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The use of underwater robot systems, including Autonomous Underwater Vehicles (AUVs), has been studied as an effective way of monitoring and exploring dynamic aquatic environments. Furthermore, advances in artificial intelligence techniques and computer processing led to a significant effort towards fully autonomous navigation and energy-efficient approaches. In this work, we formulate a reinforcement learning framework for long-term navigation of underwater vehicles in dynamic environments using the techniques of tile coding and eligibility traces. Simulation results used actual oceanic data from the Regional Ocean Modeling System (ROMS) data set collected in Southern California Bight (SCB) region, California, USA
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Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Third BARN Challenge at ICRA 2024 [Competitions]
The third Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) in Yokohama, Japan and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Similar to the trend in the first and second BARN Challenges at ICRA 2022 and 2023 in Philadelphia (North America) and London (Europe), the third BARN Challenge in Yokohama (Asia) became more regional, i.e., mostly Asian teams participated. The size of the competition has slightly shrunk (six simulation teams, four of which were invited to the physical competition). The competition results, compared to the last two years, suggest that the field has adopted new machine learning approaches, while at the same time slightly converged to a few common practices. However, the regional nature of the physical participants suggests a challenge to promote wider participation all over the world and provide more resources to travel to the venue. In this article, we discuss the challenge, the approaches used by the three winning teams, and lessons learned to direct future research and competitions.
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- Award ID(s):
- 2350352
- PAR ID:
- 10596639
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Robotics & Automation Magazine
- Volume:
- 31
- Issue:
- 3
- ISSN:
- 1070-9932
- Page Range / eLocation ID:
- 197 to 204
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/MRA.2024.3427891
