Quadruped robots have exhibited highly adaptive locomotion capabilities, yet reliable navigation on complex mixed unstructured terrain remains a major challenge. A key difficulty is that existing navigation frameworks typically rely on geometric costmaps, which fail to capture the fact that terrain traversability is inherently policy-dependent, as different locomotion controllers exhibit distinct contact strategies, dynamic limits, and behavioral capabilities. To address this issue, we present a unified framework that learns and exploits policy-dependent traversability for large-scale autonomous navigation with quadrupedal robots. We first develop a data-driven traversability estimator that predicts the traversability of each terrain region under a given locomotion policy, trained from a kernel-based traversability sample map aggregated from simulated policy rollouts. This estimator is used to construct a terrain-aware costmap with policy-dependent feasibility constraints. Then, a two-stage planning pipeline with grid-based search and trajectory optimization is proposed to generate smooth, policy-feasible global trajectories for execution by the corresponding locomotion policy. Finally, we extend the framework to multiple locomotion policies, enabling automatic policy switching based on predicted traversability and enhancing adaptability to diverse terrain conditions. Experiments across large and challenging environments demonstrate that our approach significantly improves navigation reliability, terrain adaptability, and long-horizon autonomy for quadrupedal robots.

This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conventional method that does not ensure the satisfaction of hard state-and input-dependent constraints and leads to feasibility issues for multi-CSUR systems. In this paper, we solve these problems by designing a novel coverage cost function and a saturated gradient-search-based control law. Theoretical proofs are provided to guarantee that the CSURs ultimately move to the optimal coverage configuration without moving out of the covered domain. The controller is implemented in a distributed manner based on a novel communication standard among the agents. A series of simulation studies are conducted to validate the correctness of our theory by showing the efficacy of the proposed coverage controller in different initial conditions and with various control parameters. A comparison study in simulation reveals the advantage of the proposed method over the conventional method in terms of avoiding infeasibility. The experimental study verifies the applicability of the method to real robots. The development procedure of the method from theoretical analysis to experimental validation provides a novel framework for multi-agent system coordinate control with complex dynamics.Note to Practitioners—This paper gives a novel method to effectively cover a polygonal area using multiple constant-speed unicycle robots (CSUR) like wheeled robots and fixed-wing unmanned aerial vehicles (fUAV). Compared to the conventional approaches, our method allows these robots to cover a target region using circular orbits without departing the covered region. Also, the method satisfies common control saturation constraints in practice and can be implemented in a reliable distributed scheme. While the efficacy and correctness of the proposed method are rigorously proved using control theory, we also provide necessary interpretive elucidations to explain its underlying mechanism and selection rationale. The method is validated to be effective for wheeled robots in experimental studies, although it can also be applied to fUAVs in theory.

Addressing safe and efficient navigation in dynamic, realistic, and complex environments stands as a pivotal inquiry within the realm of robotics. Recently, numerous learning-based methods are introduced into the field of navigation, yielding notable outcomes. In this letter, we propose a hierarchical safe reinforcement learning navigation approach (HSRLN) for mapless navigation. It trains mapless navigation policies for non-homogeneous complex scenarios in a hierarchical manner through a kind of three-stage learning, global planning reinforcement learning (RL) + expert imitation learning (IL) + transfer RL (TRL). The innovations of this work are fourfold: a) It effectively reduces the difficulty of training for complex navigation by effectively narrowing the task horizon of RL through a hierarchical framework. b) We designed an imitation learning method based on Relative Driving Safety Index (RDSI) [1] to focus on learning critical expert actions. c) It employs a TRL approach to improve generalization under non-homogeneity assumptions by fine-tuning the policy. d) HSRLN extracts significant features important for navigation decisions from raw observations via velocity obstacle modeling. Experiments indicate that it has performs better than existing hierarchical RL navigation methods (HDRL [2], SRL-ORCA [3]). Relative to SRL-ORCA, it improves navigation success by 12.1% under the non-homogeneity assumption. Videos are available at https://youtu.be/24h9JmcIfMw.

