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Dispertio : Optimal Sampling For Safe Deterministic Motion Planning
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- Palmieri, Luigi (author)
- Robert Bosch GmbH, Corp Res, D-70049 Stuttgart, Germany.
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- Bruns, Leonard (author)
- KTH,Robotik, perception och lärande, RPL,RWTH Aachen University, Germany
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- Meurer, Michael (author)
- Rhein Westfal TH Aachen, German Aerosp Ctr DLR, D-82234 Wessling, Germany.
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- Robert Bosch GmbH, Corp Res, D-70049 Stuttgart, Germany Robotik, perception och lärande, RPL (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2020
- 2020
- English.
- In: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 5:2, s. 362-368
- Journal article (peer-reviewed)
Abstract
Subject headings
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- A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Motion and path planning
- nonholonomic motion planning
- reactive and sensor-based planning
Publication and Content Type
- ref (subject category)
- art (subject category)
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