Path planning for drone delivery in dense building environments

• Main Authors: Hu, Xinting, Wu, Yu, Pang, Bizhao
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering; Urban Air Mobility; Path Planning; Evolutionary Algorithm
• Online Access: https://hdl.handle.net/10356/170161; https://2023.ieee-itsc.org/
• Institution: Nanyang Technological University

Drones have been introduced into urban environments to facilitate our life such as cargo delivery services. However, the densely located buildings in urban areas pose challenges for safe drone operations due to the collision risk with buildings. To address this challenge, we propose a path planning method that leverages an improved ant colony optimization (IACO) algorithm. The algorithm improves the standard setting of ACO with an adaptive parameter mechanism and an update mechanism of pheromone intensity. A further improvement is made by introducing a rapidly exploring random tree (RRT) based mechanism to improve the search efficiency. Simulation results demonstrate that our proposed method significantly increases the convergence rate and the quality of solutions for path planning in complex city environments. It can consistently produce satisfactory solutions with a more rapid convergence rate in both two-dimensional and three-dimensional environments.