Collaborative Evolutionary Planning Framework (EPF) for Route Planning

This research presents a collaborative evolutionary planning framework for large scale grid exploration and planning problems. It caters for both dynamic and unknown environments using evolutionary techniques. In addition, we integrate the exploration and planning process in a unified framework using multi agent system. As a proof of success, we have developed extensive simulations with realistic obstacles and targets. Our algorithm addresses the issues involved during such exploration and post exploration route planning. It acts as a controller and navigator for multiple agents and demonstrates the applicability for two different domains, Field Exploration and Route Planning. The EPF uses an optimized search algorithm for exploration phase and genetic algorithm for optimization of route in dynamic environments. The EPF can be used in different exploration and route planning problems but this paper focuses on obstacle detection and avoidance for its implementation.