Proceedings of the 10th annual conference on Genetic and evolutionary computation
Pattern-Based Genetic Algorithm Approach to Coverage Path Planning for Mobile Robots
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
UbiPaPaGo: Context-aware path planning
Expert Systems with Applications: An International Journal
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
A multi-objective approach for the motion planning of redundant manipulators
Applied Soft Computing
Multi-objective path planning in discrete space
Applied Soft Computing
A Multi-objective Incremental Path Planning Algorithm for Mobile Agents
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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This paper describes the use of a genetic algorithm (GA) for the problem of offline point-to-point autonomous mobile robot path planning. The problem consists of generating “valid” paths or trajectories, for an Holonomic Robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a two-dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. First, we decided to use a conventional GA to evaluate its ability to solve this problem (using only one criteria for optimization). Due to the fact that we also wanted to optimize paths under two criteria or objectives, then we extended the conventional GA to implement the ideas of Pareto optimality, making it a multi-objective genetic algorithm (MOGA). We describe useful performance measures and simulation results of the conventional GA and of the MOGA that show that both types of GAs are effective tools for solving the point-to-point path-planning problem.