Control Of Multiple 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.

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.

Formation Control of Multiple Mobile Robots Based on Iterative Learning Distributed Model Predictive Control

Formation Control of Multiple Mobile Robots Based on Iterative Learning Distributed Model Predictive Control

Formation Control of Multiple Mobile Robots Based on Fuzzy Adaptive Sliding Mode Control

In order to solve the problems of slow convergence and chattering in formation control of multiple mobile robots, and to optimize the dynamic characteristics of formation system, a hybrid adaptive controller is proposed by analyzing the characteristics of different sliding mode reaching laws, which can accelerate the convergence speed and effectively suppress the chattering of the system. Firstly, the saturation function is introduced into the double power reaching law to form the saturation double power reaching law; Secondly, the hyperbolic tangent reaching law is used to improve the stability of the system when it approaches the sliding surface; Finally, fuzzy logic rules are used to adjust the coefficients of saturated double-power reaching law to optimize the overall dynamic parameters. Simulation results show that the designed controller can improve the convergence speed of the system, effectively suppress the chattering, and realize the fast and stable following of the follower robot to the pilot robot, which verifies the correctness and availability of the proposed controller.

Multimobile Robot Cooperative Localization Using Ultrawideband Sensor and GPU Acceleration

To tackle the poor localization accuracy of multimobile robots caused by non-line-of-sight (NLOS) errors in a complex indoor environment and to meet the real-time requirement, this article proposes a multimobile robot cooperative localization system using ultrawideband (UWB) sensor and GPU hardware acceleration. First, a UWB multinode ranging network is established to obtain the relative distance information between robots and anchors. Then, the line-of-sight (LOS) and NLOS errors in distance information are effectively mitigated by using the proposed UWB ranging error mitigation algorithm based on the Bayesian filter. A cooperative particle filter (PF) localization algorithm based on the Gibbs sampling is designed to estimate the position information of each robot at any time. Finally, in order to improve the real-time performance of the collaborative localization system, a parallel Gibbs collaborative localization algorithm that can be accelerated by GPU is proposed considering the characteristics of GPU hardware and CUDA programming model. The experimental results of three TurtleBot2 mobile robots in real scene show that the proposed multimobile robot cooperative localization system using UWB technology can estimate the position information of each robot robustly and accurately, and the localization accuracy is superior to that of the popular extended Kalman filter (EKF) and PF algorithms. It is shown through further evaluations that the proposed parallel algorithm achieves about 3.2 times acceleration effect in the scenarios of three mobile robots. The speed gain is found more significant with more robots, which substantially improves the real-time performance of the cooperative localization system. In the test with seven mobile robots, the speedup is as high as 11.9, that is, the execution time of the algorithm is only 8.39% of that of the original algorithm. Note to Practitioners—The purpose of this article is to improve the accuracy and real-time indoor multimobile robot cooperative localization, but the method proposed in this article is also applicable to outdoor multimobile robot cooperative localization. The existing methods for indoor cooperative localization of mobile robots usually use Bluetooth, infrared, RFID, and other technologies to establish a wireless sensor network (WSN) and then combine Karman filter or particle filter (PF) to achieve cooperative localization, which is difficult to achieve low-cost and high-precision real-time localization. In this article, a new method of cooperative localization is proposed, which uses ultrawideband (UWB) ranging network with high penetration and high precision to obtain accurate distance information, then weakens NLOS error by the Bayesian filtering to further improve the accuracy of distance information, and, finally, uses a novel cooperative localization approach to realize fast and high-precision indoor multimobile robot localization. In addition, by redesigning the collaborative localization algorithm in parallel, the real-time performance of the algorithm is improved while ensuring high accuracy. The collaborative localization experiment of three mobile robots in the real scene shows that the proposed algorithm can effectively improve the localization accuracy, and the real-time performance of collaborative localization is significantly improved. However, the algorithm still has some limitations when mitigating UWB ranging errors in highly obstructed environments, and the algorithm parallelization framework can be further improved for higher real-time performance. In the future, we will further improve the robustness of cooperative localization and apply this algorithm to cooperative control of multiple mobile robots, such as formation control and cooperative search.

