3D Off-Line Path Planning for Autonomous Airships in Restricted Known Environments.

The paper presents a Genetic Algorithm (G.A.) for off-line path planning of Autonomous Small Airships in known 3D environments with special consideration of restricted areas. The algorithm assumes that the airship is used in Fire Fighting and Mine Detection projects, so the aircraft will fly only a few meters above the ground, which means there is a high possibility of collision with obstacles. The task of the Off-Line Path Planner algorithm is to find an optimal route to visit all the predefined locations for airborne measurement exactly once per mission, without any collisions with environmental obstacles and to avoid fly over a defined restricted area. The planner task posed here is an NP problem. This paper proposes a 3D Off-Line Path Planner using G.A. including chromosome representation, G.A. crossover and collision avoidance with known obstacles. The proposed algorithm is implemented using MATLAB with Genetic Algorithms and Mapping Toolboxes. The proposed algorithm is tested using real maps of our research airfield and the result shows that the algorithm finds a near-optimal collision free path for the airship.