Robot Formation Research Articles

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.

Accelerated Transfer Learning for Cooperative Transportation Formation Change via SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization)

A method for cooperative transportation, which required formation change in a traveling environment, is gaining interest. Deep reinforcement learning is used in formation changes for multi-robot cases. The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. On the other hand, re-learning may be required in unrecognized circumstances by using the MADDPG method. Although the development of MADDPG using model-based learning and imitation learning has been applied to reduce learning time, it is unclear how the learning results are transferred when the number of robots changes. For example, in the GASIL-MADDPG (Generative adversarial self-imitation learning and Multi-agent Deep Deterministic Policy Gradient) method, how the results of three robot training can be transferred to the four robots’ neural networks is uncertain. Nowadays, Scaled Dot Product Attention (SDPA) has attracted attention and is highly impactful for its speed and accuracy in natural language processing. When transfer learning is combined with fast computation, the efficiency of edge-level re-learning is improved. This paper proposes a formation change algorithm that allows easy and fast multi-robot knowledge transfer using SDPA combined with MAPPO (Multi-Agent Proximal Policy Optimization), compared to other methods. This algorithm applies SDPA to multi-robot formation learning and performs fast learning by transferring the acquired knowledge of formation changes to a certain number of robots. The proposed algorithm is verified by simulating the robot formation change and was able to achieve dramatic high-speed learning capabilities. The proposed SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) learned 20.83 times faster than the Deep Dyna-Q method. Furthermore, using transfer learning from a three-robot to five-robot case, the method shows that the learning time can be reduced by about 56.57 percent. A scenario of three-robot to five-robot is chosen based on the number of robots often used in cooperative robots.

A novel memetic algorithm for distributed shape formation of swarm robots with both acceleration and velocity constraints

A novel memetic algorithm for distributed shape formation of swarm robots with both acceleration and velocity constraints

Automated Discovery of Efficient Behavior Strategies for Distributed Shape Formation of Swarm Robots by Genetic Programming

Automated Discovery of Efficient Behavior Strategies for Distributed Shape Formation of Swarm Robots by Genetic Programming

Adaptive immune fuzzy quasi-sliding mode control for leader–follower formation of wheeled mobile robots under uncertainties and disturbances with obstacle avoidance

PurposeThis paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.Design/methodology/approachTo deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.FindingsTo verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.Originality/valueThe proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

Approximate optimal and scalable control for collision-free formation of uncertain nonholonomic robots

Approximate optimal and scalable control for collision-free formation of uncertain nonholonomic robots

Fault Detection of Multi-Wheeled Robot Consensus Based on EKF

Synchronizing a network of robots in consensus is an important task for cooperative work. Detecting faults in a network of robots in consensus is a much more important task. In considering a formation of Wheeled Mobile Robots (WMRs) in a master–slave architecture modeled by graph theory, the main objective of this study was to detect and isolate a fault that appears on a robot of this formation in order to remove it from the formation and continue the execution of the assigned task. In this context, we exploit the extended Kalman filter (EKF) to estimate the state of each robot, generate a residual, and deduce whether a fault exists. The implementation of this technique was proven using a Matlab simulator.

Event-Triggered Adaptive Bipartite Secure Consensus Asymptotic Tracking Control for Nonlinear MASs Subject to DoS Attacks

This paper proposes an adaptive bipartite secure consensus asymptotic tracking control scheme based on event-triggered strategy for the nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. First, by incorporating the concept of shortest path into the hierarchical algorithm, the bipartite consensus control problem in the presence of unbalanced communication topology is successfully addressed. Further, an anti-attack bipartite control strategy by designing the improved forms of the tracking errors and virtual controllers is proposed under DoS attacks. Then, a modified event-triggered mechanism based on the relative threshold strategy is proposed, which can ensure that the bipartite consensus tracking errors converge to zero asymptotically by utilizing the Barbalat’s Lemma. Under the Lyapunov stability theory, it is guaranteed that the outputs of all agents can reach agreement with an identical magnitude but opposite signs and all the signals of the closed-loop MASs are bounded. Finally, the simulation results on the model of damping pendulums are given to verify the efficiency of the proposed secure controller. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper, the adaptive bipartite secure consensus asymptotic tracking control problem is considered for nonlinear MASs subject to DoS attacks, whose models can describe many critical applications, such as bidirectional formation of unmanned vehicles and robots. The research on the secure tracking control problem will be rather complicated yet challenging if the DoS attacks of the communication channels are taken into account in the insecure complex network environment. In addition, the event-triggered mechanism is used to reduce the usage of communication resources, which makes the proposed control scheme more easier to implement and more friendly for control engineers.

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.

