Unmanned Aircraft Path Planning Using Air Traffic Density

Path planning algorithms that can account for the complexity of the airspace are imperative for operations of unmanned air traffic in Advanced Air Mobility applications such as parcel delivery, air taxis, and medical transport. This paper proposes an algorithm based on Rapidly-exploring Random Trees Star (RRT*) to plan paths for unmanned aircraft navigating in environments with solid and permeable obstacles. Thresholding in the collision-checker and distance-dependent penalties in the objective function of RRT
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are introduced in the algorithm. The path planner is applied to the case where estimated air traffic density data is used to define the permeability of obstacles. The capabilities of the algorithm and the applicability to the air traffic density case study are demonstrated through simulations of flight scenarios around an international airport.