3D Real-Time Energy Efficient Path Planning for a Fleet of Fixed-Wing UAVs

UAV path planning requires finding an optimal (or sub-optimal) collision free path in a cluttered environment, while taking into account geometric, physical and temporal constraints, eventually allowing UAVs to perform their tasks despite several uncertainty sources. This paper reviews the current state-of-the-art in path planning, and subsequently introduces a novel node-based algorithm based on the called EEA*. EEA* is based on the A* Search algorithm and aims at mitigating some of its key limitations. The proposed EEA* deals with 3D environments, it provides robustness quickly converging to the solution, it is energy efficient and it is realtime implementable and executable. Along with the proposed EEA*, a local path planner is developed to cope with unknown dynamic threats in the environment. Applicability and effectiveness is first demonstrated via simulated experiments using a fixed-wing UAV that operates in different mountain-like 3D environments in the presence of several unknown dynamic obstacles. Then, the algorithm is applied in a multi-agent setting with three UAVs that are commanded to follow their respective paths in a safe way. The energy efficiency of the EEA* algorithm has also been tested and compared with the conventional A* algorithm.