Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning Article Swipe
Allen Jiang
,
Kangtong Mo
,
Satoshi Fujimoto
,
Michael D. Taylor
,
Sanjay Kumar
,
Chiotis Dimitrios
,
Elena Figuero
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.14568
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.14568
Solar trajectory monitoring is a pivotal challenge in solar energy systems, underpinning applications such as autonomous energy harvesting and environmental sensing. A prevalent failure mode in sustained solar tracking arises when the predictive algorithm erroneously diverges from the solar locus, erroneously anchoring to extraneous celestial or terrestrial features. This phenomenon is attributable to an inadequate assimilation of solar-specific objectness attributes within the tracking paradigm. To mitigate this deficiency inherent in extant methodologies, we introduce an innovative objectness regularization framework that compels tracking points to remain confined within the delineated boundaries of the solar entity. By encapsulating solar objectness indicators during the training phase, our approach obviates the necessity for explicit solar mask computation during operational deployment. Furthermore, we leverage the high-DoF robot arm to integrate our method to improve its robustness and flexibility in different outdoor environments.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.14568
- https://arxiv.org/pdf/2411.14568
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404985477
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4404985477Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.14568Digital Object Identifier
- Title
-
Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-21Full publication date if available
- Authors
-
Allen Jiang, Kangtong Mo, Satoshi Fujimoto, Michael D. Taylor, Sanjay Kumar, Chiotis Dimitrios, Elena FigueroList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.14568Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2411.14568Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.14568Direct OA link when available
- Concepts
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Leverage (statistics), Reinforcement learning, Artificial intelligence, Robotics, Deep learning, Computer science, Reinforcement, Engineering, Robot, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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