Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adélie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response.

由于动物在整个环境中的动态运动以及天气的潜在极端变化，速度在野生动植物调查中至关重要。在这项工作中，我们提出了一种多机器人路径规划方法，用于在大面积上进行空中勘测，旨在最大程度地利用有限的飞行时间。与当前基于几何图案或整数程序的勘测路径规划解决方案不同，我们解决了一系列复杂性不断提高的可取模理论实例。每个实例在每次迭代时都会产生一组可行的路径，并在足够的时间后恢复最短路径的集合。我们与无人驾驶飞机团队一起实施了规划算法，对克罗齐尔角（Cape Crozier）进行了多次照相空中野生动物调查，这是世界上最大的阿德利企鹅栖息地之一，拥有30万对嵌套对。在大约3个小时的时间内对2平方公里进行了调查。相比之下，以前人类对同一殖民地进行的单人无人机调查需要2天才能完成。我们的方法通过限制多余的行程来减少调查时间，同时还允许在调查期间的任何时间安全召回无人机。我们的方法可以应用于其他领域，例如高风险天气条件下的野火调查或灾难响应。