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Motion Control and Trajectory Planning for Obstacle Avoidance of the Mobile Parallel Robot Driven by Three Tracked Vehicles

Published online by Cambridge University Press:  11 September 2020

Shuzhan Shentu ,
Fugui Xie Open the ORCID record for Fugui Xie [Opens in a new window] ,
Xin-Jun Liu  and
Zhao Gong
Shuzhan Shentu
Affiliation:
The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics, Department of Mechanical Engineering (DME), Tsinghua University, Beijing 100084, China. E-mails: stsz17@mails.tsinghua.edu.cn, gongzhao@126.com
Fugui Xie
Affiliation:
The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics, Department of Mechanical Engineering (DME), Tsinghua University, Beijing 100084, China. E-mails: stsz17@mails.tsinghua.edu.cn, gongzhao@126.com Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China. E-mail: xiefg@mail.tsinghua.edu.cn
Xin-Jun Liu*
Affiliation:
The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics, Department of Mechanical Engineering (DME), Tsinghua University, Beijing 100084, China. E-mails: stsz17@mails.tsinghua.edu.cn, gongzhao@126.com Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control, Tsinghua University, Beijing 100084, China. E-mail: xiefg@mail.tsinghua.edu.cn
Zhao Gong
Affiliation:
The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics, Department of Mechanical Engineering (DME), Tsinghua University, Beijing 100084, China. E-mails: stsz17@mails.tsinghua.edu.cn, gongzhao@126.com
*
*Corresponding author. E-mail: xinjunliu@mail.tsinghua.edu.cn
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Summary

This paper proposes a mobile parallel robot (MPR) and focuses on obstacle avoidance. When analyzing the collision-free trajectories, the coupling constraints caused by the parallel mechanism and the obstacle should be emphatically solved. The solution is to divide the problem into two steps. First, the genetic algorithm is employed to search and optimize the feasible trajectories under the mechanism constraint of the MPR. Then the trajectory tracking controller is designed to make the tracked vehicles move cooperatively and track a trajectory asymptotically. Finally, simulations and experiments are carried out to verify the effectiveness of the solution.

Keywords

Mobile parallel robotTrajectory trackingTrajectory planningGenetic algorithmObstacle avoidance
Type
Article
Information
Robotica , Volume 39 , Issue 6 , June 2021 , pp. 1037 - 1050
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

