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Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

Abstract:

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and covariances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For c...

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Publication status:
Published

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Calliess, JP More by this author
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Institution:
University of Oxford
Oxford college:
Exeter College
Department:
Oxford, MPLS, Engineering Science
More by this author
Institution:
University of Oxford
Oxford college:
Somerville College
Department:
Oxford, MPLS, Engineering Science
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Funding agency for:
Osborne, Michael
Publication date:
2014
Pubs id:
pubs:493161
URN:
uri:b95ce390-ec3b-4ae3-8cb1-213deea288f2
UUID:
uuid:b95ce390-ec3b-4ae3-8cb1-213deea288f2
Local pid:
pubs:493161
ISBN:
9781634391313
Keywords:

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