Presentation
HIP-MaN: Hippocampus-Inspired Periodic Mapping and Navigation for Autonomous Mobile Robot
DescriptionReal-time path planning is essential for autonomous mobile robots operating in complex and dynamic real-world environments. Conventional path planning algorithms are limited by the need to store the entire environment map in memory and to maintain either the cost values of explored nodes or their connectivity relationships. Consequently, both memory and computational loads increase rapidly with the scale and complexity of the environment. This overhead is particularly exacerbated when replanning is frequent due to unpredictable dynamic obstacles. In this paper, we propose the hippocampus-inspired periodic mapping and navigation (HIP-MaN) algorithm. This employs multi-periodic grid modules to encode unique spatial locations as phase combinations, effectively compressing the entire environment into a compact periodic representation. HIP-MaN directly computes the goal direction based solely on the phase differences between grid modules and generates detours only when a collision is predicted, minimizing replanning costs. In a 200x200 m^2 environment, HIP-MaN demonstrates near-optimal path planning quality, showing only a 5-22% increase over the optimal path length while achieving 3-50x and 5-346x faster path generation in static and dynamic environments, respectively.
Event Type
Research Manuscript
TimeTuesday, July 282:08pm - 2:21pm PDT
LocationMtg Room 202C