Incremental GA-Based 3D Trajectory Optimization for Powered Parachute Aerial Delivery Systems

This paper presents an offline optimal trajectory planning method for powered parachutes (PPCs) using dynamic model simulations, emphasizing their potential in applications such as remote sensing and aerial delivery systems. A six-degrees-of-freedom (6-DOF) dynamic model of the PPC is developed, complemented by a novel optimization technique called Incremented Genetic Algorithms (IGA). IGA improve the computational efficiency by dynamically increasing the number of variables only when optimization goals are unmet, eliminating the need to predefine input variable counts. This approach significantly reduces the computational time and CPU usage while maintaining cost-effectiveness for 3D trajectory planning. The proposed method was validated on three trajectories under diverse constraints, including the time, position, and predefined obstacles. The results demonstrate that IGA can effectively generate optimal trajectories using a single control parameter (the parachute steering angle) and a minimal number of control points, showcasing its practicality and efficiency.