Cross-Country Path Finding using Hybrid approach of BBO and ACO

Biogeography based optimization (BBO) and ant colony optimization (ACO) to develop global optimization path. In natural scenario, there are no prior paths and we don't have any prior information about any geographical area. The key factor to achieve a task in such area is Path planning; therefore this research direction is very useful in recent years. This hybrid approach describes autonomous navigation for outdoor vehicles which includes terrain mapping, obstacle detection and avoidance, and goal seeking in cross-country using Swarm Intelligence. These approaches combine the strengths of both Biogeography Based Optimization (BBO) for natural and obstacle detection from the satellite image and Ant Colony Optimization (ACO) algorithm for obstacle avoidance and shortest path to the goal. In this paper this hybrid approach 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.