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：如今，最广泛适用且应用最广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，从地面定位附着在野生动物身上的低功率无线电发射器。尽管空中机器人能够改变劳动密集型保护任务，但在实际条件下实现自主系统以应对任务复杂性仍是一项挑战。我们开发了ConservationBots——一种用于跟踪多个动态无线电标记野生动物的小型空中机器人。该空中机器人实现了鲁棒的定位性能和快速的任务完成时间——这对能量受限的航空系统至关重要，同时避免了与野生动物可能产生的反作用干扰。我们的方法克服了将轻量级传感器与以下新概念相结合所带来的技术及实际问题：i) 基于信息论目标进行规划以确定轨迹和测量动作，使机器人能够策略性地选择近乎瞬时的仅测距测量以实现更快的定位，以及耗时的传感器旋转动作以获取方位测量并实现鲁棒跟踪性能；ii) 一种对噪声更鲁棒的方位检测器；iii) 一种对实际条件下出现的漏检和误检具有鲁棒性的跟踪算法。我们进行了广泛研究：基于高分辨率高程数据在多样化地理地形上构建复杂信号传播的仿真、现场测试、对袋熊（Lasiorhinus latifrons；夜行性、易危物种，栖息于地下洞穴）的研究，以及与经验丰富的生物学家的跟踪对比，以验证我们空中机器人的有效性，并证明其相较于手动方法的显著优势。