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 / Primatesta, Stefano; Pagliano, Alessandro; Guglieri, Giorgio; Rizzo, Alessandro. - ELETTRONICO. - (2021), pp. -1. ((Intervento presentato al convegno 2021 International Conference on Unmanned Aircraft Systems (ICUAS) tenutosi a Atene, Grecia nel 15-18 Giugno 2021 [10.1109/ICUAS51884.2021.9476836].

Model Predictive Sample-based Motion Planning for Unmanned Aircraft Systems

Stefano Primatesta;Giorgio Guglieri;Alessandro Rizzo
2021

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2913912