HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments

Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group"s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra"s algorithms show that HCTNav"s memory peak is nine times lower than Dijkstra"s in maps with more than 150,000 cells.

[1]  Ehud Rivlin,et al.  Sensory-based motion planning with global proofs , 1997, IEEE Trans. Robotics Autom..

[2]  Huei-Yung Lin,et al.  A self-localization and path planning technique for mobile robot navigation , 2011, 2011 9th World Congress on Intelligent Control and Automation.

[3]  Bernardo Wagner,et al.  Dynamic path planning for coordinated motion of multiple mobile robots , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[4]  Laurence R. Rilett,et al.  Heuristic shortest path algorithms for transportation applications: State of the art , 2006, Comput. Oper. Res..

[5]  Vladimir J. Lumelsky,et al.  Incorporating range sensing in the robot navigation function , 1990, IEEE Trans. Syst. Man Cybern..

[6]  Jack Bresenham,et al.  Algorithm for computer control of a digital plotter , 1965, IBM Syst. J..

[7]  Mohd Yamani Idna Idris,et al.  High-Speed Shortest Path Co-processor Design , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

[8]  Kagan Tumer,et al.  Adaptive navigation for autonomous robots , 2011, Robotics Auton. Syst..

[9]  Kevin Grant,et al.  Combining heuristic and landmark search for path planning , 2008, Future Play.

[10]  Alberto Ortiz,et al.  A Bug-inspired algorithm for efficient anytime path planning , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Robin Wilson,et al.  Modern Graph Theory , 2013 .

[12]  Siba M. Sharef,et al.  A rule-based system for trajectory planning of an indoor mobile robot , 2010, 2010 7th International Multi- Conference on Systems, Signals and Devices.

[13]  Benedikt Nordhoff,et al.  Dijkstra’s Algorithm , 2013 .

[14]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[15]  Hiroshi Noborio,et al.  On the heuristics of A* or A algorithm in ITS and robot path-planning , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[16]  Mat Buckland,et al.  Programming Game AI by Example , 2004 .

[17]  J. Abdul-Jabbar,et al.  A New Hardware Architecture for Parallel Shortest Path Searching Processor Based-on FPGA Technology , 2012 .

[18]  Ali Selamat,et al.  A fast path planning algorithm for route guidance system , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[19]  Jie Wu,et al.  On Achieving the Shortest-Path Routing in 2-D Meshes , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[20]  Vladimir J. Lumelsky,et al.  Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape , 1987, Algorithmica.

[21]  Chunyan Ma,et al.  Mobile Robot Map Building Based on Cellular Automata , 2011, 2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS).