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Temporal Jump Point Search (JPST) is a recently introduced algorithm for grid-optimal pathfinding among dynamic temporal obstacles. In this work we consider JPST as a low-level planner in Multi-Agent Path Finding (MAPF). We investigate how the canonical ordering of JPST can negatively impact MAPF performance and we consider several strategies which allow us to overcome these limitations. Experiments show our new CBS/JPST approach can substantially improve on CBS/SIPP, a contemporary and leading method from the area.
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A new deep Q-learning method with dynamic epsilon adjustment and path planner assisted techniques for Turtlebot mobile robot
Deep Q-learning (DQL) method has been proven a great success in autonomous mobile robots. However, the routine of DQL can often yield improper agent behavior (multiple circling-in-place actions) that comes with long training episodes until convergence. To address such problem, this project develops novel techniques that improve DQL training in both simulations and physical experiments. Specifically, the Dynamic Epsilon Adjustment method is integrated to reduce the frequency of non-ideal agent behaviors and therefore improve the control performance (i.e., goal rate). A Dynamic Window Approach (DWA) global path planner is designed in the physical training process so that the agent can reach more goals with less collision within a fixed amount of episodes. The GMapping Simultaneous Localization and Mapping (SLAM) method is also applied to provide a SLAM map to the path planner. The experiment results demonstrate that our developed approach can significantly improve the training performance in both simulation and physical training environment.
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- PAR ID:
- 10490404
- Editor(s):
- Manser, Kimberly E.; Rao, Raghuveer M.; Howell, Christopher L.
- Publisher / Repository:
- SPIE
- Date Published:
- Journal Name:
- Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications
- ISBN:
- 9781510661721
- Page Range / eLocation ID:
- 34
- Format(s):
- Medium: X
- Location:
- Orlando, United States
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2663695
