Abstract
Path planning is vital for a robot deployed in a mission in a challenging environment with obstacles around. The robot needs to ensure that the mission is accomplished without colliding with any obstacles and find an optimal path to reach the goal. Three important criteria, i.e., path length, computational complexity, and completeness, need to be taken into account when designing a path planning method. Artificial Potential Field (APF) is one of the best methods for path planning as it is fast, simple, and elegant. However, the APF has a major problem called local minima, which will cause the robot fails to reach the goal. This paper proposed an Improved Potential Field method to solve the APF limitation. Despite that, the path length produced by the Improved APF is not optimal. Therefore, a path pruning technique is proposed in order to shorten the path generated by the Improved APF. This paper also compares the performance on the path length and computational time of the Improved APF with and without path pruning. Through simulation, it is proven that the proposed technique could overcome the local minima problem and produces a relatively shorter path with fast computation time.
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References
Hasircioglu I, Topcuoglu HR, Ermis M (2008) 3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms. In: Proceedings of the conference on genetic and evolutionary computation, pp 1499–1506
Omar RB (2011) Path planning for unmanned aerial vehicles using visibility line-based methods. control and instrumentation research group. Department of Engineering, University of Leicester, March 2011
Sabudin EN, Omar R, Che Ku Melor CKANH (2016) Potential field methods and their inherent approaches for path planning. ARPN J Eng Appl Sci 11(18):10801–10805
Borenstein J, Koren Y (1991) Potential field methods and their inherent limitations for mobile robot navigation, April 1991, pp 1398–1404
Cen Y, Wang L, Zhang H (2007) Real-time obstacle avoidance strategy for mobile robot based on improved coordinating potential field with genetic algorithm. In: IEEE international conference on control applications, October 2007
Lifen AL, Rouxin BS, Shuandao CL, Jiang DW (2016) Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function. In: IEEE Chinese guidance, navigation and control conference (CGNCC), 12–14 August 2016
Liu Y, Zhao Y (2016) A virtual-waypoint based artificial potential field method for UAV path planning. In: Proceedings of 2016 IEEE Chinese guidance, navigation and control conference, 12–14 August 2016
Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of the IEEE international conference on robotics and automation, pp 500–505
Mei W, Su Z, Tu D, Lu X (2013) A hybrid algorithm based on artificial potential field and BUG for path planning of mobile robot. In: 2nd international conference on measurement, information and control
Wang S, Min H (2013) Experience mixed the modified artificial potential field method. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), 3–7 November 2013
Mei JH, Arshad MR (2015) A balance-artificial potential field method for autonomous surface vessel navigation in unstructured riverine environment. In: IEEE international symposium on robotics and intelligent sensors (IRIS)
Li G, Tamura Y, Yamashita A, Asama H (2012) Effective improved artificial potential field-based regression search method for robot planning. In: IEEE international conference on mechatronic and automation, 5–8 August 2012
Li G, Tamura Y, Yamashita A, Asama H (2013) Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning. Int J Mechatron Autom 3(3):141–170
Sfeir J, Saad M, Saliah-Hasane H (2011) An improved potential field approach to real-time mobile robot path planning in an unknown environment. In: IEEE international symposium on robotic and sensors environments (ROSE)
Park JW, Kwak HJ, Kang YC, Kim DW (2016) Advanced fuzzy potential field method for mobile robot obstacle avoidance. J Comput Intell Neurosci 2016. Article No. 10
Godrich MA. Potential Field Tutorial. https://pdfs.semanticscholar.org/725e/fa1af22f41dcbecd8bd445ea82679a6eb7c6.pdf. Accessed 29 Aug 2019
Robot Motion Planning and Control. Potential Field. https://sebastian-hoeffner.de/uni/ceng786/index.php?number=2. Accessed 29 Aug 2019
Debnath SK, Omar RB, Abdul Latip NB (2019) A review on energy efficient path planning algorithms for unmanned air vehicles. In: Computational science and technology. Springer, Singapore
Omar RB, Che Ku Melor CKNAH, Sabudin EN (2015) Performance comparison of path planning methods. ARPN J Eng Appl Sci
Li G, Tong S, Lv G, Xiao R, Cong F, Tong Z, Yamashita A, Asama H (2015) An improved artificial potential field-based simultaneous forward search (improved APF-based SIFORS) method for robot path planning. In: The 12th international conference on ubiquitous robots and ambient intelligence (URAI), 28–30 October 2015
Acknowledgements
Authors like to give appreciations to Universiti Tun Hussein Onn Malaysia (UTHM) and Research Management Center (RMC) for supporting fund under TIER-1 VOT H131.
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Sabudin, E.N. et al. (2021). Improved Potential Field Method for Robot Path Planning with Path Pruning. In: Md Zain, Z., et al. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 . NUSYS 2019. Lecture Notes in Electrical Engineering, vol 666. Springer, Singapore. https://doi.org/10.1007/978-981-15-5281-6_9
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DOI: https://doi.org/10.1007/978-981-15-5281-6_9
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