A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. The robust performance objective minimization is applied to replace the quadratic performance objective minimization to establish the optimization problem, where the model uncertainty is considered in the distributed system. The control policy is derived by applying the data-driven strategy, and the future predictive value is obtained by employing the linear law in the historical data. Lyapunov theory is referred to analyze the stability of the mobile robot formation system. The effectiveness of the proposed method is proved by a set of simulation experiments.

Cooperative Target Enclosing Control of Multiple Mobile Robots Subject to Input Disturbances

This paper investigates the cooperative target enclosing of multiple unicycle-type mobile robots subject to input disturbances. The objective is to make all robots orbit around a given stationary target, and maintain evenly spaced along a common circle. The network of the multirobot systems is set in a cyclic pursuit manner. A dynamic control law is developed for the cooperative target enclosing of the multirobot systems, while tackling the heterogeneous input disturbances generated by linear exogenous systems. The proposed control law requires each robot to use the relative displacement measurements with respect to the target and its neighbors. It is shown that global asymptotic stability of the closed-loop multirobot systems can be guaranteed in the presence of a large class of input disturbance signals. Finally, simulation results illustrate the effectiveness of our approach.

Robust Vision-Based Tube Model Predictive Control of Multiple Mobile Robots for Leader–Follower Formation

Generally, vision-based controls use various camera sensors and require camera calibration, while the control performance would degrade due to inaccuracy calibration. Therefore, in this paper, the proposed controller only makes use of the image information from an un-calibrated perspective camera mounted at the follower robot without relative position measurement or any communication among the robots. First, the nominal visual formation kinematic model is developed using the camera models. Then it is redescribed as a quadratic programming (QP) with the specified constraints. A neurodynamic optimization based on primal-dual neural network is utilized to ensure the QP being converged to the exact optimal values. Through two-time-scale neuro-dynamical optimization, the gain scheduling of the ancillary state feedback can be realized so that the state variables are constrained within an invariant designed tube. The experiment results provide the verification for the effectiveness of the proposed approach.

Lyapunov and Sliding Mode Based Leader-follower Formation Control for Multiple Mobile Robots with an Augmented Distance-angle Strategy

In this paper, a new two-layer control strategy which combines Kinematic Controller based on Lyapunov Theory (KCLT) with Dynamic Controller based on Sliding Mode (DCSM) is proposed to solve the problem of leader-follower formation control for multiple wheeled mobile robots (M-WMR). An augmented distance-angle leader-follower formation kinematic is constructed to describe the formation states, and a 2D LiDAR sensor is utilized to measure the states instead of using camera and image processing on each follower. Instead of transferring the measured formation states into reference position command for each follower, KCLT is designed to generate follower’s velocity command. By taking the velocity command of followers as reference signals, DCSM is implemented to realize formation control. Lyapunov stability theory verifies that with the designed controller all the error signals can converge to 0 theoretically, which implies formation control of M-WMR under the proposed method can be realized. Real experiments with one leader and two followers are carried out to demonstrate the effectiveness of the proposed control schema. In order to verify the robustness of the proposed method, the reference rotational velocity of the leader robot is designed to change between +0:2 rad/s and -0:2 rad/s at some specified position. And the experimental results are compared with that of traditional proportional-integral-derivative (PID) method.

Formation Control of Multiple Mobile Robots Incorporating an Extended State Observer and Distributed Model Predictive Approach

This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for formation system according to the tracking error dynamic model, where the reference paths are parameterized. A local distributed controller is designed by using DMPC strategy for each mobile robot in the absence of disturbance by including parameter synchronization constraints in the quadratic performance index as coupling terms. The DMPC optimization problem is solved by using Nash-optimization iteration strategy with the maximum number of iteration constraint. To improve the ability of anti-jamming, a feedforward compensation controller is designed by using ESO method, where the ESO is designed by pole assignment. The convergence of the proposed iterative algorithm is given. Furthermore, the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed for the closed-loop system. Finally, the validity of the proposed algorithm is verified by two simulation examples.

