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.

翻译：太阳轨迹监测是太阳能系统中的关键挑战，支撑着自主能量收集和环境感知等应用。持续太阳追踪中一种普遍的失效模式发生在预测算法错误偏离太阳轨迹，转而锚定到外部天体或地面特征时。这一现象归因于追踪范式对太阳特定物体属性吸收不足。为缓解现有方法固有的这一缺陷，我们引入了一种创新的物体性正则化框架，迫使追踪点保持在太阳实体的划定边界内。通过在训练阶段封装太阳物体性指标，我们的方法避免了在运行部署期间进行显式太阳掩模计算的需求。此外，我们利用高自由度机械臂集成我们的方法，以提升其在不同户外环境中的鲁棒性和灵活性。