Overview
Path planning and trajectory optimization is a fundamental task for operating unmanned aircraft systems (UAS). The aim is to design trajectories and optimization-based feedback controllers, which are able to obey constraints and are efficient with respect to a given objective function. Herein, constraints usually result from technical or physical limitations (e.g. dynamic pressure, load factor, control bounds) or are imposed to avoid collisions or no-fly zones. Typical objective functions include, e.g., energy consumption, time efficiency, noise emission, or detection probability. We apply numerical optimal control techniques and model-predictive control schemes. More specifically we develop algorithms and software packages for the following problem classes:
- real-time optimization methods and sensitivity analysis for path planning
- linear and nonlinear model-predictive control (MPC) for path following and path planning
- imitation learning model-predictive control (IL-MPC)
- reinforcement learning (RL) for path planning and collision avoidance
- dynamic programming (DP) for path planning and dynamic scheduling
- dynamic scheduling and dynamic vehicle routing for the coordination of multi-agent systems
The methods are applied to the following scenarios (collected examples are provided below):
- reconnaissance maneuvers
- co-operative UGV and UAS maneuvers
- coordination of multi-agent systems
- obstacle avoidance for quadrocopters and helicopters
We use the robot operating system ROS2 and the Unreal Engine for visualization.