Formation control of multiple mobile robots and large obstacle avoidance

Formation control of multiple mobile robots and large obstacle avoidance

Experimental Validation of Distributed Cooperative Control of Multiple Mobile Robots via Local Information Exchange

In this paper, we present the experimental testing results of distributed cooperative control algorithms for multiple mobile robots with limited sensing/communication capacity and kinematic constraints. Rendezvous and formation control problems are considered, respectively. To deal with the inherent kinematic constraints with robot model, the input/output linearization via feedback is used to convert the nonlinear robot model into a linear one, and then the distributed cooperative control algorithms are designed via local information exchange among robots. Extensive experiments using Quanser's QBot2 mobile robot platforms are conducted to validate the effectiveness of the proposed distributed cooperative control algorithms. Specifically, the robot's onboard Kinect vision sensor is applied to solve the localization problem, and the information exchange is done through an ad-hoc peer-to-peer wireless TCP/IP connection among neighboring robots. Collision avoidance problem is also addressed based on the utilization of fuzzy logic rules.

Distributed Receding Horizon Coverage Control for Multiple Mobile Robots

This paper presents a distributed coverage control scheme based on receding horizon control for the coordination of multiple mobile robots to cover an event. The mission space is modeled using a probability density function representing the probability of occurrence of events. The distributed scheme is generated by the decomposition of a single optimal coverage problem into distributed receding horizon coverage control problems, each of them associated with one robot. The distributed coverage scheme is proven to optimally stabilize robots at a centroidal Voronoi configuration, which is an optimal configuration to cover an event. The control scheme is tested in three simulation environments to illustrate its good performance at environments with any probability density function of events, as well as its ability to generalize to much larger groups of mobile robots.

Leader–Follower Formation and Tracking Control of Mobile Robots Along Straight Paths

We address the problem of tracking control of multiple mobile robots advancing in formation along straight-line paths. We use a leader–follower approach, and hence, we assume that only one swarm leader robot has the information of the reference trajectory. Then, each robot receives information from one intermediary leader only. Therefore, the communications graph forms a simple spanning directed tree. As the existence of a spanning tree is necessary to achieve consensus, it is the minimal configuration possible to achieve the formation-tracking objective. From a technological viewpoint, this has a direct impact on the simplicity of its implementation; e.g., less sensors are needed. Our controllers are partially linear time-varying with a simple added nonlinearity satisfying a property of persistency of excitation, tailored for nonlinear systems. Structurally speaking, the controllers are designed with the aim of separating the tasks of position-tracking and orientation. Our main results ensure the uniform global asymptotic stabilization of the closed-loop system, and hence, they imply robustness with respect to perturbations. All these aspects make our approach highly attractive in diverse application domains of vehicles’ formations such as factory settings.

Vector Field Based Formation Control of Multi-Robot System

Abstract Multi-robot system as formation can perform better than a single robot in many situations like collective movement of objects, search and rescue operations etc. In this paper a vector field framework is used for achieving path planning for a single robot, and a nonlinear controller is designed for tracking this path. The tracking controller is later extended for achieving formation control of multi-robot system. Asymptotic stability of the controller for any reference path is ensured by using Lyapunov theory. The robots may encounter obstacles in their path during tracking or may collide with each other during the switching of formation patterns. Obstacle avoidance and collision avoidance have been achieved by modified vector field. If the terrain is constrained then it needs to switch from one formation to another according to the terrain requirements. Thus the capability of switching of formation or reconfiguration is an important aspect in the control of multiple mobile robots. If one of the member robots is lost, then the other robots continue with the remaining formation structure and thus ensures robustness against loss of team member. From the extensive simulations using Matlab environment it is seen that any formation of mobile robots is achieved in addition to reconfiguration and obstacle avoidance. The formation control algorithm has been experimentally demonstrated by a series of experiments conducted using LEGO NXT Mindstorm robot kit.

