Hybrid Gravity Gradient Inversion-Ant Colony Optimization Algorithm for Motion Planning of Mobile Robots
Authors: Meng Wu
Motion planning is a common task required to be fulfilled by robots. A strategy combining Ant Colony Optimization (ACO) and gravity gradient inversion algorithm is proposed for motion planning of mobile robots. In this paper, in order to realize optimal motion planning strategy, the cost function in ACO is designed based on gravity gradient inversion algorithm. The obstacles around mobile robot can cause gravity gradient anomalies; the gradiometer is installed on the mobile robot to detect the gravity gradient anomalies. After obtaining the anomalies, gravity gradient inversion algorithm is employed to calculate relative distance and orientation between mobile robot and obstacles. The relative distance and orientation deduced from gravity gradient inversion algorithm is employed as cost function in ACO algorithm to realize motion planning. The proposed strategy is validated by the simulation and experiment results.
Digital Object Identifier (DOI): doi.org/10.6084/m9.figshare.12489836Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 714
 G. J. Heald and H. D .Griiffiths, “A review of underwater detection techniques and their applicability to the landmine problem”. in Proc.2nd Int. Conf. Detection Abandoned Land Mine, pp.173-176, 1998.
 Ling Xiong, Jie Ma, Jin-wen Tian. “Gravity Gradient Aided Position Approach Based on EKF and NN”. 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, pp. 1347-1350, July.2011.
 H. Zheng, H. Wang, L. Wu, H. Chai, Y. Wang. “Simulation Research on Gravity-Geomagnetism Combined Aided Underwater Navigation”. Royal Institute of Navigation, vol. 66, No. 1, pp. 83-98, 2013.
 L. Wu, X. Tian, J. Ma, and J.W. Tian, “Underwater object detection based on gravity gradient, “IEEE Geosci. Remote Sens. Lett., vol. 7, no.2, pp.362-365, Apr.2010.
 L. Wu, X.P. Ke, H. Hsu, J. Fang, C.Y. Xiong, and Y. Wang, “Joint gravity and gravity gradient inversion for subsurface underwater object detection,” IEEE Geosci. Remote Sens. Lett., vol. 107, no.4, pp.865-869, Jul.2013.
 L. Wu and J.W.Tian, “Automated gravity gradient tensor inversion for underwater object detection,” J. Geophys. Eng., vol. 7,no. 4, pp. 410-416,Dec.2010.
 Zu Yan, J.Ma, Jinwen Tian, Hai Liu, Jinggang Yu, and Yun Zhang “A Gravity Gradient Differential Ratio Method for Underwater object Detection,“ IEEE Geosci. Remote Sens. Lett., vol. 11, no.4, pp.833-837, Apr.2014.
 Song-Hiang Chia, Kuo-Lan Su, Jr-Hung Guo, Cheng-Yun Chung.” Ant Colony System Based Mobile Robot Path Planning”, 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 210-213, 2010.
 Michael Brand, Michael Masuda, Nicole Wehner and Xiao-Hua Yu. “Ant Colony Optimization algorithm for robot path planning”, 2010 International Conference on Computer Design and Applications, pp. V3-436-V3-440, 2010.
 Zhang Chibin, Wang Xingsong and Du Yong. “Complete Coverage Path Planning Based on Ant Colony Algorithm”, 2008 15th International Conference on Mechatronics and Machine Vision in Practice, pp. 357-361, 2008.
 Jie Chen, Fang Ye and Tao Jiang. “Path Planning under Obstacle-Avoidance Constraints Based on Ant Colony Optimization Algorithm”, 2017 17th International Conference on Communication Technology (ICCT), pp. 1434-1438, 2017.
 Rong Du, Xiaobin Zhang, Cailian Chen and Xingping Guan. “Path Planning under Obstacle-Avoidance Constraints Based on Ant Colony Optimization Algorithm”, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, pp. 768-773, 2010
 Ronald Uriol, Antonio Moran. “Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm”, 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp: 15-21, 2017.