Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor-intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real-world conditions remains a challenge. We developed ConservationBots-small aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times -- significant for energy-limited aerial systems while avoiding close encounters with potential, counter-productive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: i) planning to determine both trajectory and measurement actions guided by an information-theoretic objective, which allows the robot to strategically select near-instantaneous range-only measurements to achieve faster localization, and time-consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; ii) a bearing detector more robust to noise and iii) a tracking algorithm formulation robust to missed and false detections experienced in real-world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high-resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.

翻译：当今，最广泛且通用性最强的数据采集技术依赖于经验丰富的科学家手持无线电遥测设备，在地面定位附着于野生动物身上的低功耗无线电发射器。尽管空中机器人能够变革劳动密集型的保护任务，但在真实条件下实现应对任务复杂性的自主系统仍面临挑战。我们研发了ConservationBots——用于跟踪多个动态无线电标记野生动物的小型自主空中机器人。该机器人在实现鲁棒定位性能与快速任务完成时间（这对能量受限的空中系统至关重要）的同时，能够避免与野生动物产生潜在反作用的近距离干扰。我们的方法通过将轻量级传感器与新概念相结合，克服了技术与实践难题：i）基于信息论目标规划轨迹与测量决策，使机器人能策略性地选择近乎瞬时的距离-only测量以实现快速定位，以及耗时传感器旋转动作以获取方位测量并实现鲁棒跟踪性能；ii）更抗噪声的方位检测器；iii）能够应对真实条件下漏检与误检的鲁棒跟踪算法。我们开展了广泛研究：基于高分辨率高程数据在不同地理地形上构建复杂信号传播的仿真实验、实地测试、针对袋熊（Lasiorhinus latifrons；夜行性、穴居易危物种）的跟踪实验，以及与资深生物学家的跟踪对比，验证了该空中机器人的有效性并展示了相较人工方法的显著优势。