This paper presents an innovative kinodynamic motion planning algorithm for Unmanned Aircraft Systems, called MP-RRT#. MP-RRT# leverages the idea of RRT# and the Model Predictive Control strategy to solve a motion planning problem under differential constraints. Similar to RRT#, the algorithm explores the map by constructing an asymptotically optimal graph. Each time the graph is extended with a new vertex, a forward simulation is performed with a Model Predictive Control to evaluate the motion between two adjacent vertices and compute the trajectory in the state space and the control space. As result, the MP-RRT# algorithm generates a feasible trajectory for the UAS satisfying dynamic constraints. Preliminary simulation results corroborate the proposed approach, in which the computed trajectory is executed by a simulated drone controlled with the PX4 autopilot.
Model Predictive Sample-based Motion Planning for Unmanned Aircraft Systems
2021-06-15
995914 byte
Conference paper
Electronic Resource
English
Model Predictive Sample-based Motion Planning for Unmanned Aircraft Systems
BASE | 2021
|MP-RRT#: a Model Predictive Sampling-based motion planning algorithm for Unmanned Aircraft Systems
BASE | 2021
|Nonlinear model predictive controller robustness extension for unmanned aircraft
Emerald Group Publishing | 2015
|