A Practical Coordinated Trajectory Tracking for A Group of Mixed Wheeled Mobile Robots with Communication Delays

Coordination between a specific mobile robot type has been widely investigated, e.g coordination between unicycles. To extend the applicability of the system, a coordinated trajectory tracking of mixed type of mobile robots is considered. We prove that if a certain type of wheeled mobile robot is able to individually track its own reference, then coordination in tracking with other type of robots can be achieved simply by sharing individual tracking errors. Using two types of wheeled mobile robots, namely unicycle type (a nonholonomic mobile robot) and omni wheels type (a holonomic mobile robot), a coordinated control algorithm can achieve a global asymptotically stable condition of the error dynamics of the systems. Under bidirectional communication between robots as a constraint, the group is able to maintain individual tracking while coordinating the movements with other robots regardless occurring perturbations in the system and delays in communication channels. Simulation results suggest that information sharing between the robots increase the robustness in coordinating individual trajectories. Results also show that delays cause drop in performance similar to the case of no information sharing

분산 제약하에서 원격 제어되는 다수의 논홀로노믹 이동형 로봇 대형 재구성 제어

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 이동준.본 논문에서는 변화하는 주행 환경에서 분산 제약 하에 다수의 원격으로 제어되는 논홀로노믹 이동형 로봇 대형 재구성 제어에 대한 새로운 프레임워크를 제시하였다. 센싱과 컴퓨팅 능력이 갖추어진 온보드 시스템 로봇들을 활용하여 최근 개발된 예측 디스플레이 기법을 적용, 효율적인 군집 로봇의 원격 제어가 가능하도록 하였다. 잘 알려진 논홀로노믹 패시브 디컴포지션 기법을 기반으로 대형 변경이 가능하도록 새로운 파라미터를 추가, 대형 변경간 발생할 수 있는 문제들에 대해 파악하고 이를 해결하기 위해 포텐셜 필드를 활용하였다. n대의 로봇으로 다양한 대형 변경이 가능토록 시뮬레이션 환경을 조성, 39대의 탱크를 이용하여여 5가지의 각기 다른 대형으로의 변환을 새로이 제시한 알고리즘을 적용하여 구현하였다. 또한 실제 로봇 3대를 활용하여 알고리즘의 효용성에 대한 실험을 필두로 좁은 길목, 개활지 등 연속적으로 변화하는 환경 속에서의 구동을 통해 최종적으로 제시한 프레임워크의 타당성에 대해 검증하였다.We propose a novel framework for formation reconguration of multiple nonholonomic wheeled mobile robots (WMRs) in the changing driving environment. We utilize an onboard system of WMRs with the capability of sensing and computing. Each WMR has the same computing power for visualizing the driving environment, handling the sensing information and calculating the control action. One of the WMRs is the leader with the FPV camera and SLAM, while others with monocular cameras with limited FoV, as the followers, keep a certain desired formation during driving in a distributed manner. We set two control objectives, one is group driving and the other is holding the shape of the formation. We have to capture the control objectives separately and simultaneously, we make the best use of nonholonomic passive decomposition to split the WMRs' kinematics into those of the formation maintaining and group driving. The repulsive potential function to prevent the collision among WMRs and attractive potential function to restrict the boundary of follower WMRs' moving space due to limited FoV range of the monocular cameras while switching their formation are also used. Simulation with 39 tanks and experiments with three WMRs are also performed to verify the proposed framework.Acknowledgements iii List of Figures vii Abbreviations ix 1 Introduction 1 2 Formation Reconguration Control Design 5 2.1 Nonholonomic Passive Decomposition . . . . . . . . . . . . . . . 5 2.2 Attractive and Repulsive Potential Function . . . . . . . . . . . . 10 2.3 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 Estimation and Predictive Display 20 3.1 Distributed Pose Estimation . . . . . . . . . . . . . . . . . . . . . 20 3.1.1 EKF Pose Estimation of Leader WMR . . . . . . . . . . . 20 3.1.2 EKF Pose Estimation of Follower WMRs . . . . . . . . . 22 3.2 Predictive Display for Distributed WMRs Teleoperation . . . . . 23 4 Experiment 27 4.1 Test Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Demonstrate the Proposed Algorithm . . . . . . . . . . . . . . . 30 4.3 Teleoperation Experiment with the Algorithm . . . . . . . . . . . 33 5 Conclusion 40Maste

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Trajectory tracking and formation control of a platoon of mobile robots

