This article studies the problem of output-feedback safe navigation for high-order mobile multiagent systems (MASs) in the presence of disturbances and measurement error. We provide a theoretical framework to extend the barrier Lyapunov function’s negative gradient method for safety-critical control to the case where accurate measurements of agent states are unavailable. Specifically, an observer helps estimate the unmeasured states, and these estimations are used in the control design process. We consider a worst case scenario together with a modified gradient-based virtual control to avoid sensitivity caused by estimation errors, and the system velocity is driven to track the virtual control. The safe navigation control objective is achieved even though the system states are not measured accurately, which is essential for realistic MASs with low-quality sensors. Moreover, this framework for general high-order systems is suitable for a wide range of agent dynamics. We conduct several numerical simulations to observe the behavior of the system under the output-feedback controller.

Achieving flexible human-following in real-world environments remains a critical yet challenging problem in Human-Robot Interaction (HRI). Traditional approaches typically constrain robots to fixed tracking positions—such as following from behind, ahead, or alongside—thereby limiting their adaptability in dynamic and unstructured environments. This study introduces a reinforcement learning-based framework that allows the robot to dynamically adjust its tracking positions in response to workspace constraints. An interaction space is defined to capture the relationship between the human and the robot while considering the environment. This space serves as the basis for state spaces in Deep Reinforcement Learning (DRL), helping the robot adapt to environmental changes. The selected tracking position is then utilized as input for a human-following controller, ensuring smooth and continuous motion. Experimental evaluations in both indoor and outdoor environments demonstrate that the proposed approach enables robots to follow humans flexibly and adaptively, adjusting their positions autonomously and avoiding obstacles without requiring a predefined tracking position.

This study develops a novel UAV–UGV collaborative system for persistent surveillance of dynamic environments. The main tasks are independently assigned to the UAVs and UGVs, while sharing a rendezvous-based charging mission. The UAVs focus on AoI-based persistent coverage, and the UGVs are responsible for covering signal sources in the environment, serving not only as mobile charging stations, but also as active participants in environmental sensing. The system incorporates physical constraints such as endurance limits and velocity bounds to ensure feasibility in real-world scenarios. A sequential rendezvous scheduling algorithm is presented, which dynamically adjusts the time window bounds between UAVs and UGVs, enabling conflict-free charging for multiple UAVs. Results demonstrate that the algorithm exhibits high flexibility and robustness across various UAV–UGV configurations. It effectively supports systems with a UAV-to-UGV ratio greater than 2 and performs well in most scenarios.

This paper introduces a novel impedance controller for mobile manipulators that ensures safe interaction with uncertain environments without requiring force information. Dynamics uncertainties are compensated by employing adapative radial basis function neural networks (RBFNN). Redundancy of mobile manipulators is managed to perform subtasks alongside tracking control without compromising performance. Stability analysis guarantee the uniformly ultimately boundedness of tracking errors during interaction with the environment and asymptotic convergence to zero in free motion. Numerical simulation is provided to validate the efficacy of the proposed method.

In this paper, the issue of integrating haptic technology of a robotic system and virtual environment is discussed. A robot-assisted virtual rehabilitation system is developed for bilateral training and telerehabilitation. In the system, we designed a training scene in the Unity game engine for these two rehabilitation modes, the proposed framework between two robots and virtual environment can allow the end-effectors to perform tasks as hand avatars in Unity and provide force feedback from virtual environment, the haptic rendering is generated on robot’s end-effector using a task-space impedance controller. Besides providing the feeling of interaction with virtual objects, we proposed a robot-assisted strategy to provide assistance force when the patient is unable to finish the task in virtual training scene, the assistance force can guide the patient to training path. We provide experiment results to demonstrate the performance of proposed system.

In this work, an optimization-based control scheme for a bilateral teleopered unmanned aerial vehicle (UAV) is proposed using control barrier functions (CBFs) and passivity. We consider a human operator moving the position of an end effector of a haptic device as the velocity command input to the UAV. The CBF is applied as a constraint to maintain the collision-free motion of the UAV for safety. On the other hand, to preserve the stability of the system, we consider passivity as another constraint together, but it leads to undesired behavior of an optimal solution for haptic feedback. To deal with the issue, the strategy called energy tank for passivity is included to replace the condition of strict output passivity. The aforementioned designs are formalized as a quadratically constrained quadratic program (QCQP) to solve numerically. Through numerical examples, we have a comprehensive discussion about the features of the proposed controller with better safety. The experimental results verify the effectiveness of our method in practice.

