Convex-Neural RRT* uses neural guidance to predict waypoint regions, extracts convex sampling areas from them, and reports 30-75% faster planning than other neural RRT* methods with ~5% shorter paths and >99% success rate on 18 maps.
Neural RRT*: Learning-based optimal path planning,
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cs.RO 2years
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UNVERDICTED 2representative citing papers
Neural RRT* and Neural Informed RRT* yield up to 14% shorter paths and 55-75% smoother trajectories than standard RRT* in simulated obstacle environments, with Neural Informed RRT* performing best overall.
citing papers explorer
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Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning
Convex-Neural RRT* uses neural guidance to predict waypoint regions, extracts convex sampling areas from them, and reports 30-75% faster planning than other neural RRT* methods with ~5% shorter paths and >99% success rate on 18 maps.
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Performance Comparison of Classical and Neural Sampling Algorithms for Robotic Navigation
Neural RRT* and Neural Informed RRT* yield up to 14% shorter paths and 55-75% smoother trajectories than standard RRT* in simulated obstacle environments, with Neural Informed RRT* performing best overall.