Formation Control Of Mobile Robots Research Articles

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

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

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.

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

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

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.

Distributed formation control of networked mobile robots from the Udwadia–Kalaba approach

AbstractIn this article, formation control of networked mobile robots is studied. Two control strategies for the leaderless case and the leader‐following case are developed from the Udwadia–Kalaba approach, respectively. Differing from the existing results about formation control, which focus on kinematic models of robots, this article considers dynamic models and obtained a lower‐cost control force. To improve the robustness of the communication network, two distributed formation controllers are designed by establishing distributed estimators. Different from the centralized controllers, the distributed controllers established here only require local information, thus save communication resources and improve system robustness. Finally, some numerical simulations are provided to demonstrate the proposed methods are effective and have lower control cost.

Velocity‐free formation control for omnidirectional mobile robots with input saturation

Velocity‐free formation control for omnidirectional mobile robots with input saturation

Distributed Prescribed Performance Formation Control for Nonholonomic Mobile Robots Under Noisy Communication

Distributed Prescribed Performance Formation Control for Nonholonomic Mobile Robots Under Noisy Communication

Prescribed-time formation control for wheeled mobile robots with time-varying super-twisting extended state observer

This article studies the prescribed-time formation control for wheeled mobile robots within the leader-following consensus framework under directed topology. A time-varying prescribed-time super-twisting extended state observer is constructed for each robot to estimate the total disturbance of its dynamics. Different from the traditional active disturbance rejection control, the total disturbance is allowed to contain some nonsmooth component. Based on it, a formation control method with prescribed-time convergence is developed for the follower to realize the formation. The effectiveness of the proposed approach is demonstrated through two experiments.

Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formations

Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

Decentralized Circular Formation Control of Nonholonomic Mobile Robots Under a Directed Sensor Graph

This paper investigates the circular formation control problem of multiple unicycle-type mobile robots under a directed sensor graph. The topology of the sensor graph among all robots is described by a directed graph containing a spanning tree, and the node denoting the center is only required to be globally reachable in the sensor graph. A dynamic control law is developed such that the multi-robot systems with speed constraint are able to globally converge to a circular formation prescribed by a stationary center, a radius, and a spacing configuration. Remarkably, the proposed control law does not rely on any communication, and only requires each robot to use local measurements with respect to its neighbors based on its local coordinate frame, which makes it feasible to implement this control law in a decentralized manner. The global asymptotic stability of the closed-loop multi-robot systems is established by small gain theorem of which the small-gain condition is guaranteed by a low gain parameter in the designed observer.

Formation Control of Non-Holonomic Mobile Robots: Predictive Data-Driven Fuzzy Compensator

A key research topic in the field of robotics is the formation control of a group of robots in trajectory tracking problems. Using organized robots has many advantages over using them individually, such as efficient use of resources, increased reliability due to cooperation, and better resistance against defects. To achieve this, a controller is proposed that steers the leader robot and subsequent follower robots asymptotically to a reference trajectory. The basic controller is feedback linearization. To ensure stability against perturbations, a compensator based on type-3 fuzzy logic systems (T3-FLSs) and a data-driven control strategy is designed. The approach involves employing a finite number of open-loop data and using the model-based predictive controller (MPC) approach to acquire sufficient criteria for stability. An infinite-horizon function is minimized online, which allows the data-based control policy to be considered the optimal control method. The gains of the constrained data-based control signal are computed at each time step to enhance accuracy. Applying the data-based state feedback controller to the system yields positive and stable state trajectories with appropriate transient responses. The suggested data-driven compensator is guaranteed to handle constraints. A practical example is simulated to evaluate the proposed strategy.

Cooperative Control of Multirobot Systems Subject to Control Gain Uncertainty

This article addresses the problem of cooperative control of double-integrator type multirobot systems. Different from some conventional results with the control gain explicitly known, the control gain in this article is subject to uncertainty. Three different collective behaviors are explored, i.e., leaderless consensus, leader-following consensus, and formation control. For leaderless consensus, a sliding variable is constructed, based on which a novel continuous controller is designed such that the sliding surface is reached in finite-time and thus the state agreement of all agents is realized. For leader-following consensus, two different cases are investigated, i.e., the leader with constant velocity and with time-varying velocity. In both cases, sliding-mode based controllers are developed and corresponding stability conditions are established to ensure that the leader state is tracked by all followers. Finally, the theoretical results are applied to achieve formation control of nonholonomic mobile robots and corresponding experimental studies are conducted to demonstrate the effectiveness of the proposed controllers.

Velocity‐free formation control for omnidirectional mobile robots with input saturation

AbstractThis paper presents a command‐filter‐based formation control scheme for the leader‐follower omnidirectional mobile robots (OMRs) subject to the absence of the velocity measurements, input saturation and unknown bounded disturbances. First, an adaptive velocity observer is designed based on a novel state transformation of the OMR dynamics. Then, a finite‐time command filtering strategy is proposed, where the Levant filter is used to estimate the time derivative of the virtual control law, and an error compensator with finite time convergence is constructed to eliminate the filtering error correspondingly. Moreover, the anti‐saturation module and adaptive law are designed to attenuate the influence caused by input saturation and external disturbances. The proposed control scheme can ensure that all signals of the closed‐loop system including the formation error are uniformly bounded, and the effectiveness is verified by the simulations results.