Sampling-Based Path Planning for UAV Collision Avoidance

Abstract

The ability to avoid collisions with moving obstacles, such as commercial aircraft is critical to the safe operation of unmanned aerial vehicles (UAVs) and other air traffic. This paper presents the design and implementation of sampling-based path planning methods for a UAV to avoid collision with commercial aircraft and other moving obstacles. In detail, the authors develop and demonstrate a method based on the closed-loop rapidly-exploring random tree algorithm and three variations of it. The variations are: 1) simplification of trajectory generation strategy; 2) utilization of intermediate waypoints; 3) collision prediction using reachable set. The methods were validated in software-in-the-loop simulations, hardware-in-the-loop simulations, and real flight experiments. It is shown that the algorithms are able to generate collision free paths in real time for the different types of UAVs among moving obstacles of different numbers, approaching angles, and speeds.