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Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten. (KPLAB)
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-1795-5992
Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten. (KPLAB)ORCID-id: 0000-0002-8546-4431
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0001-6957-2603
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2018 (engelsk)Inngår i: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 4467-4474Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018. s. 4467-4474
Serie
Conference on Decision and Control (CDC), ISSN 2576-2370 ; 2018
Emneord [en]
Motion Planning, Optimal Control, Autonomous System, UAV, Dynamic Obstacle Avoidance, Robotics
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-152131DOI: 10.1109/CDC.2018.8618964ISBN: 9781538613955 (digital)ISBN: 9781538613948 (digital)ISBN: 9781538613962 (digital)OAI: oai:DiVA.org:liu-152131DiVA, id: diva2:1256821
Konferanse
2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA
Forskningsfinansiär
VINNOVAKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research CouncilLinnaeus research environment CADICSCUGS (National Graduate School in Computer Science)
Merknad

This work was partially supported by FFI/VINNOVA, the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF) project Symbicloud, the ELLIIT Excellence Center at Linköping-Lund for Information Technology, Swedish Research Council (VR) Linnaeus Center CADICS, and the National Graduate School in Computer Science, Sweden (CUGS).

Tilgjengelig fra: 2018-10-18 Laget: 2018-10-18 Sist oppdatert: 2019-01-30bibliografisk kontrollert

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Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance(906 kB)83 nedlastinger
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Andersson, OlovLjungqvist, OskarTiger, MattiasAxehill, DanielHeintz, Fredrik

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