Global optimal path planning for mobile robots based on hybrid approach with high diversity and memorization

Authors:
Yun-Qian Miao;Alaa Khamis;Fakhreddine O. Karray;Mohamed S. Kamel
Affiliations:
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada;Faculty of Petroleum and Mining Engineering, Suez Canal University, Egypt;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Year:
2011

Citing 5
Cited 0

Real-time obstacle avoidance for manipulators and mobile robots

International Journal of Robotics Research
An algorithm for planning collision-free paths among polyhedral obstacles

Communications of the ACM
Robot Motion Planning

Robot Motion Planning
Problem-Solving Methods in Artificial Intelligence

Problem-Solving Methods in Artificial Intelligence
Planning Algorithms

Planning Algorithms

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a hybrid approach to the path planning problem of autonomous robots that combines potential field (PF) method and genetic algorithm (GA). The proposed PF+GA approach takes the strength of both potential field and genetic algorithm to find global optimal collision-free paths. In this integrated frame, the PF is designed as gradient-based searching strategy to exploit local optimal, and the GA is used to explore over the whole problem space. Different implementation strategies are examined through simulations in 2D scenarios. The conducted experiments show that global optimal path can be achieved effectively using the proposed approach with a strategy of high diversity and memorization.