Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance

Andersson, Olov, 1979- (author): Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,KPLAB

Ljungqvist, Oskar, 1990- (author): Linköpings universitet,Reglerteknik,Tekniska fakulteten

Tiger, Mattias, 1989- (author): Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,KPLAB

Axehill, Daniel, 1978- (author): Linköpings universitet,Reglerteknik,Tekniska fakulteten

Heintz, Fredrik, 1975- (author): Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,KPLAB

(creator_code:org_t)

Institute of Electrical and Electronics Engineers (IEEE), 2018
2018
English.
In: 2018 IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538613955 - 9781538613948 - 9781538613962 ; , s. 4467-4474

Related links:: https://liu.diva-por... (primary) (Raw object); show more...; https://urn.kb.se/re...; https://doi.org/10.1...; show less...

Abstract Subject headings

A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

By the author/editor: Andersson, Olov, ...; Ljungqvist, Oska ...; Tiger, Mattias, ...; Axehill, Daniel, ...; Heintz, Fredrik, ...

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

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