Time-bounded Lattice for Efficient Planning in Dynamic Environments

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collision avoidance
remotely operated vehicles
road vehicles
state-space methods
cluttered environments
data structure
dynamic environments
moving objects
moving obstacles
path planning algorithm
state-of-the-art planning algorithms
time-bounded lattice
trajectory prediction
vehicle navigation
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For vehicles navigating initially unknown cluttered environments, current state-of-the-art planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dynamic. Planning under these conditions is more difficult for two reasons. First, tracking and predicting the trajectories of moving objects (i.e., cars, humans) is very noisy. Second, the planning process is computationally more expensive because of the increased dimensionality of the state-space, with time as an additional variable. Moreover, re-planning needs to be invoked more often since the trajectories of moving obstacles need to be constantly re-estimated. In this paper, we develop a path planning algorithm that addresses these challenges. First, we choose a representation of dynamic obstacles that efficiently models their predicted trajectories and the uncertainty associated with the predictions. Second, to provide real-time guarantees on the performance of planning with dynamic obstacles, we propose to utilize a novel data structure for planning - a time-bounded lattice - that merges together short-term planning in time with longterm planning without time. We demonstrate the effectiveness of the approach in both simulations with up to 30 dynamic obstacles and on real robots.

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Copyright 2009 IEEE. Reprinted from Kushleyev, A.; Likhachev, M., "Time-bounded lattice for efficient planning in dynamic environments," Robotics and Automation, 2009. ICRA '09. IEEE International Conference on , vol., no., pp.1662-1668, 12-17 May 2009 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5152860&isnumber=5152175 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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