Formation Control Of Mobile Robots Research Articles

Formation control for multiple nonholonomic wheeled mobile robots based on integrated sliding mode controller and force function

In this paper, we present a novel method to achieve collision-free formation control for multiple nonholonomic wheeled mobile robots. By utilizing the leader–follower strategy, an integrated controller that combines a sliding mode controller for formation control and a force function for obstacle avoidance is proposed. To address the common chattering issue associated with sliding mode control, a novel depletion model is developed, which constrains the controller input to vary smoothly within a small range and effectively eliminates chattering. Additionally, a dynamic-gain structure is introduced to reconcile the conflicting demands of formation control and obstacle avoidance, providing enhanced coordination between the two tasks. Simulation and experiments’ results are provided to illustrate the effectiveness of the proposed method.

A robust distributed model predictive control strategy for Leader–Follower formation control of multiple perturbed wheeled mobile robotics

This article studies the formation and trajectory tracking control of multiple mobile robots with kinematic sub-systems. We proposed an effective design of robust distributed model predictive control (MPC) strategy for Leader–Follower formation control scheme in a group of multiple perturbed Wheeled Mobile Robotics (WMRs) with the consideration of tracking performance in not only the position but also the orientation, as well as the distance between the center and head in each WMR. Furthermore, the relation between formation control objective and non-holonomic property in each agent is also discussed. For the purpose of achieving the desired formation, according to trajectory of leader WMR, the barycentric of the formation requirement is known as corresponding virtual followers, and a distributed tube-MPC scheme is applied to each follower WMR for tracking a reference trajectory with not only the position but also its orientation. In addition, the stability and the performance tracking of multiple perturbed WMRs are investigated by employing Lyapunov stability theory with the indirect comparison method to be implemented by pointing out precisely the proposed terminal controller and the equivalent terminal region. Comprehensive simulation results in several scenarios demonstrate the validity of the proposed control scheme.

Formation control of networked mobile robots with weighted directed topology using the Udwadia–Kalaba approach

Formation control of networked mobile robots with weighted directed topology using the Udwadia–Kalaba approach

Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints

This paper addresses the problem of distributed consensus-based formation control for wheeled mobile robots (WMRs) under the influence of mixed disturbances, including both random noise and non-random disturbances. A consensus formation auxiliary subsystem is constructed based on the leader’s position estimated by the distributed estimator. A formation tracking subsystem for each robot is constructed based on the trajectory tracking error method. The above two subsystems are constructed into an extended formation modeling system. Further, a distributed model predictive control (DMPC) is designed to control this system without disturbance, and the controller is solved by means of a general-purpose neural network. A combination of Kalman filter (KF) and extended state observer (ESO) is intended to reduce the effect of both non-random disturbances and random noise, hence increasing the controller’s resilience to disturbances. Moreover, a composite control law is designed to ensure the controller’s effectiveness. Finally, simulation results demonstrate that the proposed control strategy is well-suited to addressing the problem, as it not only achieves accurate formation control but also effectively regulates the robot’s physical constraints while suppressing both non-random disturbances and random noise.

Semi-decentralized Lyapunov-based formation control of multiple omnidirectional mobile robots

<p>This paper introduces an advanced formation control algorithm based on a Lyapunov approach for coordinating multiple omnidirectional mobile robots in collaborative object transport tasks. The semi-decentralized strategy ensures that the robots maintain a predefined geometric formation, crucial for stability during material transportation, and dynamically adapt to avoid collisions using onboard sensors. Experimental with a physical robot simulator demonstrates successful maintenance of line and triangle formations achieving an average side length maintenance of 1.00 meters with minimal deviation. Quantitative analysis across 30 experimental runs reveals consistent performance, with a maximum side length fluctuation of only 2 centimeters, validating the effectiveness of maintaining formation within a multi-robot system (MRS) framework. The Lyapunov-based approach proves to be an efficient method for cooperative object transport, achieving consistent performance with minimal deviation.</p>

Switching-Based Distributed Adaptive Secure Formation Control for Mobile Robots With Denial-of-Service Attacks

Switching-Based Distributed Adaptive Secure Formation Control for Mobile Robots With Denial-of-Service Attacks

Optimal trajectory tracking of uncertain nonlinear continuous-time strict-feedback systems with dynamic constraints

