Hybrid approach using PSO and AGO for Cross-Country Path Planning

摘要

In natural scenario, there is no road and we don't have any prior information about any geographical area. Path planning in such area is a key factor to achieve a task; therefore this research direction is very hot in recent years. This paper describes a novel approach of autonomous navigation for outdoor vehicles which includes terrain mapping, obstacle detection and avoidance, and goal seeking in cross-country using Swarm Intelligence. This paper combines the strengths of both Particle Swarm optimization (PSO) for obstacle detection from the satellite image and Ant Colony Optimization (ACO) algorithm for obstacle avoidance and shortest path to the goal. The concept of this paper is to explore the improved swarm computing algorithms for the satellite image obstacle extraction and path planning which is safer, shorter, smoother and quickly optimized.