tracking problem of a tractor-trailer vehicle system, we propose a novel adaptive constrained tracking control algorithm for the trailer to track a desired trajectory, while satisfying multipleperformanceandfeasibilityconstraint requirements during the operation. To deal with these constraint requirements, both a universal barrier function approach and a novel state transformation scheme are incorporated to deal with constraints of different nature. An adaptive control structure is introduced to deal with system parameter uncertainties and external disturbances. We show that for the LOS distance and angle tracking errors, exponential convergence into small neighbourhoods of equilibrium can be guaranteed, with the convergence rate depending on the control input and adaptive gains. In the end, a simulation example and an experimental study further demonstrate the efficacy of the proposed algorithm.

This article studies the formation tracking control problem for a group of underactuated surface vehicles with guaranteed transient properties, including connectivity maintenance, collision avoidance, and tracking performance specifications. The formation is within the leader–follower control framework, in which every follower is controlled to track its leader and maintain a desired relative distance and bearing angle with respect to its leader such that the prescribed formation geometry is achieved based on local sensing capability. The onboard sensor systems are of limited range and angle of view, thus defining a cone of detectable region for every follower. Each follower can detect its leader, if and only if the relative distance and bearing angle keep always inside the predefined detectable region such that the connectivity between the follower and its leader is maintained over time. In addition to the consideration of connectivity maintenance, no collision between the follower and its leader is also considered. A transverse function control approach is introduced to overcome the difficulties caused by the off-diagonal system matrix and underactuation. The barrier Lyapunov function and adaptive backstepping procedure are incorporated into the formation control design to achieve the boundedness of the closed-loop systems with guaranteed transient performance. Collision avoidance and connectivity maintenance between every follower and its leader are also proven mathematically. Simulation studies are performed to show the effectiveness of the proposed control design technique.

This article addresses the cooperative learning formation control problem for multiple homogeneous marine surface vessels (MSVs) subject to external time-varying disturbances and modeling uncertainties under the prescribed performance constraint. The modeling uncertainties, including hydrodynamic damping terms and unmodeled dynamics are identified/learned by the localized radial basis function neural networks (NNs) in a cooperative way. Disturbance observers are incorporated into the formation control design to compensate for the external time-varying disturbances. A novel cooperative learning formation controller is proposed, which is shown to be capable not only of fulfilling the predefined formation pattern with guaranteed prescribed performance but also of identifying/learning the associated uncertain dynamics based on the cooperative deterministic learning theory. Moreover, the learned knowledge on identified uncertain dynamics is stored in NN models with converged constant NN weights. Based on the stored knowledge, an experience-based formation controller is developed, which can improve the control performance including reduction of the computational burden, while guaranteeing prescribed performance of formation tracking errors. Simulation results demonstrate the effectiveness of the proposed formation control protocol.

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this article, we develop such a physical human-robot interaction system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model and the Gaussian mixture regression. More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach.

For an uncertain multiagent system, distributed cooperative learning control exerting the learning capability of the control system in a cooperative way is one of the most important and challenging issues. This article aims to address this issue for an uncertain high-order nonlinear multiagent system with guaranteed transient performance and preserved initial connectivity under an undirected and static communication topology. The considered multiagent system has an identical structure and the uncertain agent dynamics are estimated by localized radial basis function (RBF) neural networks (NNs) in a cooperative way. The NN weight estimates are rigorously proven to converge to small neighborhoods of their common optimal values along the union of all agents' trajectories by a deterministic learning theory. Consequently, the associated uncertain dynamics can be locally accurately identified and can be stored and represented by constant RBF networks. Using the stored knowledge on identified system dynamics, an experience-based distributed controller is proposed to improve the control performance and reduce the computational burden. The theoretical results are demonstrated on an application to the formation control of a group of unmanned surface vehicles.

In this article, we study the formation tracking control problem for a group of nonholonomic mobile robots under communication constraints. We use a simple leader–follower formation tracking control strategy, in which only the leading robot of the robotic group obtains the given trajectory's information, and each robot only receives information from its immediate leader. Thus, the communication graph forms a static and simple directed spanning tree. We assume that the information exchange among the robots is limited by some given communication radius. Under the limited communication range, the leader–follower distance and bearing angle constraints are considered in the formation control design. To provide the given specifications on the transient and steady-state performances of formation tracking errors, the boundaries of the predefined constraints are enforced by designer-specified performance requirements, which are functions of time. Consequently, we incorporate the barrier Lyapunov function into formation control design to mathematically prove that the formation tracking errors evolve always within the predefined regions and converge asymptotically to zero. The proposed formation control strategy can also guarantee the connectivity maintenance and collision avoidance between the leader and the follower. Simulation results show the performance of the proposed formation tracking control.

