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Manipulation Planning with Soft Constraints by Randomized Exploration of the Composite Configuration Space

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  • Robot and Applications
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Abstract

In this paper, an efficient and probabilistic complete planning algorithm called Composite-space RRT is presented to address motion planning with soft constraints for spherical wrist manipulators. Firstly, we propose a novel configuration space termed Composite Configuration Space (“Composite Space” for short), which is composed of the joint space and the task space. Then, collision-free paths are generated in the composite space by the Rapidly-exploring Random Trees (RRT) algorithm. Finally, the planned paths in the composite space are mapped into the corresponding joint-space paths by a local planner. As the analytical inverse kinematics (IK) of the spherical wrist is used in the local planner, the proposed Composite-space RRT algorithm is characterized by high efficiency and no numerical iteration. Moreover, this approach can effectively improve the smoothness of the end-effector orientation path. The effectiveness of the proposed algorithm is demonstrated on the Willow Garage’s PR2 simulation platform with two typical orientation-constrained cases.

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China

    Jiangping Wang, Shirong Liu & Botao Zhang

  2. School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China

    Changbin Yu

Authors
  1. Jiangping Wang
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  2. Shirong Liu
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  3. Botao Zhang
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Correspondence to Shirong Liu.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Augie Widyotriatmo under the direction of Editor Myo Taeg Lim. This work was supported by the NSFC-DFG Grant (No. 61761136005), the Key Research and Development Program of Zhejiang Province China (No. 2019C04018), NSFC Grant (No. 61503108), and 111 Project (No. D17019).

Jiangping Wang received his M.S. degree in control engineering from Hangzhou Dianzi, Hangzhou, China, in 2015. He is currently working toward a Ph.D. degree at the School of Automation, Hangzhou Dianzi University, Hangzhou, China. His current research interests include motion planning and intelligent robotics.

Shirong Liu received his B.Sc. degree in electrical engineering from Zhejiang University, Hangzhou, China, an M.Sc. degree in industrial automation from Chongqing University, Chongqing, China, and a Ph.D. degree in system and control from East China University of Science and Technology, Shanghai, China, in 1978, 1986, and 2000, respectively. His research interests include intelligent robotics, intelligent control, system modeling, and renewable-energy electric power systems and control.

Botao Zhang received his Ph.D. degree in control engineering from East China University of Science and Technology, Shanghai, China, in 2012. He is an Associate Professor at the School of Automation, Hangzhou Dianzi University, Hangzhou, China. His current research interest includes intelligent robotics.

Changbin Yu received his B.Eng. degree (Hons.) in computer engineering from Nanyang Technological University, Singapore, in 2004 and a Ph.D. degree from Australian National University, Canberra, ACT, Australia, in 2008. He obtained tenure with ANU in 2014 and still holds an Honorary Professorship. In 2017, he founded the AI and Robotics Centre, Westlake University, Hangzhou, China. He was appointed as the Optus Chair to establish the Optus-Curtin Centre of Excellence in AI, Curtin University, Perth, WA, Australia. Dr. Yu was a recipient of the Endeavour Asia Award, APD Fellowship, QEII Fellowship, and a 2019 John Booker Medal in Engineering. He received multiple grants from AAS and ATSE. He is a fellow of Engineers Australia.

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Wang, J., Liu, S., Zhang, B. et al. Manipulation Planning with Soft Constraints by Randomized Exploration of the Composite Configuration Space. Int. J. Control Autom. Syst. 19, 1340–1351 (2021). https://doi.org/10.1007/s12555-019-0727-8

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  • DOI: https://doi.org/10.1007/s12555-019-0727-8

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