Uncertainty-aware full-body motion planning of aerial and multi-legged robots for urban search and rescue operations

用于城市搜救行动的空中和多足机器人的不确定性全身运动规划

• 批准号: 560791-2020
• 负责人: RamirezSerrano, Alejandro
• 金额: $ 7.29万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720674
• 关键词: Uncertainty; aware; full; body; motion

With the increasing severity and frequency of natural disasters such as tornados, floods, and many others, the 21st Century presents unique challenges to disaster response. The capabilities of autonomous robots have the potential to be used as effective tools for Urban Search & Rescue (USAR) operations in complex confined environments - both structured and unstructured - such as those encountered inside collapsed buildings and in burning infrastructure, among many others. However, major challenges related to autonomy, locomotion, and adaptability remain before robots can be effectively deployed inside such spaces where safety-critical decisions must often be made despite uncertainty in the estimation, execution, and the environment. These problems can often be cast as a class of problems for which finding an exact solution is challenging. This project will develop whole-body tractable motion-planning solutions enabling robotic systems to operate inside chaotic hazardous confined spaces where real-time operation is essential. The results will enable to deploy drones and multi-legged robots under uncertainty estimation via sophisticated estimation schemes like visual-inertial odometry that depend on the distribution of the features in the environment. The work is important to reduce the effects of natural and other disasters that cause billions of dollars in economic losses every year. Using the wealth of experience in Canada's emergency agencies and disaster response, the project will develop effective robotic tools for USAR that can be in position and deployed directly following an incident of structural collapse. The results will render emergency preparedness and response activities more effective and thereby save more lives and reduce community recovery time. The outcomes will improve efficiency in disaster response operations at a disaster site and provide the means to increase capacity to prepare for, mobilize and coordinate USAR assistance in support of affected communities in collapsed structure emergencies. The results will support capacity-building at the national, global, and regional levels. In collaboration with first response organizations, 6 graduate and 10 BSc students will be trained in a multidisciplinary team environment.

随着龙卷风、洪水等自然灾害的日益严重和频繁，21世纪对灾害应对提出了独特的挑战。自主机器人的能力有可能被用作城市搜索和救援(USAR)操作的有效工具，在复杂的受限环境中--包括结构化和非结构化--例如在倒塌的建筑内和燃烧的基础设施等中遇到的那些环境。然而，在机器人能够有效地部署在这样的空间中之前，与自主性、运动性和适应性相关的主要挑战仍然存在，在这些空间中，尽管估计、执行和环境存在不确定性，但往往必须做出安全关键的决定。这些问题通常可以归结为一类问题，为它们找到准确的解决方案是具有挑战性的。该项目将开发全身易处理的运动规划解决方案，使机器人系统能够在混乱的危险受限空间内操作，在那里实时操作是必不可少的。这一结果将使无人机和多腿机器人能够通过复杂的估计方案在不确定性估计下部署，例如依赖于环境中特征分布的视觉惯性里程计。这项工作对于减少自然灾害和其他灾害的影响非常重要，这些灾害每年造成数十亿美元的经济损失。利用加拿大应急机构和灾害应对方面的丰富经验，该项目将为USAR开发有效的机器人工具，这些工具可以在结构倒塌事件发生后立即到位和部署。结果将使应急准备和反应活动更加有效，从而拯救更多的生命，减少社区恢复时间。这些成果将提高灾害现场救灾行动的效率，并提供手段来提高能力，以准备、动员和协调USAR援助，以支持倒塌结构紧急情况下的受影响社区。成果将支持国家、全球和区域各级的能力建设。与急救组织合作，6名研究生和10名理科学生将在多学科团队环境中接受培训。