Due to the lack of transparent and friendly human-robot interaction (HRI) interface, as well as various uncertainties, it is usually a challenge to remotely manipulate a robot to accomplish a complicated task. To improve the teleoperation performance, we propose a new perception mechanism by integrating a novel learning method to operate the robots in the distance. In order to enhance the perception of the teleoperation system, we utilize a surface electromyogram signal to extract the human operator's muscle activation. As a response to the changes in the external environment, as sensed through haptic and visual feedback, a human operator naturally reacts with various muscle activations. By imitating the human behaviors in task execution, not only motion trajectory but also arm stiffness adjusted by muscle activation, it is expected that the robot would be able to carry out the repetitive tasks autonomously or uncertain tasks with improved intelligence. To this end, we develop a robot learning algorithm based on probability statistics under an integrated framework of the hidden semi-Markov model (HSMM) and the Gaussian mixture method. This method is employed to obtain a generative task model based on the robot's trajectory. Then, Gaussian mixture regression based on HSMM is applied to correct the robot trajectory with the reproduced results from the learned task model. The execution procedures consist of a learning phase and a reproduction phase. To guarantee the stability, immersion, and maneuverability of the teleoperation system, a variable gain control method that involves electromyography (EMG) is introduced. Experimental results have demonstrated the effectiveness of the proposed method.

This paper addresses a decentralized leader-follower formation control problem for a group of fully actuated unmanned surface vehicles with prescribed performance and collision avoidance. The vehicles are subject to time-varying external disturbances, and the vehicle dynamics include both parametric uncertainties and uncertain nonlinear functions. The control objective is to make each vehicle follow its reference trajectory and avoid collision between each vehicle and its leader. We consider prescribed performance constraints, including transient and steady-state performance constraints, on formation tracking errors. In the kinematic design, we introduce the dynamic surface control technique to avoid the use of vehicle's acceleration. To compensate for the uncertainties and disturbances, we apply an adaptive control technique to estimate the uncertain parameters including the upper bounds of the disturbances and present neural network approximators to estimate uncertain nonlinear dynamics. Consequently, we design a decentralized adaptive formation controller that ensures uniformly ultimate boundedness of the closed-loop system with prescribed performance and avoids collision between each vehicle and its leader. Simulation results illustrate the effectiveness of the decentralized formation controller.

This paper presents adaptive neural tracking control of underactuated surface vessels with modeling uncertainties and time-varying external disturbances, where the tracking errors consisting of position and orientation errors are required to keep inside their predefined feasible regions in which the controller singularity problem does not happen. To provide the preselected specifications on the transient and steady-state performances of the tracking errors, the boundary functions of the predefined regions are taken as exponentially decaying functions of time. The unknown external disturbances are estimated by disturbance observers and then are compensated in the feedforward control loop to improve the robustness against the disturbances. Based on the dynamic surface control technique, backstepping procedure, logarithmic barrier functions, and control Lyapunov synthesis, singularity-free controllers are presented to guarantee the satisfaction of predefined performance requirements. In addition to the nominal case when the accurate model of a marine vessel is known a priori, the modeling uncertainties in the form of unknown nonlinear functions are also discussed. Adaptive neural control with the compensations of modeling uncertainties and external disturbances is developed to achieve the boundedness of the signals in the closed-loop system with guaranteed transient and steady-state tracking performances. Simulation results show the performance of the vessel control systems.

This paper studies the problem of learning from adaptive neural network (NN) control of a class of nonaffine nonlinear systems in uncertain dynamic environments. In the control design process, a stable adaptive NN tracking control design technique is proposed for the nonaffine nonlinear systems with a mild assumption by combining a filtered tracking error with the implicit function theorem, input-to-state stability, and the small-gain theorem. The proposed stable control design technique not only overcomes the difficulty in controlling nonaffine nonlinear systems but also relaxes constraint conditions of the considered systems. In the learning process, the partial persistent excitation (PE) condition of radial basis function NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition and an appropriate state transformation, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the implicit desired control input dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, an NN learning control design technique that effectively exploits the learned knowledge without re-adapting to the controller parameters is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.

This paper presents the problems of accurate identification and learning control of ocean surface ship in uncertain dynamical environments. Thanks to the universal approximation capabilities, radial basis function neural networks (NNs) are employed to approximate the unknown ocean surface ship dynamics. A stable adaptive NN tracking controller is first designed using backstepping and Lyapunov synthesis. Partial persistent excitation (PE) condition of some internal signals in the closed-loop system is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN controller is shown to be capable of accurate identification/learning of the uncertain ship dynamics in the stable control process. Subsequently, a novel NN learning control method which effectively utilizes the learned knowledge without re-adapting to the unknown ship dynamics is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

This paper is concerned with simultaneous stability of a collection of continuous-time linear plants whose feedback control loops are closed via a shared digital communication network. Because of the limitation of communication capacity, only a limited number of controller-plant connections can be accommodated at any time instant. Therefore, it is necessary to carefully design the scheduling policy so as to achieve simultaneous stabilization for all these control loops. In the paper, sufficient condition on the existence of such a scheduling policy is presented for the collection of networked LTI systems with sampled-data controllers and network-induced uncertain delays. It turns out that the condition is only based on the convergence rate of the closed-loop system and the divergence rate of the open-loop plant. The proof for this schedulability condition is in a constructive way, which can also serve as a systematic way for the scheduling policy design.