UAV Path Planning Based on Multi-Resolution Algorithm and Markov Decision Process

To address the challenges of high computational complexity and poor real-time performance in UAV path planning within complex environments, this paper proposes a multi-resolution algorithm based on Markov Decision Processes. By defining the state space, action space, state transition probability function, and multi-objective reward function, the algorithm dynamically partitions the mission's environmental map into units of varying resolutions under communication-constrained conditions, thereby reducing computational overhead. The optimal policy is then derived through value iteration, which balances computational efficiency with navigation safety. Simulation results demonstrate that the proposed approach not only maintains the quality of path planning and satisfies communication constraints, but also significantly reduces computation time while keeping the collision probability close to zero.