Integrating Large Language Models (LLMs) into robotics significantly enhances autonomous task planning. However, ensuring that multi-step task plans (action sequence) generated by LLMs comply with pre-defined safety constraints during planning and execution remains a challenge, limiting their adaptability in complex environments. To address this issue, a mechanism that can monitor and adjust the plan generated by the LLM-driven task planner and guide the motion planner to avoid potential risks during action execution is required. Therefore, this paper proposes a cross-layer sequence supervision mechanism. Specifically, we employ linear temporal logic syntax to express safety constraints and convert them into a set of nondeterministic Büchi automatons to build a cross-layer safety supervisor. For the task planning layer, the safety supervisor provides a closed-loop correction mechanism that can identify violations in the task plan in real time and guide LLM-driven planners to correct this plan to ensure compliance. For the motion planning layer, the safety supervisor introduces virtual "obstacle" information into the task plan to form the task plan tuple. Based on this plan tuple, the motion planner can proactively prevent unsafe behaviors during action execution. Extensive experimentation demonstrates significant improvements in safety with this cross-layer supervision mechanism, highlighting its potential to enhance LLM-driven robotic technology. Experiment details can be found in https://youtu.be/BDdSSEP6HJw.

Safety as a fundamental requirement for human-swarm interaction has attracted a lot of attention in recent years. Most existing approaches solve a constrained optimization problem at each time step, which has a high real-time requirement. To deal with this challenge, this article formulates the safe human-swarm interaction problem as a Stackerberg-Nash game, in which the optimization is performed over the entire time domain. The leader robot is supposed to be in a dominant position, interacting directly with the human operator to realize trajectory tracking and responsible for guiding the swarm to avoid obstacles. The follower robots always take their best responses to leader's behavior with the purpose of achieving the desired formation. Following the bottom-up principle, we first design the best-response controllers, that is, Nash equilibrium strategies, for the followers. Then, a Lyapunov-like control barrier function-based safety controller and a learning-based formation tracking controller for the leader are designed to realize safe and robust cooperation. We show that the designed controllers can make the robotic swarms move in a desired geometric formation following the human command and modify their motion trajectories autonomously when the human command is unsafe. The effectiveness of the proposed approach is verified through simulation and experiments. The experiment results further show that safety can still be guaranteed even when there exists a dynamic obstacle.

Differential game is an effective technique to describe the negotiation between the humans and robots, which is widely used to realize the trajectory tracking tasks in the human-robot interaction (HRI). However, most existing works consider the control-affine HRI systems and assume the desired trajectory is available to both the human and the robot, which limit the scope of applications. To overcome these difficulties, this work focuses on the nonaffine HRI system and supposes that the desired trajectory is not available to the robot. A novel differential game framework encoding the desired trajectory estimator is proposed, where the desired trajectory is estimated via the Gaussian process regression (GPR) technique. To address the challenge arising from the nonlinearity of the HRI system, we equivalently transform the original problem into the one in a differentially flat space, and seek the equilibrium strategies for the transformed problem substitutionally. We further prove that the trajectory tracking error satisfies a probabilistic bound, whose confidence interval tightens as the decrease of noise variance during the interaction. Comparative simulation results show that our method outperforms the learning-based method in terms of robustness, parameters setting, and time consumption. Experiment results further show that the tracking error under the proposed human-robot cooperative algorithm is reduced by 55% compared to the human direct control.

This article is concerned with the output feedback security control of a class of high-order nonlinear-interconnected systems with denial-of-service (DoS) attacks, nonlinear dynamics, and exogenous disturbances. First, extreme learning machine (ELM) and adaptive techniques are adopted to approximate the unknown nonlinearities. Then, novel adaptive ELM-based nonlinear state observers with adaptive compensation functions are developed to estimate the unmeasurable states during DoS attacks under the influence of the disturbances. Further, by combining with the backstepping control and filtering techniques, adaptive ELM-based controllers are proposed to achieve uniformly ultimately bounded results based on the observation and adaption control signals under the influence of DoS attacks, nonlinear dynamics, and exogenous disturbances. Comparative studies are carried out to validate the effectiveness of the developed ELM-based adaptive observation and control strategies for two interconnected power systems.

