Chance-constrained collision avoidance for MAVs in dynamic environments

Chance-constrained collision avoidance for MAVs in dynamic environments

Zhu, H. (TU Delft Learning & Autonomous Control)
Alonso-Mora, J. (TU Delft Learning & Autonomous Control)

2019

Safe autonomous navigation of microair vehicles in cluttered dynamic environments is challenging due to the uncertainties arising from robot localization, sensing, and motion disturbances. This letter presents a probabilistic collision avoidance method for navigation among other robots and moving obstacles, such as humans. The approach explicitly considers the collision probability between each robot and obstacle and formulates a chance constrained nonlinear model predictive control problem (CCNMPC). A tight bound for approximation of collision probability is developed, which makes the CCNMPC formulation tractable and solvable in real time. For multirobot coordination, we describe three approaches, one distributed without communication (constant velocity assumption), one distributed with communication (of previous plans), and one centralized (sequential planning). We evaluate the proposed method in experiments with two quadrotors sharing the space with two humans and verify the multirobot coordination strategy in simulation with up to sixteen quadrotors.

collision avoidance
motion and path planning
Path planning for multiple mobile robots or agents

http://resolver.tudelft.nl/uuid:4c2e8664-4eb7-45ff-9e7e-a57ae643a371

https://doi.org/10.1109/LRA.2019.2893494

2019-07-16

2377-3766

IEEE Robotics and Automation Letters, 4 (2), 776-783

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2019 H. Zhu, J. Alonso-Mora