A UAV 3D Path Planning Algorithm Integrating Improved RRT Algorithm and Ant Colony Optimization Algorithm

In response to the challenges of low convergence path quality and susceptibility to fall into deadlocks encountered in the traditional ant colony optimization algorithm(ACO) for drone path planning in complex three-dimensional environments, this paper proposes an enhanced ant colony optimization algorithm(ACO-RRT). This algorithm conducts deadlock detection after each step of ant movement and utilizes the improved rapidly exploring random tree algorithm(RRT) for deadlock resolution, enabling ants to break free from deadlocks and discover feasible paths. Subsequently, the obtained path undergoes optimization. The optimized path serves as the guiding path for subsequent ants. Experimental results demonstrate that the ACO-RRT algorithm can not only find shorter paths compared to the traditional ACO algorithm but also effectively addresses the issue of ants being trapped in deadlocks without a means of escape.