TR2016-052
Particle Filtering for Online Motion Planning with Task Specifications
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- "Particle Filtering for Online Motion Planning with Task Specifications", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525232, July 2016, pp. 2123-2128.BibTeX TR2016-052 PDF Videos
- @inproceedings{Berntorp2016jul2,
- author = {Berntorp, Karl and Di Cairano, Stefano},
- 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 = {https://www.merl.com/publications/TR2016-052}
- }
,
- "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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Research Area:
Abstract:
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.
Related News & Events
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NEWS MERL makes a strong showing at the American Control Conference Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Mouhacine Benosman; Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine LearningBrief- The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.