Real-time Trajectory Design for Unmanned Aerial Vehicles using Receding Horizon Control
Yoshiaki Kuwata · 2003
This thesis investigates the coordination and control of fleets of unmanned aerial vehicles (UAVs). Future UAVs will operate autonomously, and their control systems must com-pensate for significant dynamic uncertainty. A hierarchical approach has been proposed to account for various types of uncertainty at different levels of the control system. The resulting controller includes task assignment, graph-based coarse path planning, detailed trajectory optimization using receding horizon control (RHC), and a low-level waypoint fol-lower. Mixed-integer linear programming (MILP) is applied to both the task allocation and trajectory design problems to encode logical constraints and discrete decisions together with the continuous vehicle dynamics. The MILP RHC uses a simple vehicle dynamics model in the near term and an approxi-mate path model in the long term. This combination gives a good estimate of the cost-to-go and greatly reduces the computational effort required to design the complete trajectory, but discrepancies in the assumptions made in the two models can lead to infeasible solutions. The primary contribution of this thesis is to extend the previous stable RHC formulation