NRI: INT: Adaptive Bio-inspired Co-Robot algorithms for volcano monitoring

据估计有500座火山向大气中排放火山气体。该项目将设计、建造并现场测试一个名为火山协同机器人自适应自然算法（VolCAN）群的飞行机器人群。火山群将改变我们预测火山爆发的能力。这群无人机由多架自主无人机组成，它们使用受生物学启发的算法来监测火山周围不可预测的环境。除了监测火山爆发前的气体，从而保护人类生命外，它还将测量火山排放的二氧化碳量，以更好地了解它们对全球碳预算的贡献。VolCAN蜂群可以实时自主适应环境条件，也可以在小型无人机电池有限的飞行过程中，在科学家的引导下收集科学数据。我们的方法利用了生物启发算法的优势，即快速而不是完全准确，有弹性而不是集中控制。该项目将通过让未被充分代表的群体的学生参与机器人研究和编程课程，扩大对计算机的参与。该项目将开发、分析和严格测试无人驾驶飞行器（uav）的协同机器人群，在动态和不可预测的环境中收集有价值的科学数据。VolCAN群将使用生物启发算法来检测CO2羽流，下降羽流梯度以测量来自地面源的最大CO2通量，估计羽流大小，并推断数百平方公里内多个CO2源的地图。考虑到电池对飞行时间的限制以及危险、不可预测的条件，算法优先考虑速度、鲁棒性和可解释性，而不是高精度。新型的生物启发算法可覆盖广阔的区域，适应感测环境，实时集中监测最重要的区域，并且在小型无人机有限的电池寿命内足够快地收集许多同时排放。理论分析将确定算法的速度和收敛时间的界限，模拟和频繁的现场测试将在严格的、重复的实验中衡量VolCAN系统的性能。此外，该项目还将证明，科学家可以通过完全自主的自适应监控或科学家的指导，操作VolCAN群来收集现场数据。这种方法将人的灵活性和判断力与无人机群的速度和机动性相结合。该项目将展示VolCAN群在环境监测应用实验中的广泛适用性，以测量管道的甲烷排放和评估植物群落的生态健康。它还将表明，受生物启发的机器人可以在高度结构化的工厂、实验室和仓库之外工作，在危险和不可预测的活火山环境中收集有价值的科学数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There are an estimated 500 volcanoes that emit volcanic gases to the atmosphere. This project will design, build, and field-test a collaborative swarm of flying robots called the Volcano Co-robot Adaptive Natural algorithms (VolCAN) swarm. The VolCAN swarm will transform our ability to forecast volcanic eruptions. The swarm consists of multiple autonomous aerial drones that use algorithms inspired by biology to monitor the unpredictable environments surrounding volcanoes. In addition to monitoring gasses that precede volcanic eruptions, thereby protecting human lives, it will also measure how much carbon dioxide is emitted from volcanoes to better understand how they contribute to the global carbon budget. The VolCAN swarm can adapt to environmental conditions autonomously in real time, and it can also be guided by scientists to collect scientific data during the battery-limited flights of small drones. Our approach leverages the advantages of bio-inspired algorithms that are fast rather than perfectly accurate, and resilient rather than centrally controlled. The project will broaden participation in computing by involving students from underrepresented groups in both robotics research and programming courses.This project will develop, analyze and rigorously test a co-robot swarm of unpiloted air vehicles (UAVs) that collect valuable scientific data in dynamic and unpredictable environments. The VolCAN swarm will use bio-inspired algorithms to detect CO2 plumes, descend plume gradients to measure maximum flux of CO2 from ground sources, estimate plume size, and infer maps of multiple CO2 sources over hundreds of square kilometers. Given battery limitations on flight times and dangerous, unpredictable conditions, the algorithms prioritize speed, robustness and interpretability over high accuracy. The novel bio-inspired algorithms scale to cover vast areas, adapt to the sensed environment to focus monitoring on the most important regions in real time, and are fast enough to collect many simultaneous emissions within the limited battery life of small UAV. Theoretical analyses will determine bounds on the speed and convergence times of the algorithms, and simulations and frequent field tests will measure the performance of the VolCAN system in rigorous, replicated experiments. Additionally, the project will demonstrate that scientists can operate the VolCAN swarm to collect data in the field with either fully autonomous adaptive surveillance or with scientist guidance. This approach combines human flexibility and judgment with the speed and mobility of a UAV swarm. The project will demonstrate the broad applicability of the VolCAN swarm in environmental monitoring applications in experiments to measure methane emissions from pipelines and assess ecological health of plant communities. It will also show that bio-inspired robots can function outside of highly-structured factories, labs, and warehouses to gather valuable scientific data in the hazardous and unpredictable environments of active volcanoes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.