Obstacle Avoidance Control Strategy of Multi-robot Dynamic Cooperative Formation of Large-load-ratio Six-legged Robot under Nonholonomic Constraints

doi:10.3901/JME.2024.01.284

Abstract

Abstract: In the real environment of multi-robot formation task, it is inevitable to encounter the dynamic formation problem of formation transformation. To improve the dynamic cooperative obstacle avoidance ability of multi-robot formation in unstructured environment, taking the multi-robot system of large-load-ratio six-legged robot as an example, an obstacle avoidance strategy of dynamic formation transformation for the cluster consensus of multi-leader robots is proposed by using the leader-follower formation idea of the trajectory tracking of nonholonomic constrained mobile robots. Based on the mathematical foundation of algebraic graph theory, the communication topology and topology analysis matrixes of multiple large-load-ratio six-legged robots are designed. The global dynamic relationship and consistency mathematical model of the multi-leader cluster consensus formation system are established. The obstacle avoidance strategy of dynamic formation transformation is proposed on the cluster consensus formation control of multi-leader robots. The simulation experiments are carried out based on MATLAB software. The simulation results show that the virtual leader robot issues the control commands to make the multi-robot system complete the formation transformation and smoothly pass through the obstacle environment. Then, the effectiveness and generalization ability of the obstacle avoidance control strategy are verified for the multi-robot dynamic cooperative formation. The proposed obstacle avoidance control strategy of multi-robot dynamic cooperative formation for the large-load-ratio six-legged robot is helpful to improve the terrain passing ability of multi-robot system in unstructured environment.

Key words: nonholonomic constraint, large-load-ratio six-legged robot, cluster consensus, cooperative formation, formation transformation

CLC Number:

TP242

ZHUANG Hongchao, WANG Ning, DONG Kailun, LI Weihua, ZHOU Anliang, DONG Lei, XIA Yilu. Obstacle Avoidance Control Strategy of Multi-robot Dynamic Cooperative Formation of Large-load-ratio Six-legged Robot under Nonholonomic Constraints[J]. Journal of Mechanical Engineering, 2024, 60(1): 284-295.

