Collaborative Control of Autonomous Swarms Under Communication and Resource Constraints

In order to integrate the Gibbs sampler based path planning algorithm in applications, a two-level scheme is proposed by combining high-level path planing and low-level vehicle motion control. The high-level path planing module mainly addresses the path generation. The low-level motion control module aims to follow the desired path by considering vehicle dynamics. A model predictive based (MPC) based motion control for car-like nonholonomic UAVs is investigated. Multiple control objectives, e.g., minimizing tracking error, avoiding actuator/state saturation, and minimizing control effort, are easily encoded in the objective function. Two numerical optimization approaches, gradient descendent approach and dynamic programming approach, are studied to strike the balance between computation time and complexity.