Lu, Y., Liu, Y., Zhang, L., Ye, N. and Wang, Y., “Dynamics analysis of a novel 5-DoF parallel manipulator with couple-constrained wrench,” Robotica 36(10), 1421–1435 (2016).CrossRefGoogle Scholar
Xie, F. G., Liu, X.-J., Wu, C. and Zhang, P., “A novel spray painting robotic device for the coating process in automotive industry,” Proc. Inst. Mech. Eng. C: J. Mech. Eng. Sci. 229(11), 2081–2093 (2015).Google Scholar
Mo, J., Shao, Z. F., Guan, L., Xie, F. G. and Tang, X. Q., “Dynamic performance analysis of the X4 high-speed pick-and-place parallel robot,” Robot. Comput.-Integr. Manuf. 46, 48–57 (2017).CrossRefGoogle Scholar
Lafmejani, A. S., Masouleh, M. T. and Kalhor, A., “Trajectory tracking control of a pneumatically actuated 6-DOF Gough–Stewart parallel robot using Backstepping-Sliding Mode controller and geometry-based quasi forward kinematic method,” Robot. Comput.-Integr. Manuf. 54, 96–114 (2018).CrossRefGoogle Scholar
Chen, X., Liu, X.-J. and Xie, F. G., “Screw theory based singularity analysis of lower-mobility parallel robots considering the motion/force transmissibility and constrainability,” Math. Prob. Eng. 2015, 1–11 (2015).Google Scholar
Wang, J. S., Liu, X.-J. and Wu, C., “Optimal design of a new spatial 3-DOF parallel robot with respect to a frame-free index,” Sci. Chin. Ser. E: Technol. Sci. 52(4), 986–999 (2009).Google Scholar
Kuhlbusch, W., Moritz, W., Lückel, J. and Toepper, S., “TriPlanar-A new parallel 6-DOF robot for highly precise measurement and process tasks,” IFAC Proc. Vol. 33(26), 463–467 (2000).CrossRefGoogle Scholar
Tahmasebi, F. and Tsai, L. W., “Closed-form direct kinematics solution of a new parallel minimanipulator,” J. Mech. Des. 116(4), 1141–1147 (1994).CrossRefGoogle Scholar
Ben-Horin, R., Shoham, M. and Djerassi, S., “Kinematics, dynamics and construction of a planarly actuated parallel robot,” Robot. Comput.-Integr. Manuf. 14(2), 163–172 (1998).CrossRefGoogle Scholar
Hu, Y., Zhang, J., Wan, Z. and Lin, J., “Design and analysis of a 6-DOF mobile parallel robot with 3 limbs,” J. Mech. Sci. Technol. 25(12), 3215–3222 (2011).CrossRefGoogle Scholar
Liu, X.-J., Gong, Z., Xie, F. G. and Shentu, S. Z., “Kinematics Analysis and Motion Control of a Mobile Robot Driven by Three Tracked Vehicles,” In: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2018) pp. V05AT07A068–V05AT07A068.Google Scholar
Sathiya, V. and Chinnadurai, M., “Evolutionary algorithms-based multi-objective optimal mobile robot trajectory planning,” Robotica 37(8), 1363–1382 (2019).CrossRefGoogle Scholar
Chu, X., Peng, Z., Wen, G. and Rahmani, A., “Distributed fixed-time formation tracking of multi-robot systems with nonholonomic constraints,” Neurocomputing 313, 167–174 (2018).CrossRefGoogle Scholar
Fierro, R., Das, A. K., Kumar, V. and Ostrowski, J. P., “Hybrid Control of Formations of Robots,” Proceedings 2001 ICRA, IEEE International Conference on Robotics and Automation, Vol. 1 (Cat. No. 01CH37164), (2001) pp. 157–162.CrossRefGoogle Scholar
Lawton, J. R., Beard, R. W. and Young, B. J., “A decentralized approach to formation maneuvers,” IEEE Trans. Robot. Autom. 19(6), 933–941 (2003).CrossRefGoogle Scholar
Abbaspour, A., Alipour, K., Jafari, H. Z. and Moosavian, S. A., “Optimal formation and control of cooperative wheeled mobile robots,” Comptes Rendus Mécanique 343(5–6), 307–321 (2015).CrossRefGoogle Scholar
Anderson, B. D., Yu, C., Fidan, B. and Hendrickx, J. M., “Rigid graph control architectures for autonomous formations,” IEEE Cont. Syst. Mag. 28(6), 48–63 (2008).Google Scholar
Wang, P. and Ding, B., “Distributed RHC for tracking and formation of nonholonomic multi-vehicle systems,” IEEE Trans. Autom. Cont. 59(6), 1439–1453 (2014).CrossRefGoogle Scholar
Dai, L., Cao, Q., Y. Xia and Y Gao, “Distributed MPC for formation of multi-agent systems with collision avoidance and obstacle avoidance,” J. Franklin Inst. 354(4), 2068–2085 (2017).CrossRefGoogle Scholar
Lima, P. U., Ahmad, A., Dias, A., Conceição, A. G., Moreira, A. P., Silva, E. and Nascimento, T. P., “Formation control driven by cooperative object tracking,” Robot. Autonom. Syst. 63, 68–79 (2015).CrossRefGoogle Scholar
De, S., Sahoo, S. R. and Wahi, P., “Trajectory tracking control with heterogeneous input delay in multi-agent system,” J. Intell. Robot. Syst. 92(3–4), 521–544 (2018).CrossRefGoogle Scholar
Gan, Y. H. and Dai, X. Z., “Optimal trajectory-planning based on genetic algorithm for multi-robot system.,” Cont. Theory Appl. 27(9), 1245–1252 (2010).Google Scholar
Zou, T., Angeles, J. and Hassani, F., “Dynamic modeling and trajectory tracking control of unmanned tracked vehicles,” Robot. Autonom. Syst. 110, 102–111 (2018).CrossRefGoogle Scholar
Sarfraz, M. and Rehman, F. U., “Feedback stabilization of nonholonomic drift-free systems using adaptive integral sliding mode control,” Arab. J. Sci. Eng. 42(7), 2787–2797 (2017).CrossRefGoogle Scholar
Mustafa, A., Dhar, N. K., Agrawal, P. and Yerma, N. K., “Adaptive Backstepping Sliding Mode Control Based on Nonlinear Disturbance Observer for Trajectory Tracking of Robotic Manipulator,” In: 2017 2nd International Conference on Control and Robotics Engineering (ICCRE) (2017) pp. 29–34.Google Scholar
Serrano, M. E., Godoy, S. A., Mut, V. A., Ortiz, O. A. and Scaglia, G. J., “A nonlinear trajectory tracking controller for mobile robots with velocity limitation via parameters regulation,” Robotica 34(11), 2546–2565 (2016).CrossRefGoogle Scholar
Fareh, R., Saad, M. R., Saad, M., Brahmi, A. and Bettayeb, M., “Trajectory tracking and stability analysis for mobile manipulators based on decentralized control,” Robotica 37(10), 1–18 (2019).CrossRefGoogle Scholar
Fu, J. and Gao, F., “Optimal design of a 3-leg 6-DOF parallel manipulator for a specific workspace,” Chin. J. Mech. Eng. 29(4), 659–668 (2016).CrossRefGoogle Scholar
Bonev, I. A., Geometric Analysis of Parallel Mechanisms (Université Laval, Canada, 2002).Google Scholar
Huang, J. T. and Sung, Y. L., “Adaptive Beckstepping Dynamic Surface Tracking Control of Nonholonomic Mobile Robots,” In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA) (2016) pp. 1026–1031.Google Scholar
Chen, C., He, Y., Bu, C., Han, J. and Zhang, X., “Quartic Bézier Curve Based Trajectory Generation for Autonomous Vehicles with Curvature and Velocity Constraints,” In: 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014) pp. 6108–6113.Google Scholar
Jolly, K. G., Kumar, R. S. and Vijayakumar, R., “A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits,” Robotics and Autonomous Systems 57(1), 23–33 (2009).CrossRefGoogle Scholar
Simba, K. R., Uchiyama, N. and Sano, S., “Real-time smooth trajectory generation for nonholonomic mobile robots using Bézier curves,” Robot. Comput.-Integr. Manuf. 41, 31–42 (2016).CrossRefGoogle Scholar
Marsh, D., Applied Geometry for Computer Graphics and CAD (Springer Science & Business Media, Berlin, Germany, 2006).Google Scholar
Qu, Y., Zhang, Y. and Zhang, Y., “A global path planning algorithm for fixed-wing UAVs,” J. Intell. Robot. Syst. 91(3–4), 691–707 (2018).CrossRefGoogle Scholar
Qi, R., Zhou, W. and Wang, T., “An obstacle avoidance trajectory planning scheme for space manipulators based on genetic algorithm,” Robot 36(3), 263–270 (2014).Google Scholar