Attitude and position control for formation flying of space robots equipped with a robotic manipulator

This paper presents a novel six degree-of-freedom guidance and control algorithm for free-flyer robots equipped with a robotic manipulator operating on the International Space Station. The proposed algorithm combines a sliding mode controller for rotational motion and a model predictive controller for translational motion. A novel prediction-twisting sliding mode controller (PT-SMC) is introduced to stabilize configuration changes induced by the manipulator and correlated disturbances and to achieve a trade-off among control law flexibility, robustness, and accuracy. The proposed PT-SMC is shown to have improved stability compared to a first-order sliding mode controller. Model predictive control is investigated as a position controller to achieve formation flight between multiple free flyers in a constrained environment, and a leader–follower approach is used to move a triangular formation of robots. A control allocation problem is solved to address the subdivision of attitude and position controls between twelve thrusters. Simulation results of multiple robots approaching a target object demonstrate the improved performance of the proposed control approaches.

Consensus-Based Formation Control with Time Synchronization for a Decentralized Group of Mobile Robots.

The development and study of an optimal control method for the problem of controlling the formation of a group of mobile robots is still a current and popular theme of work. However, there are few works that take into account the issues of time synchronization of units in a decentralized group. The motivation for taking up this topic was the possibility of improving the accuracy of the movement of a group of robots by including dynamic time synchronization in the control algorithm. The aim of this work was to develop a two-layer synchronous motion control system for a decentralized group of mobile robots. The system consists of a master layer and a sublayer. The sublayer of the control system performs the task of tracking the reference trajectory using a single robot with a kinematic and dynamic controller. In this layer, the input and output signals are linear and angular velocity. The master layer realizes the maintenance of the desired group formation and synchronization of robots during movement. Consensus tracking and virtual structure algorithms were used to implement this level of control. To verify the correctness of operation and evaluate the quality of control for the proposed proprietary approach, simulation studies were conducted in the MATLAB/Simulink environment, followed by laboratory tests using real robots under ROS. The developed system can successfully find application in transportation and logistics tasks in both civilian and military areas.

Maintaining and steering a formation in an unknown dynamic environment via a consistent distributed dynamic map

AbstractIn this paper, we study the problem of maintaining a stable mobile robot formation, steering and localizing all robots in an unknown dynamic environment consisting of multiple periodically moving objects, without the presence of a global positioning system or a robot tracking system. We propose a distributed observer such that each agent can estimate global positions of all mobile robots and that of moving landmarks in an unknown environment. By combining the proposed distributed observer with the distributed formation control and centroid tracking control law, we show that the formation shape can be maintained by utilizing its available relative measurements and the estimated relative measurements to its neighbors, and the group's centroid follows a desired trajectory. We present stability analysis of the closed‐loop system. Finally, we validate the proposed methods in a simulation result where a group of mobile robots can maintain a robust formation and maneuver in an unknown dynamic environment.

Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via Modified Artificial Potential Field and Improved PSO Algorithm

This paper addresses two substantial aspects in the field of robotics: trajectory planning and trajectory tracking for multi-robot systems. The collective movement of the multi-wheeled mobile robots is based on a formation control with a leader–follower strategy. To ensure safe navigation toward the destination within a workspace containing multiple obstacles, we skillfully combine the Modified Artificial Potential Field (MAPF) method and the Improved Particle Swarm Optimization (IPSO) algorithm for a group of nonholonomic mobile robots. A global planner based on IPSO algorithm is assigned to the leader robot, to find the optimal collision-free path. Compared to recent nature-inspired methodologies, the improvement suggested enhance the search capabilities of the algorithm, resulting in a 2% reduction in path length and a 29% decrease in computation time. Simultaneously, the follower robot has the ability to employ either a formation policy or a local planner using MAPF. The proposed modifications address the primary limitations of the conventional method, notably the susceptibility to local minima and the challenge of reaching goals near obstacles. Furthermore, this local planner is adept at evading both static and dynamic obstacles. Extensive real hardware experiments are conducted within multiple scenarios using the mobile robot Qbot-2e to corroborate the obtained simulation results and validate the effectiveness of the proposed approaches both in tracking the desired trajectories, finding the optimal feasible collision-free path and avoiding static and dynamic obstacles for multi-robot systems.

An improved dynamic window approach algorithm for dynamic obstacle avoidance in mobile robot formation

Dynamic Window Approach (DWA) algorithm is a commonly used choice in dynamic obstacle avoidance. However, the traditional DWA algorithm evaluation function is poorly adapted to dynamic obstacle avoidance and has defects in efficiency and safety. In this paper, we consider the speed and heading of the mobile robot formation and the speed and heading of the obstacles and design the speed-varying obstacle avoidance and safety distance evaluation coefficients. The objective is to design an improved DWA algorithm to improve the obstacle avoidance ability of mobile robot formations when encountering dynamic obstacle interference. First, the obstacle environment in which the mobile robot formation is located is analyzed to determine the obstacles that threaten the formation and those that are not. Second, the obstacle velocity space is evaluated and analyzed using the velocity change evaluation coefficient so that the robot formation’s obstacle avoidance behavior after the combination of angular and linear travel velocities has high robustness in the face of dynamic obstacle interference. Finally, the evaluation coefficient of safe obstacle avoidance distance is designed to accurately judge the positional relationship between the robot formation and dynamic obstacles at the moment of obstacle avoidance, which maximizes the safety of the robot formation traveling. The experimental results show that the improved algorithm shortens the obstacle avoidance time by 37.3% and saves 16.8% of the distance traveled than the traditional algorithm. It also achieves good results in terms of robustness and safety.

