用深度强化学习让高自由度机械臂精准追踪太阳，防漂移。
Maximum Solar Energy Tracking Leverage High-DoF Robotics System with Deep Reinforcement Learning
- 引入太阳能专属特征正则化，防止跟踪点偏离太阳
- 无需实时计算太阳掩膜，提升部署效率
- 结合高自由度机器人，适应复杂户外环境
太阳能轨迹监测是太阳能系统中的关键挑战，支撑自主能源采集与环境感知等应用。现有持续追踪方法常因预测算法错误偏离太阳轨迹，误锚定于其他天体或地面物体，根源在于追踪机制未能充分融合太阳能特有的对象性特征。为解决这一缺陷，我们提出一种创新的对象性正则化框架，强制追踪点始终位于太阳实体边界内。通过在训练阶段嵌入太阳能对象性指标，该方法避免了运行时显式计算太阳掩膜的需要。此外，我们利用高自由度机器人臂集成该方法，显著提升了系统在不同室外环境下的鲁棒性与灵活性。
原文摘要 · Abstract (English)
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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