This article presents an effective design of omnidirectional four-mecanum-wheeled vehicles to transport an object and track a predefined trajectory cooperatively. Furthermore, a novel design of the rotary platform is presented for multiple unmanned ground vehicles (m-UGVs) to load objects and provide better maneuverability in confined spaces during cooperative transportation. The number of unmanned ground vehicles (UGVs) is adjustable according to the object's weight and size in the proposed framework because transportation is accomplished without physical grippers. Moreover, to minimize the complexity in dealing with the interactive force between the object and UGVs, no force/torque sensor is used in the design of the control algorithm. Instead, an adaptive sliding-mode controller is formulated to cope with the dynamic uncertainties and smoothly transport an object along a desired trajectory. Thus, three external force analyses-gradient projection method, adaptive force estimation, and radial basis function neural network force estimation-are proposed for m-UGVs. In addition, the stability and the performance tracking of the m-UGV system in the presence of dynamic uncertainties using the proposed force estimation are investigated by employing the Lyapunov theory. Finally, experiments on cooperative transportation are presented to demonstrate the efficiency and efficacy of the m-UGV system.

Aerial manipulators have the potential to perform various tasks with high agility and mobility, but the requirement of system parameters and the complicated dynamic model impede the implementation in practice. To deal with uncertain parameters and complexity of the coupled dynamic model, a decoupling approach is presented in this article by utilizing the adaptive/robust techniques and reinforcement learning approach for the tracking control of quadrotors with position control on the robotic arm. A reinforcement learning approach is proposed to control the robotic arm ensuring minimal effect on the quadrotor dynamics while following the desired trajectory. With the design of nominal inputs, the dynamic uncertainties from the quadrotor, robotic arm, and payload are coped with by utilizing the proposed adaptive algorithms. In addition, the residue of interactive force/torque after the use of DDPG is compensated by robust controllers so that the stability and tracking performance are guaranteed. Numerical examples and experiments are illustrated to demonstrate the efficacy of the presented aerial manipulator control structure and algorithms.

In recent years, micro-mobility transport vehicles have become very popular. As a result, the challenge of developing new strategies and tools for the safety of users and pedestrians in shared travelways has attracted a lot of attention. Previous studies have generally used a social force model as the main tool for the prediction of pedestrian movements. However, existing models cannot be directly applied to wheeled vehicles such as electrical scooters. This study presents a modified social force model to predict the interactions of an electric scooter with a pedestrian crowd by considering the scooter’s kinematics constraints and geometry and the velocity-dependent behaviors of the rider. Moreover, experiments are performed to calibrate the parameters of the proposed model and compare it to an existing social force model. The experimental results demonstrate that the scooter social force model is superior to the original model due to its higher prediction accuracy. Using the scooter model with experimentally calibrated parameters, numerical simulations illustrate the behavior of an e-scooter rider in a pedestrian crowd. Acceleration and force metrics are introduced to evaluate the pedestrian comfort and safety. Monte Carlo simulations provide insights for urban planners about how travelway width, pedestrian density, and the e-scooter rider’s desired velocity affect pedestrian actual and perceived safety.

The problem of a networked uncertain Euler-Lagrange systems (followers) to track a virtual dynamic leader under asymmetric time-varying communication delays is studied in this paper. It is assumed that the network is a directed spanning tree with the virtual leader as the root. Due to highly nonlinear of Euler-Lagrange systems and communication delays, it is challenging to design a control algorithm for followers to track the moving leader. To cope with the problems, we proposed a distributed cascade control framework which decouples an estimate of the leader velocity in the task space and an adaptive controller in the generalized space. It is verified that the network asymptotically achieves task-space consensus. Simulation results of networked Omni-directional mobile robots are provided to demonstrate the efficacy of the proposed control algorithm.

This article proposed robust control algorithms to cope with dynamic uncertainties in nonlinear bilateral teleoperation system with variable time delays and without relative velocity. With the design of sliding mode control, the robust teleoperator is stable and the solutions are ultimately bounded if the control gains are contingent to the time delays. To tackle the requirement of relative velocity, another control algorithm is presented with the consideration of disturbance to show the robustness of the teleoperation system. Subsequently, the modified robust controller with exponential control terms is addressed to ensure position coordination and convergence of sliding vector in the proposed systems. Simulation and experimental results are illustrated to show the efficacy of the proposed robust controllers for nonlinear teleoperator, and the force information is acquired to demonstrate the performance of force reflection.

This paper addresses the problem of cooperative transportation using multiple omnidirectional Automated Guided Vehicles (AGVs). To enhance flexibility and application potentials, mecanum-wheeled platform is considered for the proposed multi-AGV system while the cooperative transportation is executed without physical link/gripper to fix the object on the AGVs. Therefore, the position and number of AGV is adjustable depending on the size and weight of the transported object. Analysis of force distribution to each AGV during cooperative transportation is presented. Furthermore, the gradient projection method is exploited to regulate internal force according to the operational capability of each AGV. Moreover, an adaptive sliding mode controller is designed for AGV to cope with dynamic uncertainty during cooperative transportation. Stability of the proposed controller is proven by using Lyapunov Theorem. Finally, numerical simulation is presented to demonstrate the performance of the proposed control system.