A novel method for optimal trajectory tracking of uncertain nonlinear systems by using adaptive dynamic programming (ADP)-based integral reinforcement learning (IRL) and neural networks (NNs) is proposed. The method utilises an actor-critic framework with optimal backstepping to minimise a discounted value function and uses a single-layer NN identifier for estimating the unknown dynamics. For safety assurance, a time-varying barrier Lyapunov function (TVBLF) is used in the control design to handle the constraints. A novel weight-tuning law by using the control input error, integral Bellman error, and the NN identifier is used for the actor-critic framework. A novel lifelong learning (LL)-based method for critic NN is utilised in an optimal framework by incorporating the Bellman error to mitigate catastrophic forgetting in multitasking scenarios. Stability analysis using Lyapunov stability theory is obtained for the overall closed-loop system. Simulations of leader-follower mobile robot formation control show a 25% reduction in the cost in multitasking scenarios.

Embedded technique-based formation control of multiple wheeled mobile robots with application to cooperative transportation

The cooperative transportation problem for multiple wheeled mobile robots (WMRs) via formation control is investigated in this paper. A formation control algorithm based on an embedded technique is proposed for cooperative transportation of a shared object using n-WMRs. Instead of relying on the conventional design philosophy directly based on formation errors, the proposed algorithm is divided into two parts by considering the communication topology and WMRs’ dynamics “separately”. The first part involves a distributed signal generator that generates desired trajectories for the WMRs based on their initial positions, the formation vector, and the desired trajectory of the object. The second part consists of tracking controllers to enable the WMRs to track their desired trajectories. The proposed algorithm is distributed and differs from the existing cooperative transportation algorithms, as it eliminates the requirement for all WMRs to know the object’s position. Moreover, it exhibits remarkable compatibility and features a concise modular design. With the proposed algorithm, WMRs achieve formation in finite time. Theoretical proof supports the effectiveness of the proposed algorithm, which is further validated through several conducted experiments.

Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing

People use natural language to express their thoughts and wishes. As robots reside in various human environments, such as homes, offices, and hospitals, the need for human–robot communication is increasing. One of the best ways to achieve this communication is the use of natural languages. Natural language processing (NLP) is the most important approach enabling robots to understand natural languages and improve human–robot interaction. Also, due to this need, the amount of research on NLP has increased considerably in recent years. In this study, commands were given to a multiple-mobile-robot system using the Turkish natural language, and the robots were required to fulfill these orders. Turkish is classified as an agglutinative language. In agglutinative languages, words combine different morphemes, each carrying a specific meaning, to create complex words. Turkish exhibits this characteristic by adding various suffixes to a root or base form to convey grammatical relationships, tense, aspect, mood, and other semantic nuances. Since the Turkish language has an agglutinative structure, it is very difficult to decode its sentence structure in a way that robots can understand. Parsing of a given command, path planning, path tracking, and formation control were carried out. In the path-planning phase, the A* algorithm was used to find the optimal path, and a PID controller was used to follow the generated path with minimum error. A leader–follower approach was used to control multiple robots. A platoon formation was chosen as the multi-robot formation. The proposed method was validated on a known map containing obstacles, demonstrating the system’s ability to navigate the robots to the desired locations while maintaining the specified formation. This study used Turtlebot3 robots within the Gazebo simulation environment, providing a controlled and replicable setting for comprehensive experimentation. The results affirm the feasibility and effectiveness of employing NLP techniques for the formation control of multiple mobile robots, offering a robust and effective method for further research and development on human–robot interaction.

Distributed Leader Follower Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter

This paper investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method with proof of its stability. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. Additionally, considering the system and measurement noises, the proposed control strategy not only removes the chattering issue existing in the conventional sliding mode control but also provides smooth control input with extra robustness. After that, an adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates that are robust to modeling uncertainties. Finally, we performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.

