Reference : A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
Engineering, computing & technology : Aerospace & aeronautics engineering
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/41905
A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles
English
Castillo Lopez, Manuel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ludivig, Philippe [ispace Europe]
Sajadi-Alamdari, Seyed Amin [> >]
Sanchez Lopez, Jose Luis [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Olivares Mendez, Miguel Angel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Voos, Holger [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Engineering Research Unit >]
21-Feb-2020
IEEE Robotics and Automation Letters
Institute of Electrical and Electronics Engineers
5
2
3620 - 3625
Yes (verified by ORBilu)
International
2377-3766
[en] Motion Planning ; Stochastic Optimization ; Robotics
[en] Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/41905
10.1109/LRA.2020.2975759
https://ieeexplore.ieee.org/abstract/document/9006821
This paper has been accepted for publication in the IEEE Robotics and Automation Letters. Please cite the paper as: M. Castillo-Lopez, P. Ludivig, S. A. Sajadi-Alamdari, J. L. Sanchez-Lopez, M. A. Olivares-Mendez, H. Voos, "A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles", IEEE Robotics and Automation Letters (RA-L), 2020.
FnR ; FNR10484117 > Holger Voos > BEST-RPAS > Robust Emergency Sense-and-Avoid Capability for Small Remotely Piloted Aerial Systems > 01/02/2016 > 31/01/2019 > 2015

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