This paper presents the design, modeling, control, and experimental test of a solar-powered quadcopter to allow for long-endurance missions. We first present the design of a large-scale quadcopter that incorporates solar energy harvesting capabilities. Based on the design results, we built the dynamical model of the customized quadcopter with analysis of the aerodynamic influence. A feedback control system is developed for the solar-powered quadcopter that takes into account the wind disturbance and is verified in virtual simulation examples. All parameters used in the modeling and simulations are based on a developed prototype of the solar-powered quadcopter. Flight tests with the prototype are presented to validate the feasibility and theoretical basis of the solar-powered quadcopter.

In this paper, the problem of consensus for multiagent systems, modeling by Euler-Lagrange equations, is studied with event-triggered communication network. By utilizing a proportional plus damping control scheme, a triggering mechanism is developed so that agents can achieve consensus without continuously exchange their signals. The networked system is demonstrated to be stable with guaranteed consensus performance if the communication topology between agents are undirected and connected. Moreover, a triggering gain is introduced to tune the triggering sensitivity and frequency. The lower bounds of inter-event time for agents are presented so that the Zeno behavior is avoided in the event-triggered control system. The proposed system can reduce network-access-frequency, and stop exchanging output signals when the multi-agent system achieve consensus. Simulations with four Euler-Lagrange agents are illustrated to show the performance and efficiency of the proposed system.

The coverage control problem for multiple mobile robot system with limited and dissimilar sensing abilities is addressed in this article. There exist missing areas while taking the previously developed r-limited Voronoi partitions due to distinct sensing ranges. Thus, a modified partitioning approach is proposed to enhance coverage performance. Furthermore, the dynamic coverage controllers are developed with time-varying density function so that the system is more applicable to various tasks such as target tracking or information decays. In addition to theoretical analysis, numerical examples and experiments with four mobile robots are conducted to verify stability and coverage performance of mobile robot network utilizing the proposed coverage partition algorithm.

A decentralized bilateral teleoperation system is studied in this paper by using estimators with task abstraction. There is only a part of mobile robots in the multi-robot system can receive the human command via the master robot located in the local side. By exchanging information through a undirected and connected graph, mobile robots in the network have to estimate the human command and the global task function for the control of teleoperation. The control framework is more scalable and flexible that the size and formation of mobile robot network can be teleoperated. Stability analysis is presented and experimental results are illustrated to show the efficacy of the performance of the decentralized teleoperation system.

This paper presents a mission planning problem for a cooperative team of unmanned ground vehicles (UGVs), which includes multiple rovers and a solar-powered mobile charging station. The team is required to start at an initial point and visit a series of objective points before arriving at the final point selected from the set of objective points, where the UGVs will be charged from the solar-powered mobile charging station. This mission is represented as a multi-Hamiltonian Path Problem (mHPP). In order to effectively coordinate the team, an understanding of the mission environment is first obtained by generating a scalar field representation of the solar insolation of the environment from a visual-spectrum image. Then, a cascaded heuristic optimization algorithm, using modified genetic algorithm and particle swarm optimization, is used to generate a time-optimized mission plan for the team of UGVs, which guides each UGV to its assigned objective points and then rendezvous at the final charging location while guaranteeing compliance with the net energy gain constraint. The feasibility and efficiency of the proposed algorithm are verified using an experimental testbed and constructed indoor simulation environments.

Plug-in hybrid electric vehicle (PHEV) is a kind of hybrid electric vehicles (HEV) that has a large capacity battery to satisfy the requirement of the distance for commuters. The parallel PHEV has two kinds of power source, internal combustion engine (ICE) and electric motor (EM). To have a superior fuel economy, a control strategy to split the driving power to ICE and EM is important. In this paper, we propose a model predictive control strategy to regulate the individual power from ICE and EM in a parallel PHEV to minimize fuel consumption. The control strategy can improve fuel economy comparing with other strategies, and the proposed strategy also considers the computational burden for real time implement.

This paper presents the experimental validation of a multiple mobile robot system with human-in-the-loop coordination. It has been proposed that the control framework for networked autonomous robots can be augmented by artificial functions for task abstraction, so that a human operator is able to operate a group of robots remotely. By utilizing the redundancy of a multirobot system with task-space control, the group of mobile robots, in addition to achieving the missions tele-controlled by the human, can perform added tasks simultaneously. In this paper, the human-robot cooperative control system was validated experimentally through a robotic manipulator and a group of mobile robots by taking into account communication delays. Multirobot coverage control, formation control, and cooperative transportation were demonstrated, to validate the performance and efficiency of the robotic control system with human-in-the-loop coordination.

