TR2016-052

Particle Filtering for Online Motion Planning with Task Specifications


    •  Berntorp, K.; Di Cairano, S., "Particle Filtering for Online Motion Planning with Task Specifications", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525232, July 2016, pp. 2123-2128.
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      • @inproceedings{Berntorp2016jul2,
      • author = {Berntorp, K. and {Di Cairano}, S.},
      • title = {Particle Filtering for Online Motion Planning with Task Specifications},
      • booktitle = {American Control Conference (ACC)},
      • year = 2016,
      • pages = {2123--2128},
      • month = jul,
      • doi = {10.1109/ACC.2016.7525232},
      • url = {http://www.merl.com/publications/TR2016-052}
      • }
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  • Research Area:

    Mechatronics


A probabilistic framework for online motion planning of vehicles in dynamic environments is proposed. We develop a sampling-based motion planner that incorporates prediction of obstacle motion. A key feature is the introduction of task specifications as artificial measurements, which allows us to cast the exploration phase in the planner as a nonlinear, possibly multimodal, estimation problem, which is effectively solved using particle filtering. For certain parameter choices, the approach is equivalent to solving a nonlinear estimation problem using particle filtering. The proposed approach is illustrated on a simulated autonomous-driving example. The results indicate that our method is computationally efficient, consistent with the task specifications, and computes dynamically feasible trajectories.