This article proposes an off-policy risk-sensitive reinforcement learning (RL)-based control framework to jointly optimize the task performance and constraint satisfaction in a disturbed environment. The risk-aware value function, constructed using the pseudo control and risk-sensitive input and state penalty terms, is introduced to convert the original constrained robust stabilization problem into an equivalent unconstrained optimal control problem. Then, an off-policy RL algorithm is developed to learn the approximate solution to the risk-aware value function. During the learning process, the associated approximate optimal control policy is able to satisfy both input and state constraints under disturbances. By replaying experience data to the off-policy weight update law of the critic neural network, the weight convergence is guaranteed. Moreover, online and offline algorithms are developed to serve as principled ways to record informative experience data to achieve a sufficient excitation required for the weight convergence. The proofs of system stability and weight convergence are provided. The Simulation results reveal the validity of the proposed control framework.

With the popularization of network equipment and the rapid development of information technology, the scale and complexity of wireless sensor networks (WSNs) continue to expand. How to effectively locate link failures has become a challenging problem in WSNs. In this paper, we propose a novel method of locating link failures based on distributed cluster consensus protocol and graph decomposition technique. In our method, the initial data is injected into sensor nodes for distributed interactions, and then link failures can be located by observing and comparing the output data of the nodes. The proposed method is suitable for the situations with both single-link failure and multi-link failures, and has no limitations on the number, distribution and correlation of link failures. Necessary and sufficient conditions are provided to guarantee the accuracy of the proposed method in locating link failures. At last, the effectiveness of the proposed method is verified by both real and simulation experiments.

Voronoi coverage control is a particular problem of importance in the area of multi-robot systems, which considers a network of multiple autonomous robots, tasked with optimally covering a large area. This is a common task for fleets of fixed-wing Unmanned Aerial Vehicles (UAVs), which are described in this work by a unicycle model with constant forward-speed constraints. We develop event-based control/communication algorithms to relax the resource requirements on wireless communication and control actuators, an important feature for battery-driven or otherwise energy-constrained systems. To overcome the drawback that the event-triggered algorithm requires continuous measurement of system states, we propose a self-triggered algorithm to estimate the next triggering time. Hardware experiments illustrate the theoretical results.

This article discusses cooperative stabilization control of rigid formations via an event-based approach. We first design a centralized event-based formation control system, in which a central event controller determines the next triggering time and broadcasts the event signal to all the agents for control input update. We then build on this approach to propose a distributed event control strategy, in which each agent can use its local event trigger and local information to update the control input at its own event time. For both cases, the triggering condition, event function, and triggering behavior are discussed in detail, and the exponential convergence of the event-based formation system is guaranteed.

This paper studies the problem of distributed computation of higher order Voronoi partition over a bounded region by a group of robots with both range-limited visibility sensors and communication devices. We model the sensing and communication capabilities by discs with limited radius. Motivated by the concept of dominating region in higher-order Voronoi partition, we propose a detecting ray based algorithm, which computes the boundary points of the dominating region of a robot in an omnidirectional manner, with local position information of its neighbors within the communication range. Simulations are provided to demonstrate the performance of our proposed algorithm by using a thirteen-robot group.

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We first propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work studying event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities, which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information, and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics that include the vector of gravitational potential forces, an adaptive algorithm is proposed. This requires more information about the agent dynamics but allows for the estimation of uncertain parameters associated with the agent self-dynamics. For each algorithm, a trigger function is proposed to govern the event update times. The controller is only updated at each event, which ensures that the control input is piecewise constant and thus saves energy resources. We analyze each controller and trigger function to exclude Zeno behavior.