References

[1] ZHUANG Hongchao,DONG Kailun,WANG Ning,et al. Multi-robot leader grouping consistent formation control method research with low convergence time based on nonholonomic constraints[J]. Applied Sciences,2022,12(5):1-19.
[2] CHEN Yangquan,WANG Zhongmin. Formation control:A review and a new consideration[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton,AB,Canada:IEEE,2005:3181-3186.
[3] OH K K,PARKM C,AHN H S. A survey of multi-agent formation control[J]. Automatica,2015,53:424-440.
[4] BALCH T,ARKIN R C. Behavior-based formation control for multirobot teams[J]. IEEE Transactions on Robotics and Automation,1998,14(6):926-939.
[5] LAWTON J R T,BEARD R W,YOUNG B J. A decentralized approach to formation maneuvers[J]. IEEE Transactions on Robotics and Automation,2003,19(6):933-941.
[6] DESAI J P. A graph theoretic approach for modeling mobile robot team formations[J]. Journal of Field Robotics,2002,19(11):511-525.
[7] REN W,SORENSEN N. Distributed coordination architecture for multi-robot formation control[J]. Robotics and Autonomous Systems,2008,56(4):324-333.
[8] KARRAS G C,BECHLIOULIS C P,FOURLAS G K,et al. A mixed-initiative formation control strategy for multiple quadrotors[J]. Robotics,2021,10(4):1-22.
[9] XU Peng,TAO Jin,XU Minyi,et al. Practical formation control for multiple anonymous robots system with unknown nonlinear disturbances[J]. Applied Sciences,2021,11(19):1-14.
[10] ELAAMERY B,PESAVENTO M,ALDOVINI T,et al. Model predictive control for cooperative transportation with feasibility-aware policy[J]. Robotics,2021,10(3):1-20.
[11] RASHID M Z A,YAKUB F,ZAKI S A,et al. Comprehensive review on controller for leader-follower robotic system[J]. Indian Journal of Geo Marine Sciences,2019,48(7):985-1007.
[12] DRAGANJAC I,MIKLIĆ D,KOVAČIĆ Z,et al. Decentralized control of multi-AGV systems in autonomous warehousing applications[J]. IEEE Transactions on Automation Science and Engineering,2016,13(4):1433-1447.
[13] ZHU H,CLARAMUNT F M,BRITO B,et al. Learning interaction-aware trajectory predictions for decentralized multi-robot motion planning in dynamic environments[J]. IEEE Robotics and Automation Letters,2021,6(2):2256-2263.
[14] LUIS C E,VUKOSAVLJEV M,SCHOELLIG A P. Online trajectory generation with distributed model predictive control for multi-robot motion planning[J]. IEEE Robotics and Automation Letters,2020,5(2):604-611.
[15] LIU Dejian,ZONG Chengguo,WANG Detang,et al. Multi-robot formation control based on high-order bilateral consensus[J]. Measurement and Control,2020,53(5-6):983-993.
[16] BRIÑÓN-ARRANZ L,SEURET A,CANUDAS-DE- WIT C. Cooperative control design for time-varying formations of multi-robot systems[J]. IEEE Transactions on Automatic Control,2014,59(8):2283-2288.
[17] YU Xiao,LIU Lu. Cooperative control for moving-target circular formation of nonholonomic vehicles[J]. IEEE Transactions on Automatic Control,2017,62(7):3448-3454.
[18] 顾勇平,周华平,马宏绪. 多足机器人群控策略及可靠性问题[J]. 机器人,2002,24(2):140-143. GU Yongping,ZHOU Huaping,MA Hongxu. Group-control on multiped robot and method of reliability[J]. Robot,2002,24(2):140-143.
[19] 詹惠轲,蒋刚,李鑫,等. 六足机器人多机可达空间协同越障仿真分析[J]. 机械设计与制造,2019,9:209-212. ZHAN Huike,JIANG Gang,LI Xin,et al. Simulation analysis of cooperative over-obstacle capability of multiple hexapod robots based on achievable workspace[J]. Machinery Design and Manufacture,2019,9:209-212.
[20] 任立敏,王伟东,杜志江,等. 障碍环境下多移动机器人动态优化队形变换[J]. 机器人,2013,35(5):535-543. REN Limin,WANG Weidong,DU Zhijiang,et al. Dynamic and optimized formation switching for multiple mobile robots in obstacle environments[J]. Robot,2013,35(5):535-543.
[21] JIA Yongnan,ZHANG Weicun. Distributed adaptive flocking of robotic fish system with a leader of bounded unknown input[J]. International Journal of Control,Automation and Systems,2014,12(5):1049-1058.
[22] YOO S J,PARK B S. Connectivity preservation and collision avoidance in networked nonholonomic multi-robot formation systems:Unified error transformation strategy[J]. Automatica,2019,103(1):274-281.
[23] CHEN B S,TSAI Y Y,LEE M Y. Robust decentralized formation tracking control for stochastic large-scale biped robot team system under external disturbance and communication requirements[J]. IEEE Transactions on Control of Network Systems,2021,8(2):654-666.
[24] XIAO Hanzhen,CHEN C L P. Leader-follower consensus multi-robot formation control using neurodynamic- optimization-based nonlinear model predictive control[J]. IEEE Access,2019,7(1):43581-43590.
[25] GE Xiaohua,HAN Qinglong,WANG Jun,et al. A scalable adaptive approach to multi-vehicle formation control with obstacle avoidance[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(6):990-1004.
[26] ALONSO-MORA J,BAKER S,RUS D. Multi-robot formation control and object transport in dynamic environments via constrained optimization[J]. The International Journal of Robotics Research,2017,36(9):1000-1021.
[27] MAI Chouyaun,LIAN Fengli. Analysis of formation control and communication pattern in multi-robot systems[C]//Proceedings of the 2006 SICE-ICASE International Joint Conference. Busan,Korea:IEEE,2006:640-645.
[28] CRUZ C D L,CARELLI R. Dynamic model based formation control and obstacle avoidance of multi-robot systems[J]. Robotica,2008,26(3):345-356.
[29] MCCLINTOCK J,FIERRO R. A hybrid system approach to formation reconfiguration in cluttered environments[C]//Proceedings of the 16th Mediterranean Conference on Control and Automation. Ajaccio,France:IEEE,2008:83-88.
[30] BAI Chengchao,YAN Peng,PAN Wei,et al. Learning-based multi-robot formation control with obstacle avoidance[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(8):11811-11822.
[31] OH H,SHIRAZI A R,SUN C,et al. Bio-inspired self-organising multi-robot pattern formation:A review[J]. Robotics and Autonomous Systems,2017,91:83-100.
[32] ZHUANG Hongchao,GAO Haibo,DENG Zongquan,et al. A review of heavy-duty legged robots[J]. Science China-Technological Sciences,2014,57(2):298-314.
[33] ZHUANG Hongchao,GAO Haibo,DENG Zongquan. Analysis method of articulated torque of heavy-duty six-legged robot under its quadrangular gait[J]. Applied Sciences,2016,6(11):1-21.
[34] ZHUANG Hongchao,GAO Haibo,DING Liang,et al. Method for analyzing articulated torques of a heavy-duty six-legged robot[J]. Chinese Journal of Mechanical Engineering,2013,26(4):801-812.
[35] ZHUANG Hongchao,WANG Ning,GAO Haibo,et al. Quickly obtaining range of articulated rotating speed for electrically driven large load-ratio six-legged robot based on maximum walking speed method[J]. IEEE Access,2019,7(1):29453-29470.
[36] ZHUANG Hongchao,WANG Ning,GAO Haibo,et al. Autonomous fault-tolerant gait planning research for electrically driven large-load-ratio six-legged robot[C]//Proceedings of the 12th International Conference on Intelligent Robotics and Applications. Shenyang,China:Springer Cham,2019:231-244.
[37] ZHUANG Hongchao,GAO Haibo,DENG Zongquan. Gait planning research for an electrically driven large-load-ratio six-legged robot[J]. Applied Sciences,2017,7(3):1-17.
[38] 任立敏,王伟东,杜志江. 移动机器人队形控制关键技术及其进展[J]. 智能系统学报,2013,8(5):381-394. REN Limin,WANG Weidong,DU Zhijiang. Key technologies and development of formation control of mobile robots[J]. CAAI Transactions on Intelligent Systems,2013,8(5):381-394.