Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian Processes

Chiara Fulgenzi 1 Christopher Tay 1 Anne Spalanzani 1, 2 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 LIG - Laboratoire d'Informatique de Grenoble
Abstract : The paper describes a navigation algorithm for dynamic, uncertain environment. Moving obstacles are supposed to move on typical patterns which are pre-learned and are represented by Gaussian processes. The planning algorithm is based on an extension of the Rapidly-exploring Random Tree algorithm, where the likelihood of the obstacles trajectory and the probability of collision is explicitly taken into account. The algorithm is used in a partial motion planner, and the probability of collision is updated in real-time according to the most recent estimation. Results show the performance of the navigation algorithm for a car-like robot moving among dynamic obstacles with probabilistic trajectory prediction.
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Conference papers
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https://hal.inria.fr/inria-00332595
Contributor : Chiara Fulgenzi <>
Submitted on : Tuesday, October 21, 2008 - 11:32:41 AM
Last modification on : Thursday, April 11, 2019 - 12:04:11 PM
Long-term archiving on : Monday, June 7, 2010 - 7:06:14 PM

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Chiara Fulgenzi, Christopher Tay, Anne Spalanzani, Christian Laugier. Probabilistic navigation in dynamic environment using Rapidly-exploring Random Trees and Gaussian Processes. IEEE/RSJ 2008 International Conference on Intelligent RObots and Systems, Sep 2008, Nice, France. ⟨inria-00332595⟩

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