This paper presents a control algorithm to guarantee stability and regulation performance for networked robotic systems with communication delay and packet loss. A local controller is developed to transform the robot dynamics so that the robot is passive with respect to an output signal containing position and velocity information. With the utilization of scattering transformation and a passive remote controller (for position regulation), the interconnected robotic system is passive and stable under time delay. Subsequently, we develop a packet management, called Wave-Variable Modulation (WVM), to deal with the proposed networked robotic system under packet loss. The passivity of the communication network can be preserved, and the performance of position regulation is guaranteed by using WVM. Simulations are presented to demonstrate the performance of the proposed control architecture.

This paper addresses the control problem for networked robotic manipulators to achieve task-space synchronization with uncertainties in kinematic and dynamic models. Since most of the previous results focused on such systems over an undirected topology without considering communication delays, in this paper we develop controller for networked robots to achieve synchronization in the presence of time delays. If robots exchange their output signals over a strongly connected topology, then the proposed control system is proven to be stable with guaranteed position and velocity synchronization in task space. Additionally, the synchronization problem is also studied in this paper when the interconnection topology is time-varying with constant delays. Simulation results are presented to demonstrate the performance of the proposed control system.

This paper presents system framework and control algorithm that enable a human operator to simultaneously interact with a group of swarm robots in a remote environment. In this control system, several characteristics of the configuration of the swarm robots are encoded as task functions, for which a human operator can specify desired values that are conveyed to the end-effector of the master robot. Stability and tracking performance of the proposed control system are investigated in the presence of communication delays so that the swarm robots can be manipulated remotely. Moreover, the swarm robots, which perform like a redundant robotic system, can also regulate their position to achieve secondary tasks autonomously. The proposed control algorithms are validated via numerical simulations on a 3-DOF robot manipulator with a group of mobile robots.

In this paper, the problem of set-point control for flexible-joint robotic manipulators with input/output time delays is investigated. By utilizing scattering transformation with an input-output passive controller, it is demonstrated that the flexible-joint robotic control system can be stabilized when there are time delays in the communication channels. Although stabilization is achieved, the flexible-joint robot cannot be regulated to the desired configuration when utilizing the scattering variables. Hence, a new control framework without scattering transformation is subsequently studied in this paper to guarantee both stability and position regulation provided that the control gain is appropriately selected based on a bound on the time delays. The proposed control algorithms are validated via numerical examples on a two-link flexible-joint robotic manipulator.

Passivity-based control has emerged as an important paradigm for synchronization of networked robotic systems. Despite the practical utility of task-space algorithms, the previous results focused on joint-space synchronization and were primarily derived for kinematically identical manipulators. Hence, in this paper, the problem of task-space synchronization of (possibly redundant) heterogeneous robotic systems is studied. By exploiting passivity-based synchronization results that are developed previously, an adaptive control algorithm is proposed to guarantee task-space synchronization of networked robotic manipulators in the presence of dynamic uncertainties and time-varying communication delays. To demonstrate the efficacy of the proposed framework, numerical simulations and experiments are conducted with redundant and nonredundant manipulators, respectively.

Input/output delays in a control system can pose significant impediments to the stabilization problem and potentially degrade the performance of the closed-loop system. In this paper, we study the classical set-point control problem for rigid robots with input-output communication delays in the closed-loop system. We demonstrate that if there are transmission delays between the robotic system and the controller, then the use of the scattering variables can stabilize an otherwise unstable system for arbitrary unknown constant delays. It is also demonstrated that the proposed algorithm results in guaranteed set-point tracking. In the case of time-varying delays, scattering variables together with additional gains can be utilized to stabilize the closed-loop system that is composed of the robotic manipulator and the controller. Furthermore, a scattering representation-based design with position feedback is proposed to improve closed-loop performance under time-varying delays. The proposed algorithms are validated via experiments in this paper.

Bilateral teleoperation systems have been extensively utilized for implementing tasks in remote or hazardous environments. However, due to the cognitive limitations of the human operator, efficient teleoperation of complex robotic system operating in cluttered environments has been difficult to achieve. In this paper, we study the control problem of a semi-autonomous teleoperation system, where the redundant slave robot can autonomously satisfy several constraints while tracking the position of the master robot in the task space. Considering heterogeneous master and slave robots, we first develop a control algorithm to ensure task space position and velocity tracking between the master and slave robots in the presence of dynamic uncertainties and communication delays. The redundancy of the slave robot is then utilized for achieving sub-task control, such as singularity avoidance, joint limits, and collision avoidance. The control algorithms for the proposed semi-autonomous teleoperation system are validated using numerical simulations on a non-redundant master and a redundant slave robot.