DPOFEC: A Dynamic UAV-Based Path Planning Optimization Framework with Federated Learning and Edge Computing in Complex Environments

In complex map environments, the Rapidlyexploring Random Tree (RRT) algorithm is recognized as an efficient initial path planning method for unmanned aerial vehicles (UAVs). Relying solely on centralized computation or planning capabilities of a single node often faces challenges such as limited device resources, vulnerability of navigation points to interception, and insufficient real-time adaptability. This paper proposes a distributed path optimization framework based on federated learning (FL) and edge computing (EC), referred to as DPOFEC, which formulates the path optimization problem as a global optimization task within the framework of FL. First, edge nodes of the server execute initial path planning using the RRT algorithm to generate local path segments. Then, the FL framework aggregates the weights uploaded by each edge node and optimizes the path points. Finally, the enclosed and safe sphereshaped corridors are designed around the optimized global path points, with the size of these corridors dynamically adjusted to accommodate obstacle distributions and the UAV's flight state. Experiments demonstrate that in a simple scenario (Case 1), the proposed method improves the path generation processing time by approximately 38 % and 43 %, compared to the traditional RRT algorithm attempts 1 and 2, respectively. In a complex scenario (Case 2), the improvements are approximately 51 % and 40 %, respectively. Leveraging distributed collaboration, the algorithm enhances the performance and robustness of path planning while effectively protecting the privacy of path-point data.