research-article

Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

Authors:

Mayur J. Bency,

Ahmed H. Qureshi,

Michael C. YipAuthors Info & Claims

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Pages 3965 - 3972

https://doi.org/10.1109/IROS40897.2019.8968089

Published: 01 November 2019 Publication History

Abstract

Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot’s actuation or configuration space, where popular pathfinding methods such as A<sup>*</sup>, RRT<sup>*</sup>, get exponentially more computationally expensive to execute as the dimensionality increases or the spaces become more cluttered and complex. On the other hand, if one were to save the entire set of paths connecting all pair of locations in the configuration space a priori, one would run out of memory very quickly. In this work, we introduce a novel way of producing fast and optimal motion plans for static environments by using a stepping neural network approach, called OracleNet. OracleNet uses Recurrent Neural Networks to determine end-to-end trajectories in an iterative manner that implicitly generates optimal motion plans with minimal loss in performance in a compact form. The algorithm is straightforward in implementation while consistently generating near-optimal paths in a single, iterative, end-to-end roll-out. In practice, OracleNet generally has fixed-time execution regardless of the configuration space complexity while outperforming popular pathfinding algorithms in complex environments and higher dimensions<sup>1</sup>.

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Cited By

Luo YSun CTenenbaum JDu YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Pot potential based diffusion motion planning ential based diffusion motion planningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693430(33486-33510)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693430
Zhang RYu CChen JFan CGao SKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Learning-based motion planning in dynamic environments using GNNs and temporal encodingProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602445(30003-30015)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602445
Huang LZang XGong YYuan BMitra TYoung EXiong J(2022)Hardware Architecture of Graph Neural Network-Enabled Motion Planner (Invited Paper)Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3561113(1-7)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3561113
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

6597 pages

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IEEE Press

Publication History

Published: 01 November 2019

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Cited By

Luo YSun CTenenbaum JDu YSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Pot potential based diffusion motion planning ential based diffusion motion planningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693430(33486-33510)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693430
Zhang RYu CChen JFan CGao SKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Learning-based motion planning in dynamic environments using GNNs and temporal encodingProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602445(30003-30015)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602445
Huang LZang XGong YYuan BMitra TYoung EXiong J(2022)Hardware Architecture of Graph Neural Network-Enabled Motion Planner (Invited Paper)Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design10.1145/3508352.3561113(1-7)Online publication date: 30-Oct-2022
https://dl.acm.org/doi/10.1145/3508352.3561113
Yu CGao SRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Reducing collision checking for sampling-based motion planning using graph neural networksProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540588(4274-4289)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3540588

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