Fixed-time sliding mode estimator-based distributed formation control for multiple nonholonomic mobile robots

This paper proposes a fixed-time estimator-based distributed formation controller for a system consisting of multiple nonholonomic mobile robots, taking into consideration the stability of individuals and the effectiveness of internal communication. The desired trajectory of the formation is given by the virtual leader whose state is available only to some followers, and the information interaction within the followers is localized. To begin with, a distributed fixed-time sliding mode estimator is designed for each follower to estimate the state information of the virtual leader within a fixed time. Subsequently, based on the estimator, a formation controller is designed according to the trajectory tracking error of each follower. In addition, the Lyapunov function is constructed for stability analysis. Finally, simulation experiments are conducted in MATLAB/Simulink to demonstrate the effectiveness of the proposed estimator and controller, and the robustness of the control system is further verified by combining the actual situation of communication interruption and external disturbance.

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

Fixed-Time Formation Control of Multiple Wheeled Mobile Robots for Circumnavigating a Moving Target

Fixed-Time Formation Control of Multiple Wheeled Mobile Robots for Circumnavigating a Moving Target

Global Consensus-Based Formation Control of Nonholonomic Mobile Robots With Time-Varying Delays and Without Velocity Measurements

We propose a solution to the full consensus-based formation problem for torque-controlled nonholonomic mobile robots under time-varying communication delays and without velocity measurements. Under the assumptions of static and undirected communication topologies, and smooth and bounded delays, we propose a distributed smooth, time-varying control law which achieves the goal of acquiring a global formation pattern in a common consensus point for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">both</i> position and orientation. The controller is a combination of a simple Proportional-Plus-Damping scheme and a simple globally exponentially convergent velocity observer. Consensus is reached independently of all initial conditions. Realistic simulations in the Gazebo-ROS environment illustrate the effectiveness of the proposed algorithm and the robustness with respect to measurement noise, non-smooth delays and uncertain dynamics.

Informative Cooperative Path Planning and Time-varying Formation Control of Heterogenous Mobile Robots

Informative Cooperative Path Planning and Time-varying Formation Control of Heterogenous Mobile Robots

A Leader-follower formation control of mobile robots by position-based visual servo method using fisheye camera

This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots.

A novel hedge algebra formation control for mobile robots

Hedge algebra is gaining increasing attention due to its high efficiency and fast calculation speed compared to fuzzy logic. This paper proposes the first algebra-based formation controller with a leader–follower approach to maintain the desired formation of multiple robots. In each time step, the controller computes the desired positions of the followers and strives to minimize the distance between them and their desired positions. We evaluate the performance of this controller on our robots using ROS in a virtual environment with Gazebo. Our work proves hedge algebra’s significant potential in the mobile robots’ domain, specifically in formation control. HAFC allows not only the precise formation of a robot swarm into any desired formation, with minimal deviation but also adaptive transition to the different formations. Our results show that HAFC can effectively maintain the desired formation of multiple robots, even in certain extraordinary situations. Furthermore, HAFC exhibits higher efficiency than the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robot has to move in formation and change formation accordingly with the terrain.

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

Robustness in formation control of mobile robots using leader-follower method

This paper presents the robustness in formation control of multiple mobile robots using leader-follower method. The uncertainty considered is the measured error which is included in the relative state. The robust stability conditions against the relative state error are derived by Lyapunov’s stability theory (direct method). We also obtain the formation steady-state deviation when formation stability is ensured. The formation control environment is constructed on Simulink. The validity of the stability condition and the steady-state deviation is demonstrated by numerical simulation. It is seen that the L-F method provides a robust control law against the relative state error although large formation steady-state deviation is occurred in some cases.

Lifelong learning‐based multilayer neural network control of nonlinear continuous‐time strict‐feedback systems

AbstractIn this paper, we investigate lifelong learning (LL)‐based tracking control for partially uncertain strict feedback nonlinear systems with state constraints, employing a singular value decomposition (SVD) of the multilayer neural networks (MNNs) activation function based weight tuning scheme. The novel SVD‐based approach extends the MNN weight tuning to layers. A unique online LL method, based on tracking error, is integrated into the MNN weight update laws to counteract catastrophic forgetting. To adeptly address constraints for safety assurances, taking into account the effects caused by disturbances, we utilize a time‐varying barrier Lyapunov function (TBLF) that ensures a uniformly ultimately bounded closed‐loop system. The effectiveness of the proposed safe LL MNN approach is demonstrated through a leader‐follower formation scenario involving unknown kinematics and dynamics. Supporting simulation results of mobile robot formation control are provided, confirming the theoretical findings.