Design and Evaluation of Intelligent Global Path Planning Algorithms
Global path planning is a crucial component for robot navigation in map-based environments. It consists in finding the shortest path between start and goal locations. The analysis of existing literature in Chap. 2 shows two main approaches commonly used to address the path planning problem: (1) exact methods and (2) heuristic methods. A* and Dijkstra are known to be the most widely used exact methods for mobile robot global path planning. On the other hand, several heuristic methods based on ant colony optimization (ACO), genetic algorithms (GA), Tabu Search (TS), and hybrid approaches of both have been proposed in the literature. One might wonder which of these methods is more effective for the robot path planning problem. Several questions also arise: Do exact methods consistently outperform heuristic methods? If so, why? Is it possible to devise more powerful hybrid approaches using the advantages of exact and heuristics methods? To the best of our knowledge, there is no comprehensive comparison between exact and heuristic methods in solving the path planning problem. This chapter fills the gap, addresses the aforementioned research questions, and proposes a comprehensive simulation study of exact and heuristic global path planners to identify the more appropriate technique for the global path planning.
- 1.Anis Koubaa. 2014. The Iroboapp Project. http://www.iroboapp.org. Accessed 27 Jan 2016.
- 2.Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, and Andrew Y Ng. Ros. 2009. An open-source robot operating system. In ICRA workshop on open source software, vol. 3, p. 5. Kobe.Google Scholar
- 3.Anis Koubaa. 2014. Ipath simulator. http://www.iroboapp.org/index.php?title=IPath. Accessed 6 Nov 2014.
- 4.Anis Koubaa. 2014. Api documentation. http://www.iroboapp.org/ipath/api/docs/annotated.html. Accessed 6 Nov 2014.
- 5.Evangelos Kanoulas, Yang Du, Tian Xia, and Donghui Zhang. 2006. Finding fastest paths on a road network with speed patterns. In Proceedings of the 22nd international conference on data engineering, ICDE 2006, 10–10. IEEE.Google Scholar
- 7.Russell, S., and P. Norvig. 2009. Artificial intelligence: a modern approach, 3rd ed. Prentice Hall.Google Scholar
- 8.Huilai Zou, Lili Zong, Hua Liu, Chaonan Wang, Zening Qu, and Youtian Qu. 2010. Optimized application and practice of a* algorithm in game map path-finding. In IEEE 10th international conference on computer and information technology (CIT), 2138–2142. IEEE.Google Scholar
- 9.Imen Châari, Anis Koubâa, Hachemi Bennaceur, Adel Ammar, Sahar Trigui, Mohamed Tounsi, Elhadi Shakshuki, and Habib Youssef. 2014. On the adequacy of tabu search for global robot path planning problem in grid environments. Procedia Computer Science, 32(0): 604–613, The 5th international conference on ambient systems, networks and technologies (ANT-2014), the 4th international conference on sustainable energy information technology (SEIT-2014).Google Scholar
- 10.Maram Alajlan, Anis Koubaa, Imen Chaari, Hachemi Bennaceur, and Adel Ammar. 2013. Global path planning for mobile robots in large-scale grid environments using genetic algorithms. In 2013 international conference on individual and collective behaviors in robotics ICBR’2013, Sousse, Tunisia.Google Scholar
- 12.Adem Tuncer and Mehmet Yildirim. 2011. Chromosome coding methods in genetic algorithm for path planning of mobile robots. In Computer and Information Sciences II, 377–383. Springer.Google Scholar
- 13.Masoud Samadi and Mohd Fauzi Othman. 2013. Global path planning for autonomous mobile robot using genetic algorithm. In International conference on signal-image technology & internet-based systems (SITIS), 726–730. IEEE.Google Scholar
- 14.Imen Châari, Anis Koubaa, Hachemi Bennaceur, Sahar Trigui, and Khaled Al-Shalfan. 2012. Smartpath: A hybrid aco-ga algorithm for robot path planning. In IEEE congress on evolutionary computation (CEC), 1–8. IEEE.Google Scholar
- 15.Mandavilli Srinivas and Lalit. M Patnaik. 1994. Genetic algorithms: A survey. computer 27 (6): 17–26.Google Scholar
- 16.Nathan Sturtevant. 2012. Benchmark. http://www.movingai.com/benchmarks/.
- 17.Anis Koubaa. 2014. Grid-maps: 10 x 10 up to 2000 x 2000. http://www.iroboapp.org/index.php?title=Maps. Accessed 28 Jan 2014.
- 18.Robot Operating System (ROS). http://www.ros.org.