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Gong Z, Nie Z, Liu Q, Liu XJ. Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector. ISA TRANSACTIONS 2023; 139:606-620. [PMID: 37117051 DOI: 10.1016/j.isatra.2023.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 02/26/2023] [Accepted: 04/05/2023] [Indexed: 06/19/2023]
Abstract
Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS.
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Affiliation(s)
- Zhao Gong
- Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Zhenguo Nie
- Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Quan Liu
- Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China.
| | - Xin-Jun Liu
- Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China.
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Heredia JDF, Rubrico JIU, Shirafuji S, Ota J. Teaching Tasks to Multiple Small Robots by Classifying and Splitting a Human Example. JOURNAL OF ROBOTICS AND MECHATRONICS 2017. [DOI: 10.20965/jrm.2017.p0419] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
[abstFig src='/00290002/14.jpg' width='300' text='Two robots performing the task of opening the folding chair lying on the floor' ] In this study, we present a novel framework to address the problem of teaching manipulation tasks performed by a single human to a set of multiple small robots in a short period. First, we focused on classifying the manipulation style used during a human-performed task. An allocator process is proposed to determine the type and number of robots to be taught based on the capabilities of available robots. Then, according to the detected task requirements, robot behaviors are generated to create robot programs by splitting human demonstration data. Small robots were used to evaluate our approach in four defined tasks that were taught by a single human. Experiments demonstrated the efficiency of the method to classify and judge whether the division of a task is necessary or not. Moreover, robot programs were generated for manipulating selected objects either individually or in a cooperative manner.
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