Virtual Structure Based Formation Tracking of Multiple Wheeled Mobile Robots: An Optimization Perspective

Today, with the increasing development of science and technology, many systems need to be optimized to find the optimal solution of the system. this kind of problem is also called optimization problem. Especially in the formation problem of multi-wheeled mobile robots, the optimization algorithm can help us to find the optimal solution of the formation problem. In this paper, the formation problem of multi-wheeled mobile robots is studied from the point of view of optimization. In order to reduce the complexity of the formation problem, we first put the robots with the same requirements into a group. Then, by using the virtual structure method, the formation problem is reduced to a virtual WMR trajectory tracking problem with placeholders, which describes the expected position of each WMR formation. By using placeholders, you can get the desired track for each WMR. In addition, in order to avoid the collision between multiple WMR in the group, we add an attraction to the trajectory tracking method. Because MWMR in the same team have different attractions, collisions can be easily avoided. Through simulation analysis, it is proved that the optimization model is reasonable and correct. In the last part, the limitations of this model and corresponding suggestions are given

Admittance control for collaborative dual-arm manipulation

Human-robot collaboration is an appealing solution to increase the flexibility of production lines. In this context, we propose a kinematic control strategy for dual-arm robotic platforms physically collaborating with human operators. Based on admittance control, our approach aims at improving the performance of object transportation tasks by acting on two levels: estimating and compensating gravity effects on one side, and considering human intention in the cooperative task space on the other. An experimental study using virtual reality reveals the effectiveness of our method in terms of reduced human energy expenditure

Force-based Perception and Control Strategies for Human-Robot Shared Object Manipulation

Physical Human-Robot Interaction (PHRI) is essential for the future integration of robots in human-centered environments. In these settings, robots are expected to share the same workspace, interact physically, and collaborate with humans to achieve a common task. One of the primary tasks that require human-robot collaboration is object manipulation. The main challenges that need to be addressed to achieve a seamless cooperative object manipulation are related to uncertainties in human trajectory, grasp position, and intention. The object’s motion trajectory intended by the human is not always defined for the robot and the human may grasp any part of the object depending on the desired trajectory. In addition, the state-of-the-art object-manipulation control schemes suffer from the translation/rotation problem, where the human cannot move the object in all degrees of freedom, independently, and thus, needs to exert extra effort to accomplish the task. To address the challenges, first, we propose an estimation method for identifying the human grasp position. We extend the conventional contact point estimation method by formulating a new identification model with the human applied torque as an unknown parameter and employing empirical conditions to estimate the human grasp position. The proposed method is compared with a conventional contact point estimation using the experimental data collected for various collaboration scenarios. Second, given the human grasp position, a control strategy is suggested to transport the object in all degrees of freedom, independently. We employ the concept of “the instantaneous center of zero velocity” to reduce the human effort by minimizing the exerted human force. The stability of the interaction is evaluated using a passivity-based analysis of the closed-loop system, including the object and the robotic manipulator. The performance of the proposed control scheme is validated through simulation of scenarios containing rotations and translations of the object. Our study indicates that the exerted torque of the human has a significant effect on the human grasp position estimation. Besides, the knowledge of the human grasp position can be used in the control scheme design to avoid the translation/rotation problem and reduce the human effort

Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys

In this thesis is presented a novel approach to model, control, and planning the motion of a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a nonholonomic cart (York mail trolley) in a loosely structured environment. The method is based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its kinematics properties. In consequence, two robot configurations are produced by the task of grasping and ungrasping the load, they are: the single-robot configuration and the robot-trolley configuration. Furthermore, in order to comply with the general planar motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for each configuration, the robot has been provided with two motorized steerable wheels in order to have a flexible platform able to adapt to these restrictions. [Continues.

Control of Multiple Arm Systems With Rolling Constraints

When multiple arms are used to manipulate a large object, it is necessary to maintain and control contacts between the object and effector(s) on one or more arms. The contacts are characterized by holonomic as well as nonholonomic constraints. This paper addresses the control of mechanical systems subject to nonholonomic constraints, rolling constraints in particular. It has been shown that such a system is always controllable, but cannot be stabilized to a single equilibrium by smooth feedback [l, 2]. In this paper, we show that the system is not input-state linearizable though input-output linearization is possible with appropriate output equations. Further, if the system is position-controlled (i.e., the output equation is a functions of position variables only), it has a zero dynamics which is Lagrange stable but not asymptotically stable. We discuss the analysis and controller design for planar as well as spatial multi-arm systems and present results from computer simulations to demonstrate the theoretical results

Cooperative Material Handling by Human and Robotic Agents:Module Development and System Synthesis

In this paper we present the results of a collaborative effort to design and implement a system for cooperative material handling by a small team of human and robotic agents in an unstructured indoor environment. Our approach makes fundamental use of human agents\u27 expertise for aspects of task planning, task monitoring, and error recovery. Our system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of human abilities within the present state of the art of autonomous systems. It is designed to allow for and promote cooperative interaction between distributed agents with various capabilities and resources. Our robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. Our robotic agents are not required to be homogeneous with respect to either capabilities or function. Our research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to the basic functioning of such a cooperative multi-agent system. We have constructed a testbed facility for experimenting with distributed multi-agent architectures. The required modular components of this testbed are currently operational and have been tested individually. Our current research focuses on the integration of agents in a scenario for cooperative material handling

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