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, AlejandroA
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759745
• 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，洪水等自然灾害的严重程度和频率的增加，21世纪为灾难反应带来了独特的挑战。自主机器人的功能有可能用作在复杂的密闭环境中（包括结构化和非结构化的复杂环境中）的有效工具（USAR）操作，例如在崩溃的建筑物内以及燃烧基础设施中遇到的那些。但是，与自主权，运动和适应性相关的主要挑战仍然存在于机器人可以有效地部署在此类空间内，尽管估计，执行和环境不确定，但仍必须做出安全关键决策。这些问题通常可以作为一类问题，这些问题都具有挑战性。该项目将开发全身可进行的运动规划解决方案，使机器人系统能够在混乱的危险狭窄空间内运行，而实时操作是必不可少的。结果将使在不确定性估计下通过复杂的估计方案（如视觉惯性探测器）（依赖于环境中特征的分布）（例如视觉惯性探测器）来部署无人机和多腿机器人。这项工作对于减少自然和其他灾害的影响很重要，每年造成数十亿美元的经济损失。利用加拿大紧急机构的丰富经验和灾难响应，该项目将为USAR开发有效的机器人工具，该工具可以在结构崩溃事件发生后直接处于位置并部署。结果将使应急准备和应对活动更加有效，从而节省更多的生命并减少社区恢复时间。结果将提高灾害现场灾难响应操作的效率，并提供增加准备，动员和协调USAR协助以支持受影响社区崩溃的紧急情况的能力。结果将支持国家，全球和地区一级的能力建设。与第一回应组织合作，将在多学科团队的环境中对6名毕业生和10名BSC学生进行培训。