Nonlinear Efficient Path Planning for UAV in Complex Three-Dimensional Urban Environment

This paper investigates the problem of nonlinear and efficient path planning for UAVs in complex urban three-dimensional environments. Aiming at the issues of low computational efficiency, inability to meet real-time requirements, and insufficient obstacle avoidance capability that are present in traditional path planning methods in complex urban environments, a new nonlinear path planning method is proposed. This method effectively deals with the kinematic constraints and obstacle avoidance requirements of UAVs by integrating sampling, search, and optimization techniques. Through kinematic modeling and mathematical analysis, the path planning problem is transformed into an optimal control problem and solved using an iterative algorithm. Experimental results show that this method outperforms existing Particle Swarm Optimization (PSO), Rapidly-exploring Random Tree (RRT), and its variant RRT∗ algorithms in terms of trajectory length and computation time while satisfying the kinematic constraints, demonstrating potential for practical applications such as urban logistics and transportation.