Bio-Inspired Predator-Prey Large Spacial Search Path Planning

Traditional path-planning algorithms are entirely reliant on a priori information to function, meaning user interaction is always required. This rigidity constrains the functionality of Unmanned Aerial Systems in search, rescue, and reconnaissance missions. A new adaptive scalable and intelligent path-planning algorithm is developed based on biological evolution and metaheuristic methods that can operate independently of predefined waypoints using an energy constraint cost function to optimize scalability of number of Unmanned Aerial Systems search pathways in dynamically unknown areas. To demonstrate the capabilities of the proposed optimization techniques, several simulations are performed for various number of agents attempting to track both static and dynamic targets which remain unknown to all agents until they are within a range of UAS. Results show that the agents are able to find the desired targets without any a priori information.