Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns
This paper presents a real-time path planning algorithm which can guarantee probabilistic feasibility for autonomous robots subject to process noise and an uncertain environment, including dynamic obstacles with uncertain motion patterns. The key contribution of the work is the integration of a...
| Main Authors: | , , , , |
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| Format: | Technical Report |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/64738 |
| _version_ | 1826199193242304512 |
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| author | Luders, Brandon D. Aoude, Georges S. Joseph, Joshua M. Roy, Nicholas How, Jonathan P. |
| author_facet | Luders, Brandon D. Aoude, Georges S. Joseph, Joshua M. Roy, Nicholas How, Jonathan P. |
| author_sort | Luders, Brandon D. |
| collection | MIT |
| description | This paper presents a real-time path planning algorithm which can guarantee
probabilistic feasibility for autonomous robots subject to process noise and an
uncertain environment, including dynamic obstacles with uncertain motion
patterns. The key contribution of the work is the
integration of a novel method for modeling dynamic obstacles with uncertain future
trajectories. The method, denoted as RR-GP, uses a learned motion pattern model
of the dynamic obstacles to make long-term predictions of their future paths. This is done by combining the
flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach,
a sampling-based reachability computation method which ensures dynamic
feasibility. This prediction model is then utilized within chance-constrained rapidly-exploring random
trees (CC-RRT), which uses chance constraints to explicitly achieve probabilistic
constraint satisfaction while maintaining the computational
benefits of sampling-based algorithms. With RR-GP embedded in the CC-RRT framework, theoretical guarantees
can be demonstrated for linear systems subject to Gaussian uncertainty,
though the extension to nonlinear systems is also considered. Simulation results
show that the resulting approach can be used in real-time to efficiently and
accurately execute safe paths. |
| first_indexed | 2024-09-23T11:16:14Z |
| format | Technical Report |
| id | mit-1721.1/64738 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:16:14Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | mit-1721.1/647382019-04-12T07:21:19Z Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns Luders, Brandon D. Aoude, Georges S. Joseph, Joshua M. Roy, Nicholas How, Jonathan P. probabilistic path planning intention prediction gaussian processes uncertainty in predictability collision avoidance dynamic obstacles probabilistic constraint satisfaction sampling-based reachability This paper presents a real-time path planning algorithm which can guarantee probabilistic feasibility for autonomous robots subject to process noise and an uncertain environment, including dynamic obstacles with uncertain motion patterns. The key contribution of the work is the integration of a novel method for modeling dynamic obstacles with uncertain future trajectories. The method, denoted as RR-GP, uses a learned motion pattern model of the dynamic obstacles to make long-term predictions of their future paths. This is done by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation method which ensures dynamic feasibility. This prediction model is then utilized within chance-constrained rapidly-exploring random trees (CC-RRT), which uses chance constraints to explicitly achieve probabilistic constraint satisfaction while maintaining the computational benefits of sampling-based algorithms. With RR-GP embedded in the CC-RRT framework, theoretical guarantees can be demonstrated for linear systems subject to Gaussian uncertainty, though the extension to nonlinear systems is also considered. Simulation results show that the resulting approach can be used in real-time to efficiently and accurately execute safe paths. 2011-07-04T21:36:27Z 2011-07-04T21:36:27Z 2011-07-04 Technical Report http://hdl.handle.net/1721.1/64738 ;ACL11-02 Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf |
| spellingShingle | probabilistic path planning intention prediction gaussian processes uncertainty in predictability collision avoidance dynamic obstacles probabilistic constraint satisfaction sampling-based reachability Luders, Brandon D. Aoude, Georges S. Joseph, Joshua M. Roy, Nicholas How, Jonathan P. Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns |
| title | Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns |
| title_full | Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns |
| title_fullStr | Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns |
| title_full_unstemmed | Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns |
| title_short | Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns |
| title_sort | probabilistically safe avoidance of dynamic obstacles with uncertain motion patterns |
| topic | probabilistic path planning intention prediction gaussian processes uncertainty in predictability collision avoidance dynamic obstacles probabilistic constraint satisfaction sampling-based reachability |
| url | http://hdl.handle.net/1721.1/64738 |
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