Reactive Integrated Motion Planning and Execution
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Williams_Reactive integrated.pdf
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Author(s)
Hofmann, Andreas
Helbert, Justin C.
Fernandez Gonzalez, Enrique
Smith, Scott
Williams, Brian
Date Issued
July 2015
Journal
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
Publisher
AAAI Press/International Joint Conferences on Artificial Intelligence
Citation
Hofmann, Andreas et al. "Reactive Integrated Motion Planning and Execution" Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, 25-31 July, 2015.
Version
Author's final manuscript
Abstract
Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not
practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely
new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these
problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems.
Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which
imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://ijcai-15.org/index.php/accepted-papers