Hydrodynamic analysis of fin–fin interactions in two-manta-ray schooling in the vertical plane

This study investigates the interaction of a two-manta-ray school using computational fluid dynamics simulations. The baseline case consists of two in-phase undulating three-dimensional manta models arranged in a stacked configuration. Various vertical stacked and streamwise staggered configurations are studied by altering the locations of the top manta in the upstream and downstream directions. Additionally, phase differences between the two mantas are considered. Simulations are conducted using an in-house developed incompressible flow solver with an immersed boundary method. The results reveal that the follower will significantly benefit from the upstroke vortices (UVs) and downstroke vortices depending on its streamwise separation. We find that placing the top manta 0.5 body length (BL) downstream of the bottom manta optimizes its utilization of UVs from the bottom manta, facilitating the formation of leading-edge vortices (LEVs) on the top manta’s pectoral fins during the downstroke. This LEV strengthening mechanism, in turn, generates a forward suction force on the follower that results in a 72% higher cycle-averaged thrust than a solitary swimmer. This benefit harvested from UVs can be further improved by adjusting the phase of the top follower. By applying a phase difference of π/3 to the top manta, the follower not only benefits from the UVs of the bottom manta but also leverages the auxiliary vortices during the upstroke, leading to stronger tip vortices and a more pronounced forward suction force. The newfound interaction observed in schooling studies offers significant insights that can aid in the development of robot formations inspired by manta rays.

Cooperative Output Regulation of MASs With a Novel Parameter Convergence Condition and Prescribed-Time Event-Triggered Observers and Its Application to Robot Formation

Cooperative Output Regulation of MASs With a Novel Parameter Convergence Condition and Prescribed-Time Event-Triggered Observers and Its Application to Robot Formation

Design and Implementation of a Model Predictive Formation Tracking Control System for Underwater Multiple Small Spherical Robots

Due to the characteristics of good concealment ability and strong mobility, multiple, small spherical underwater robot formations play an important role in near coast defense missions, such as cruising, reconnaissance, surveillance, and sensitive target capturing. Referring to the formation problem for underwater small spherical robots with limited energy, perception, and computation abilities, a trajectory tracking-based formation strategy that transforms the complex formation tracking problem into a simple trajectory tracking problem of a single robot is provided. Two layers are designed in the formation tracking strategy. The upper layer is a virtual structure-based formation algorithm. The bottom layer is a tracking controller based on model predictive control (MPC). The formation algorithm is in charge of calculating reference trajectory for each robot in the formation according to the global formation path. The MPC-based dynamic controller for each robot is designed to track the self reference trajectory. Compared with the model predictive control method used for the traditional trajectory tracking problem of a single robot, this paper additionally considers the formation constraints and the internal collision avoidance. In addition, the extended state observer (ESO) is utilized to estimate the lumped disturbance composed of environment disturbance and the inaccurate dynamic model of a small spherical robot. Not only are the numerical simulations based on MATLAB v.2015a, but physical simulations based on self-building multi-spherical robot formation platform are also carried out. Furthermore, through using two small spherical robots, a formation tracing experiment is conducted. All of the results prove that the proposed formation method is feasible and practical for small spherical robots.

Robust Cooperative Transport System with Model Error Compensator Using Multiple Robots with Suction Cups

In cooperative transport systems, multiple robots work together to transport objects that are difficult to transport with a single robot. In recent years, multi-robot systems that cooperate to transport objects have been researched. However, during the transfer of objects, misalignment occurs between the ideal and actual grasp positions. In an automatic transport system, a grasping error can cause an error in the trajectory of the object, significantly reducing the transport efficiency. In this paper, a control system that allows robust cooperative transport control using a model error compensator is proposed for a leader–follower system in which the transported object is the virtual leader and the followers are ideally arranged. This system adds robustness to the operation of a conventional cooperative transport system by using the ideal formation of robots. The effectiveness of the proposed method was evaluated through cooperative transport experiments using two ideal formations for passing through a narrow entrance. The cooperative transport system could not pass through the narrow entrance using the conventional method; however, the system using the compensator passed through the narrow entrance smoothly.

O tochnykh resheniyakh mnogomernoy sistemy ellipticheskikh uravneniy so stepennymi nelineynostyami

Equations and systems of elliptic type with power-law nonlinearities are considered.Such equations are found in modeling distributed robotic formations, as well as in chemical kinetics,biology, astrophysics, and many other fields. The problem of constructing multidimensionalexact solutions is studied. It is proposed to use a special type of ansatz that reduces the problemto solving systems of algebraic equations. A number of multiparameter families of new exactmultidimensional solutions (both radially symmetric and anisotropic) represented by explicitformulas are obtained. Examples are given to illustrate the exact solutions found.

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.