Similar articles

Abstract: We propose a contraction transformation algorithm to plan a complete coverage trajectory for a mobile robot to accomplish specific types of missions based on the Arnold dynamical system. First, we construct a chaotic mobile robot by combining the variable z of the Arnold equation and the kinematic equation of the robot. Second, we construct the candidate sets including the initial points with a relatively high coverage rate of the constructed mobile robot. Then the trajectory is contracted to the current position of the robot based on the designed contraction transformation strategy, to form a continuous complete coverage trajectory to execute the specific types of missions. Compared with the traditional method, the designed algorithm requires no obstacle avoidance to the boundary of the given workplace, possesses a high coverage rate, and keeps the chaotic characteristics of the produced coverage trajectory relatively unchanged, which enables the robot to accomplish special missions with features of completeness, randomness, or unpredictability.

特殊任务下基于Arnold系统的移动机器人全覆盖路径规划

[1]Banerjee C, Datta D, Agarwal A, 2015. Chaotic patrol robot with frequency constraints. Proc IEEE Int Conf on Research in Computational Intelligence and Communication Networks, p.340-344.

[2]Curiac DI, Volosencu C, 2009. Developing 2D chaotic trajectories for monitoring an area with two points of interest. Proc 10^th WSEAS Int Conf on Automation & Information, p.366-369.

[3]Curiac DI, Volosencu C, 2012. Chaotic trajectory design for monitoring an arbitrary number of specified locations using points of interest. Math Probl Eng, 2012:940276.

[4]Curiac DI, Volosencu C, 2014. A 2D chaotic path planning for mobile robots accomplishing boundary surveillance missions in adversarial conditions. Commun Nonl Sci Numer Simul, 19(10):3617-3627.

[5]Curiac DI, Volosencu C, 2015a. Imparting protean behavior to mobile robots accomplishing patrolling tasks in the presence of adversaries. Bioinspir Biomim, 10(5):056017.

[6]Curiac DI, Volosencu C, 2015b. Path planning algorithm based on Arnold cat map for surveillance UAVs. Def Sci J, 65(6):483-488.

[7]Curiac DI, Banias O, Volosencu C, et al., 2018. Novel bioinspired approach based on chaotic dynamics for robot patrolling missions with adversaries. Entropy, 20(5):378.

[8]Fahmy AA, 2012. Performance evaluation of chaotic mobile robot controllers. Int Trans J Eng Manag Appl Sci Technol, 3(2):145-158.

[9]Galceran E, Carreras M, 2013. Planning coverage paths on bathymetric maps for in-detail inspection of the ocean floor. Proc Int Conf on Robotics and Automation, p.4159- 4164.

[10]Hwang KS, Lin JL, Huang HL, 2011. Dynamic patrol planning in a cooperative multi-robot system. Proc 14^th FIRA RoboWorld Congress on Next Wave in Robotics, p.116- 123.

[11]Li CH, Song Y, Wang FY, et al., 2015. Chaotic path planner of autonomous mobile robots based on the standard map for surveillance missions. Math Probl Eng, 2015:263964.

[12]Li CH, Song Y, Wang FY, et al., 2016. A bounded strategy of the mobile robot coverage path planning based on Lorenz chaotic system. Int J Adv Robot Syst, 13(3):107.

[13]Li CH, Song Y, Wang FY, et al., 2017. A chaotic coverage path planner for the mobile robot based on the Chebyshev map for special missions. Front Inform Technol Electron Eng, 18(9):1305-1319.

[14]Li CH, Wang ZQ, Fang C, et al., 2018. An integrated algorithm of CCPP task for autonomous mobile robot under special missions. Int J Comput Intell Syst, 11:1357-1368.

[15]Liu P, Sun JJ, Qin HZ, et al., 2017. The area-coverage path planning of a novel memristor-based double-stroll chaotic system for autonomous mobile robots. Proc Chinese Automation Congress, p.6982-6987.

[16]Lorenz EN, 1997. The Essence of Chaos. Liu SD, translator. China Meteorological Press, Beijing, China, p.186-189 (in Chinese).

[17]Martins-Filho LS, Macau EEN, 2007a. Patrol mobile robots and chaotic trajectories. Math Probl Eng, 2007:61543.

[18]Martins-Filho LS, Macau EEN, 2007b. Trajectory planning for surveillance missions of mobile robots. In: Mukhopadhyay SC, Gupta GS (Eds.), Autonomous Robots and Agents. Springer Berlin Heidelberg, p.109-117.

[19]Nakamura Y, Sekiguchi A, 2001. The chaotic mobile robot. IEEE Trans Robot Autom, 17(6):898-904.

[20]Oksanen T, Visala A, 2009. Coverage path planning algorithms for agricultural field machines. J Field Robot, 26(8):651- 668.

[21]Ousingsawat J, Earl MG, 2007. Modified lawn-mower search pattern for areas comprised of weighted regions. Proc American Control Conf, p.918-923.

[22]Park E, Kim KJ, del Pobil P, 2012. Energy efficient complete coverage path planning for vacuum cleaning robots. In: Park JJ, Leung VCM, Wang CL, et al. (Eds.), Future Information Technology, Application, and Service. Springer, Dordrecht, p.23-31.

[23]Peitgen HO, Jürgens H, Saupe D, 2004. Chaos and Fractals: New Frontiers of Science. Springer, New York, NY.

[24]Prado J, Marques L, 2014. Energy efficient area coverage for an autonomous demining robot. In: Armada MA, Sanfeliu A, Ferre M (Eds.), ROBOT2013: First Iberian Robotics Conf. Springer, Cham, p.459-471.

[25]Sekiguchi A, Nakamura Y, 1999. The chaotic mobile robot. Proc IEEE/RJS Int Conf on Intelligent Robots and Systems, p.172-178.

[26]Sooraska P, Klomkarn K, 2010. “No-CPU” chaotic robots: from classroom to commerce. IEEE Circ Syst Mag, 10(1): 46-53.

[27]Torres M, Pelta DA, Verdegay JL, et al., 2016. Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Syst Appl, 55:441-451.

[28]Volos CK, Kyprianidis IM, Stouboulos IN, 2012a. A chaotic path planning generator for autonomous mobile robots. Robot Auton Syst, 60(4):651-656.

[29]Volos CK, Bardis NG, Kyprianidis IM, et al., 2012b. Implementation of mobile robot by using double-scroll chaotic attractors. Proc 11^th Int Conf on Applications of Electrical and Computer Engineering, p.119-124.

[30]Volos CK, Kyprianidis IM, Stouboulos IN, 2013. Experimental investigation on coverage performance of a chaotic autonomous mobile robot. Robot Auton Syst, 61(12):1314- 1322.

[31]Zheng Z, Liu Y, Zhang XY, 2016. The more obstacle information sharing, the more effective real-time path planning? Knowl-Based Syst, 114:36-46.