A Collision-Free Path Planning Algorithm for Unmanned Aerial Vehicle Delivery
This work addresses path planning for UAV delivery systems, which is incremental as it builds on existing A* methods with a new heuristic for dynamic constraints.
The paper tackles the problem of collision-free path planning for UAV delivery in uncertain and dynamic environments by formulating it as a Constraint Satisfaction Problem and proposing an A*-based algorithm with a novel heuristic that includes waiting time, showing optimality and simulating it with Singapore's airspace to analyze collision-free route percentages as request density increases.
Path planning is important for the autonomy of Unmanned Aerial Vehicle (UAV), especially for scheduling UAV delivery. However, the operating environment of UAVs is usually uncertain and dynamic. Without proper planning, collisions may happen where multiple UAVs are congested. Besides, there may also be temporary no-fly zone setup by authorities that makes airspace unusable. Thus, proper pre-departure planning that avoids such places is needed. In this paper, we formulate this problem into a Constraint Satisfaction Problem to find a collision-free shortest path on a dynamic graph. We propose a collision-free path planning algorithm that is based on A* algorithm. The main novelty is that we invent a heuristic function that also considers waiting time. We later show that, with added waiting penalty, the proposed algorithm is optimal because the heuristic is admissible. Implementation of this algorithm simulates UAV delivery using Singapore's airspace structure. Our simulation exhibits desirable runtime performance. Using the proposed algorithm, the percentage of collision-free routes decreases as number of requests per unit area increases, and this percentage drops significantly at boundary value. Our empirical analysis could aid the decision-making of no-fly zone policy and infrastructure of UAV delivery.