CIAO⋆: MPC-based Safe Motion Planning in Predictable Dynamic Environments

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Tobias Schoels, Per Rutquist, Luigi Palmieri, Andrea Zanelli, Kai O. Arras, and Moritz Diehl
CIAO⋆: MPC-based Safe Motion Planning in Predictable Dynamic Environments
Proceedings of the IFAC World Congress 2020

 

 

Abstract

Robots have been operating in dynamic environments and shared workspaces fordecades. Most optimization based motion planning methods, however, do not consider themovement of other agents, e.g. humans or other robots, and therefore do not guarantee collision avoidance in such scenarios. This paper builds upon the Convex Inner ApprOximation (CIAO) method and proposes a motion planning algorithm that guarantees collision avoidance in predictable dynamic environments. Furthermore, it generalizes CIAO’s free region concept to arbitrary norms and proposes a cost function to approximate time optimal motion planning. The proposed method, CIAO⋆, finds kinodynamically feasible and collision free trajectories for constrained single body robots using model predictive control (MPC). It optimizes the motion of one agent and accounts for the predicted movement of surrounding agents and obstacles. The experimental evaluation shows that CIAO⋆ reaches close to time optimal behaviors.

 

@inproceedings{schoelsIFAC2020,
title={CIAO $\^{}$\backslash$star $: MPC-based Safe Motion Planning in Predictable Dynamic Environments},
author={Schoels, Tobias and Rutquist, Per and Palmieri, Luigi and Zanelli, Andrea and Arras, Kai O and Diehl, Moritz},
booktitle={IFAC {W}orld {C}ongress},
year={2020}
}