916 ある編隊アルゴリズムを用いた複数移動ロボットのドッキング制御(ロボティクス・メカトロニクスVI)

916 ある編隊アルゴリズムを用いた複数移動ロボットのドッキング制御(ロボティクス・メカトロニクスVI)

A PSO-Optimized Reciprocal Velocity Obstacles Algorithm for Navigation of Multiple Mobile Robots

In this paper, a new optimization method for the Reciprocal Velocity Obstacles (RVO) is proposed. It uses the well-known Particle Swarm Optimization (PSO) for navigation control of multiple mobile robots with kinematic constraints. The RVO is used for collision avoidance between the robots, while PSO is used to choose the best path for the robot maneuver to avoid colliding with other robots and to get to its goal faster. This method was applied on 24 mobile robots facing each other. Simulation results have shown that this method outperforms the ordinary RVO when the path is heuristically chosen.

SDRE based Leader-Follower Formation Control of Multiple Mobile Robots

<span style="font-family: T3Font_6; font-size: xx-small;"><span style="font-family: T3Font_6; font-size: xx-small;"><em> <span style="font-size: small;"> </span></em><p class="MsoNormal" style="margin: 0cm 0cm 0pt; text-align: justify;"><em><em style="mso-bidi-font-style: normal;"><span style="font-size: 11pt; mso-bidi-font-size: 10.0pt; mso-ansi-language: EN-US;" lang="EN-US">Formation control of multiple mobile robots is relatively a new area of robotics and increase the control performance and advantages of multiple mobile robots systems. <a name="OLE_LINK72">In this work we present a study concerning the modeling and formation control of a robotic system composed by two mobile robots, where one robot is the leader and the other is follower</a></span></em><em style="mso-bidi-font-style: normal;"><span style="font-size: 11pt; mso-bidi-font-size: 10.0pt; mso-ansi-language: EN-US;" lang="EN-US">. The system is a nonlinear dynamical system and cannot be controlled by traditional linear control techniques. The control strategy proposed is the SDRE (State-Dependent Riccati Equation) method. Simulations results with the software Matlab show the efficiency of the control method.</span></em></em></p><span style="font-size: small;"> </span></span></span>

Self-Localization Based on Multiresolution Map for Remote Control of Multiple Mobile Robots

This paper proposes a localization method using multiresolution maps for the navigation of multiple mobile robots based on formation behaviors. The remote control of multiple mobile robots is one the most important tasks in robotics to realize distributed remote monitoring in unknown and/or dynamic environments. However, it is very difficult for a human operator to control multiple mobile robots separately at the same time. Therefore, autonomous formation behaviors of multiple robots are required to reduce mental and physical loads of the human operator. If each mobile robot can estimate the self-position or relative position in a group, it is easier for multiple mobile robots to realize formation behaviors. First, we propose a method of simultaneous localization and mapping based on a grid approach. Next, we explain how to share the build map among multiple mobile robots, and propose a self-localization method based on multiresolution maps. Furthermore, we explain the formation behaviors of multiple mobile robots. Finally, we show several experimental results, and discuss the effectiveness of the proposed method.

A finite-time approach to formation control of multiple mobile robots with terminal sliding mode

In this study, a new finite-time synchronised approach is developed for multiple mobile robots formation control based on terminal sliding mode control principle and system synchronisation theory. Associated stability analysis is presented to lay a foundation for analytical understanding in generic theoretical aspects and safe operation for real systems. An illustrative example of multiple mobile robots formation control is bench tested to validate the effectiveness of the proposed approach.