Primatesta, Stefano (Politecnico di Torino), Pagliano, Alessandro (Politecnico di Torino), Guglieri, Giorgio (Politecnico di Torino), Rizzo, Alessandro (Politecnico di Torino)

Model Predictive Sample-Based Motion Planning for Unmanned Aircraft Systems

Scheduled for presentation during the Regular Session "Path Planning I" (WeA3), Wednesday, June 16, 2021, 10:30−10:50, Edessa

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 16, 2024

Abstract

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.