SBIR Phase I: Path Planning for Multi-target Search and Localization in Co-Robotic Architectures

SBIR 第一阶段：协作机器人架构中多目标搜索和定位的路径规划

• 批准号: 2325364
• 负责人: Paul Flikkema
• 金额: $ 27.5万
• 依托单位: BIOTRONIC INNOVATIONS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325364&HistoricalAwards=false
• 关键词: SBIR; Phase; Path; Planning; Multi

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is a high-performance, drone-based, wildlife telemetry technology that is economical and easy to use. Its improved performance and significantly lower cost than existing competitive technologies will empower users with greater capability and lower the barrier to entry for field data collection. This technology will enable researchers to better understand the complex effects of geography, climate, interspecies competition, invasive species, and land use policy on animals and their habitats. This project will enable a co-robotic system that employs an unmanned aerial vehicle (UAV) as an assistant wildlife tracker. By fusing data from the UAV and the knowledge and insights of the human tracker, the team will greatly increase the capability of these systems to track a larger number of animals, to increase the frequency of tracking campaigns, and to improve the ease of use. This research will develop and refine a new class of machine learning algorithms for wildlife localization. As the UAV platform executes its mission, the human tracker can monitor its progress and plans, and intervene at any time with commands to re-direct the UAV. The research plan tackles the key challenges in this problem domain, including safety of UAV operation and the possible effects of noise emissions. Based on the identification of key risks and mitigation strategies, the research plan executes multiple rapid iterations of a develop-simulate-deploy-test design process within a set of tasks that tackle increasingly complex localization scenarios.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.

这个小企业创新研究 (SBIR) 第一阶段项目的更广泛/商业影响是一种经济且易于使用的高性能、基于无人机的野生动物遥测技术。与现有竞争技术相比，其改进的性能和显着降低的成本将为用户提供更大的能力并降低现场数据收集的准入门槛。这项技术将使研究人员能够更好地了解地理、气候、物种间竞争、入侵物种和土地利用政策对动物及其栖息地的复杂影响。 该项目将实现一个协作机器人系统，该系统采用无人机（UAV）作为辅助野生动物追踪器。通过融合无人机的数据和人类跟踪器的知识和见解，该团队将大大提高这些系统跟踪大量动物的能力，增加跟踪活动的频率，并提高易用性。这项研究将开发和完善一类用于野生动物定位的新型机器学习算法。当无人机平台执行任务时，人类跟踪器可以监控其进度和计划，并随时通过命令进行干预以重新引导无人机。该研究计划解决了该问题领域的关键挑战，包括无人机操作的安全性和噪声排放可能产生的影响。基于对关键风险和缓解策略的识别，该研究计划在一系列任务中执行开发-模拟-部署-测试设计流程的多次快速迭代，以应对日益复杂的本地化场景。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。