Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/54597
Title: Multiobjective path planning on uneven terrains based on NAMOA*
Authors: Ganganath, N
Cheng, CT 
Tse, CK 
Keywords: Multiobjective
Heuristic search
Pareto-optimal
Path planning
Uneven terrains
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montreal, QC, Canada, 22-25 May 2016, p. 1846-1849 How to cite?
Abstract: Existing path planning algorithms are capable of finding physically feasible, shortest, and energy-efficient paths for mobile robots navigating on uneven terrains. However, shortest paths on uneven terrains are often energy inefficient while energy-optimal paths usually take long time to be traversed. Therefore, due to time and energy constraints imposed on mobile robots, these shortest and energy-optimal paths might not be applicable. We propose a multiobjective path planner that can find pareto-optimal solutions in terms of path length and energy consumption. It is based on NAMOA* search algorithm that utilizes a proposed monotone heuristic cost function. The simulation results show that nondominated path options found by the proposed path planner can be very useful in many real-world applications.
URI: http://hdl.handle.net/10397/54597
DOI: 10.1109/ISCAS.2016.7538930
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication N. Ganganath, C.-T. Cheng, and C. K. Tse, “Multiobjective path planning on uneven terrains based on NAMOA*”, in International Symposium on Circuits & Systems (ISCAS), IEEE, 22-25 May 2016, Montreal, QC, Canada, pp. 1846-1849 is available at http://dx.doi.org/10.1109/ISCAS.2016.7538930
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