On the performance of selective adaptation in state lattices for mobile robot motion planning in cluttered environments

Autonomous mobile robots require motion planning algorithms that match limitations of on-board computing resources to safely navigate complex environments. In situations where near-optimality is preferential to runtime performance, search spaces that optimize their local connectivity to improve the...

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Bibliographic Details
Main Authors: Napoli, Michael E., Biggie, Harel, Howard, Thomas M.
Format: Conference Proceeding
Language:English
Subjects:
Lattices
Mobile robots
Optimization
Planning
Runtime
Trajectory
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Summary:Autonomous mobile robots require motion planning algorithms that match limitations of on-board computing resources to safely navigate complex environments. In situations where near-optimality is preferential to runtime performance, search spaces that optimize their local connectivity to improve the global optimality of generated solutions are desirable. However, not all nodes in the search space benefit equally from optimization which can result in an inefficient use of computational resources and unnecessary increase in runtime. To address this limitation, we propose an approach called the Selectively Adaptive State Lattice which uses a heuristic based on the local environment to selectively perform optimization and obtain a balance between runtime performance and relative optimality. We present a statistical evaluation of local connectivity optimization and global search with the State Lattice, Adaptive State Lattice, and Selectively Adaptive State Lattice algorithms in randomly generated obstacle fields. We further highlight the performance of each method on a Clearpath Robotics TurtleBot2 in a qualitative physical experiment.
ISSN:2153-0866
DOI:10.1109/IROS.2017.8206309