This thesis is concerned with controlling a platoon of wheeled mobile robots (WMR), where the robots are aimed to follow a trajectory while they maintain their formation intact. The control design is carried out by considering unicycle kinematics for each robot, and the leader-follower structure for the formation. It is assumed that every robot except the one located at the end of each platoon can potentially be the leader to the one behind it. It is also assumed that each follower is capable of sensing its relative distance and relative velocity with respect to its preceding robot. The stability of the proposed control law is investigated in the case of perfect sensing and in the presence of input saturation. The impact of measurement noise on the followers is then studied assuming that a known upper bound exists on the measurement error, and a linear matrix inequality (LMI) methodology is proposed to design a control law which minimizes the upper bound on the steady-state error. The problem is then investigated in a more practical setting, where the control input is subject to delay, and that the tracking trajectory can be different in distinct time intervals. It is to be noted that delay often exists in this type of cooperative control system due to data transmission and signal processing, and if neglected in the control design, can lead to poor closed-loop performance or even instability. Furthermore, switching in tracking trajectory can be used as a collision avoidance strategy in the formation control problem. Delay dependent stability conditions are derived in the form of LMIs, and the free-weighting matrix approach is used to obtain less conservative results. Simulations are presented to demonstrate the efficacy of the results obtained in this thesis

Chatter-Free Distributed Control for Multi-agent Nonholonomic Wheeled Mobile Robot

This paper proposes to design a chatter-free distributed control for multiagent nonholonomic wheeled mobile robot systems employing terminal exponential functions with graph theory. The terminal tracking criteria are estimated using the Lyapunov approach. The development of distributed control for nonholonomic multiagent wheeled robot systems is defined in the paper along with consensus tracking for undirected fixed/switched topologies. Numerical simulations have been done in order to assess the efficacy and efficiency of the proposed distributed control method in multiple scenarios

Lane changing and merging maneuvers of car-like robots

This research paper designs a unique motion planner of multiple platoons of nonholonomic car-like robots as a feasible solution to the lane changing/merging maneuvers. The decentralized planner with a leaderless approach and a path-guidance principle derived from the Lyapunov-based control scheme generates collision free avoidance and safe merging maneuvers from multiple lanes to a single lane by deploying a split/merge strategy. The fixed obstacles are the markings and boundaries of the road lanes, while the moving obstacles are the robots themselves. Real and virtual road lane markings and the boundaries of road lanes are incorporated into a workspace to achieve the desired formation and configuration of the robots. Convergence of the robots to goal configurations and the repulsion of the robots from specified obstacles are achieved by suitable attractive and repulsive potential field functions, respectively. The results can be viewed as a significant contribution to the avoidance algorithm of the intelligent vehicle systems (IVS). Computer simulations highlight the effectiveness of the split/merge strategy and the acceleration-based controllers

Multi-robot Tethering Using Camera

An autonomous multi-robot or swarm robot able to perform various cooperative mission such as search and rescue, exploration of unknown or partially known area, transportation, surveillance, defence system, and also firefighting. However, multi-robot application often requires synchronised robotic configuration, reliable communication system and various sensors installed on each robot. This approach has resulted system complexity and very high cost of development

Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey

Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized

타이어 모델을 사용한 자율 드리프트 주행 제어 설계 및 분석

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 이동준.본 논문에서는 Wheeled Mobile Robot(WMR)의자율드리프트 드라이빙 컨트롤러를 디자인 하고 분석하며, 이를 상용 프로그램인 CarSim을 사용한 시뮬레이션을 통하여 알고리즘을 검증 한다. 첫째로, WMR의 다이나믹스와 타이어 모델을 정의 하고, 이러한 모델로 인한 제약 사항에 대하여 논의한다. 다음으로, 사람의 관점에서 드리프트 드라이빙을 분석하고, 드리프트 드라이빙 제어기의 제어 목적을 정의한다. (차량의 방향과 요 각속도를 제어한다.) 드리프트 드라이빙 제어기는 고-레벨 제어, 목표 값을 찾기 위한 최적화 그리고 고-게인 제어로 구성된다. 다음으로, 제어하지 않는 속도에 대한 분석을 진행하였다. 마지막으로 제안한 알고리즘을 CarSim 시뮬 레이터를 이용하여 검증하였다. 정상 상태의 드리프트 시뮬레이션 결과와, 헤어핀 경로에 대한 드리프트 시뮬레이션 결과를 제시 한다.Control design and analysis of Wheeled Mobile Robot(WMR) autonomous drift-driving and the simulation experiment using the CarSim simulator are presented and the analysis of the controller proceeds. We first introduce WMR dynamics, tire model and problem formulation of the WMR. We then design drift-driving control using human strategy (control side slip angle and yaw rate). The drift-driving control consists of high-level control, optimization to find desired control input and high-gain control. We analyze the uncontrolled velocity dynamics and stability of the controller. The CarSim simulation results of drift-driving on steady-state equilibriums and the hairpin path with the desired yaw rate are provided.List of Figures - v List of Tables - vi Abbreviations - vii 1 Introduction - 1 1.1 Motivation and related works . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution of this work . . . . . . . . . . . . . . . . . . . . . . 3 2 System Modeling - 5 2.1 Model dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Tire model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Drift-Driving Control Design - 10 3.1 High-level control . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 High-gain control . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Analysis of Control - 17 4.1 Internal dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Stability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5 Simulation Results - 25 5.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Steady-state drift-driving . . . . . . . . . . . . . . . . . . . . . . 27 5.3 Hairpin turn drift-driving . . . . . . . . . . . . . . . . . . . . . . 33 6 Conclusion and Future Work - 40 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Maste