Hostname: page-component-7f64f4797f-kg7gq Total loading time: 0.001 Render date: 2025-11-03T17:29:58.008Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm

Published online by Cambridge University Press:  16 April 2019

Zhenhua Pan ,
Dongfang Li ,
Kun Yang  and
Hongbin Deng
Zhenhua Pan
Affiliation:
Department of Electromechanical Engineering, Beijing Institute of Technology, Beijing, China. E-mails: pzh-mingzhe@outlook.com, 188377985@qq.com, yk3210281001@163.com
Dongfang Li
Affiliation:
Department of Electromechanical Engineering, Beijing Institute of Technology, Beijing, China. E-mails: pzh-mingzhe@outlook.com, 188377985@qq.com, yk3210281001@163.com
Kun Yang
Affiliation:
Department of Electromechanical Engineering, Beijing Institute of Technology, Beijing, China. E-mails: pzh-mingzhe@outlook.com, 188377985@qq.com, yk3210281001@163.com
Hongbin Deng*
Affiliation:
Department of Electromechanical Engineering, Beijing Institute of Technology, Beijing, China. E-mails: pzh-mingzhe@outlook.com, 188377985@qq.com, yk3210281001@163.com
*
*Corresponding author. E-mail: denghongbin@bit.edu.cn
Get access Rights & Permissions [Opens in a new window]

Summary

As for the obstacle avoidance and formation control for the multi-robot systems, this paper presents an obstacle-avoidance method based on the improved artificial potential field (IAPF) and PID adaptive tracking control algorithm. In order to analyze the dynamics and kinematics of the robot, the mathematical model of the robot is built. Then we construct the motion situational awareness map (MSAM), which can map the environment information around the robot on the MSAM. Based on the MSAM, the IAPF functions are established. We employ the rotating potential field to solve the local minima and oscillations. As for collisions between robots, we build the repulsive potential function and priority model among the robots. Afterwards, the PID adaptive tracking algorithm is utilized to multi-robot formation control. To demonstrate the validity of the proposed method, a series of simulation results confirm that the approaches proposed in this paper can successfully address the obstacle- and collision-avoidance problem while reaching formation.

Keywords

Multi-robotIAPFObstacle avoidanceMSAMFormation control

Information

Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1883 - 1903
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Mac, T., Copot, C., Tran, D. T., De Keyser, R., “A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization,” Appl. Soft Comput.59, 68–76 (2017).CrossRefGoogle Scholar
Song, I. Y., Kim, D. Y., Ahn, H.-S. and Shin, V., “Simultaneous Pedestrian and Multiple Mobile Robot Localization Using Distributed Extended Kalman Filter,” Proceedings of the IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand (2008) pp. 1056–1069.Google Scholar
Glorieux, E., Riazi, S. and Lennartson, B., “Productivity/energy optimization of trajectories and coordination for cyclic multi-robot systems,” Robot. Comput. Integr. Manuf.49, 152–161 (2018).CrossRefGoogle Scholar
Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern.4, 100–107 (1968).CrossRefGoogle Scholar
Howden, W., “The sofa problem,” Comput. J. 11, 299–301 (1968).CrossRefGoogle Scholar
Masehian, E. and Amin Naseri, M., “A voronoi diagram-visibility graph potential field compound algorithm for robot path planning,” J. Field Robot.21, 275–300 (2004).Google Scholar
Dorigo, M., Maniezzo, V. and Colorni, A., “Ant system: Optimization by a colony of cooperating agents,” IEEE Trans. Syst. Man Cybern. Part B Cyber.26, 29–41 (1996).CrossRefGoogle ScholarPubMed
van Ast, J., Babuska, R. and De Schutter, B., “Particle swarms in optimization and control,” IFAC Proc. 41, 5131–5136 (2008).CrossRefGoogle Scholar
Ju, T., Liu, S., Yang, J. and Sun, D., “Rapidly exploring random tree algorithm-based path planning for robot-aided optical manipulation of biological cells,” IEEE Trans. Automat. Sci. Eng. 11, 649–657 (2014).CrossRefGoogle Scholar
Bircher, A., Alexis, K., Schwesinger, U., Omari, S., Burri, M. and Siegwart, R., “An incremental sampling-based approach to inspection planning: The rapidly exploring random tree of trees,” Robotica.35(6), 1327–1340 (2017).CrossRefGoogle Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Rob. Res. 5, 90–98 (1986).CrossRefGoogle Scholar
Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” Proceedings of the 1991 IEEE International Conference on Robotics and Automation, Sacramento, CA, USA, vol. 2 (1991) pp. 1398–1404.Google Scholar
Li, G., Tamura, Y., Yamashita, A. and Asama, H., “Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning,” Int. J. Mechatr. Automat. 3(3), 141–170 (2013).CrossRefGoogle Scholar
Tsourveloudis, N. C., Valavanis, K. P. and Hebert, T., “Autonomous vehicle navigation utilizing electrostatic potential fields and fuzzy logic,” IEEE Trans. Robot. Automat. 17(4), 490–497 (2001).CrossRefGoogle Scholar
Rajvanshi, A., Islam, S., Majid, H., Atawi, I., Biglerbegian, M. and Mahmud, S., “An efficient potential-function based path planning algorithm for mobile robots in dynamic environments with moving targets,” Br. J. Appl. Sci. Technol. 9, 534–550 (2015).CrossRefGoogle Scholar
Ahmed, A. A. and Abdalla, T. Y., “Path planning of mobile robot by using modified optimized potential field method,” Int. J. Comput. Appl. 113(4), 6–10 (2015).Google Scholar
Loizou, S. G., “The navigation transformation,” IEEE Trans. Robot. 33(6), 1516–1523 (2017).CrossRefGoogle Scholar
Xiao, F., Wang, L., Chen, J. and Gao, Y., “Finite-time formation control for multi-agent systems,” Automatica. 45, 2605–2611 (2009).CrossRefGoogle Scholar
Ji, M. and Egerstedt, M., “Distributed coordination control of multi-agent systems while preserving connectedness,” IEEE Trans. Robot. 23(4), 693–703 (2007).CrossRefGoogle Scholar
Olfati, R.-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Trans. Automat. Contr. 51(3), 401–420 (2006).CrossRefGoogle Scholar
Kuriki, Y. and Namerikawa, T., “Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control,” SICE J. Control Measur. Syst. Integr. 8, 285–294 (2015).CrossRefGoogle Scholar
Nagy, I., “Behaviour study of a multi-agent mobile robot system during potential field building,” Acta Polytech. Hung. 6, 111–136 (2009).Google Scholar
Dai, Y., Kim, Y., Wee, S., Lee, D. and Lee, S., “A switching formation strategy for obstacle avoidance of a multi-robot system based on robot priority model,” ISA Trans. 56, 123–134 (2015).CrossRefGoogle ScholarPubMed
Hou, P., Pan, H. and Guo, C., “Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim,” Int. J. Model. Simul. Sci. Comput. 8, 1750046 (2017).CrossRefGoogle Scholar
Hu, X.-P., Li, Z.-Y. and Jing, C., “A path planning method based on artificial potential field improved by potential ow theory,” 2nd International Conference on Computer Science and Technology, Guilin, Guangxi, China (2017) pp. 617–625.Google Scholar
Masoud, S. A. and Masoud, A. A., “Constrained motion control using vector potential fields,” IEEE Trans. Syst. Man Cybern. A Syst. Hum. 30(3), 251–272 (2000).CrossRefGoogle Scholar
Chen, J., Sun, D., Yang, J. and Chen, H., “Leader-follower formation control of multiple nonholonomic mobile robots incorporating a receding-horizon scheme,” Int. J. Robot. Res. 29, 727–747 (2010).CrossRefGoogle Scholar
Lee, G. and Chwa, D., “Decentralized behavior-based formation control of multiple robots considering obstacle avoidance,” Intel. Serv. Robot. 11(1) 1–12 (2017).Google Scholar
Abbasi, Y., Moosavian, S. A. A. and Novinzadeh, A. B., “Formation control of aerial robots using virtual structure and new fuzzy-based self-tuning synchronization,” Trans. Inst. Meas. Control 39(12), 1906–1919 (2017).CrossRefGoogle Scholar
Dong, L., Chen, Y. and Qu, X., “Formation control strategy for nonholonomic intelligent vehicles based on virtual structure and consensus approach,” Procedia Eng. 137, 415–424 (2016).CrossRefGoogle Scholar
Desai, J. P., Ostrowski, J. and Kumar, V., “Controlling Formations of Multiple Mobile Robots,” Proceedings of the IEEE Intemational Conference on Robotics and Automation, Leuven (1998) pp. 2864–2869.Google Scholar
Shen, D., Sun, W. and Sun, Z., “Adaptive PID formation control of nonholonomic robots without leader’s velocity information,” ISA Trans. 53, 474–480 (2014).CrossRefGoogle ScholarPubMed
Mariappan, V., Lee, M., Cho, J. and Cha, J., “On board vision-based object tracking control stabilization using PID controller,” Int. J. Adv. Culture Technol. 4, 81–86 (2016).CrossRefGoogle Scholar
Jung, J.-W., Leu, V. Q., Do, T. D., Kim, E.-K. and Choi, H. H., “Adaptive PID speed control design for permanent magnet synchronous motor drives,” IEEE Trans. Power Electronics 30, 900–908 (2015).CrossRefGoogle Scholar