CAREER: Robust and Collaborative Perception and Navigation for Construction Robots

这项研究的结果将广泛适用于建筑以外的场景，从互联和自动驾驶汽车到智能和无障碍城市的服务机器人。此外，它的目标是通过教育游戏、多学科机器人课程和劳动力培训工作坊，扩大未被充分代表的群体对工程的参与。建筑工地中的移动机器人由于遮挡和有限的视野而经常受到感知挑战的限制。为了提高移动机器人视觉系统的健壮性、可靠性和可扩展性，将使用新型的自监督和基于图的表示学习来从高维感官输入中提取、组织和推理位置和对象。这项研究将沿着三个方向发展：(1)基于拓扑表示的稳健导航，用于动态和不断变化的工作场所的监控；(2)协作感知，用于在繁忙的作业场所提供更安全的操作监控和碰撞警告；(3)在算法、系统和数据集级别上集成感知和导航。这项研究将通过行业合作伙伴在真实的建筑工地进行验证，由此产生的软件、硬件设计和数据集将是开源的，以刺激未来的研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The construction industry is responsible for maintaining aging civil infrastructure and build new facilities that can accommodate the social needs of the 21st century. This is in addition to the ongoing critical need to address long-standing problems in occupational safety, labor productivity, costs, and labor shortage. A promising technical solution is to introduce mobile robots on construction jobsites. It is possible to leverage recent discoveries in robotics and artificial intelligence (AI) to tackle those aforementioned challenges. However, unlike manufacturing automation or self-driving cars, construction robots face unique challenges due to the need to navigate dynamic environments. Such robots are also required to work closely with humans in a variaty of tasks and often handle heavy payloads. This award supports fundamental robotics research to allow better perception and navigation for construction jobsite monitoring robots. It will produce an intelligent mobile robot team equipped with cameras to autonomously monitor construction progress and operations to improve jobsite efficiency and safety. The results of this research will be widely applicable to scenarios beyond construction, ranging from connected and autonomous vehicles to service robotics in smart and accessible cities. The project will facilitate collaboration between robotics, artificial intelligence, and civil and mechanical engineering. Furthermore, it aims to broaden participation of underrepresented groups in engineering via educational games, multi-disciplinary robotics curriculum, and workforce training workshops.Mobile robotics in construction jobsites are often limited by perception challenges due to occlusion and limited field of view. In dynamic jobsites, limited perception leads to navigation and inefficient assistance. To improve the robustness, reliability, and scalability of the vision system in mobile robots, novel self-supervised and graph-based representation learning will be used to extract, organize, and reason about places and objects from high-dimensional sensory inputs. This research will advance the state of the art along three directions: (1) robust navigation from topological representations for monitoring in dynamic and ever-changing jobsites, (2) collaborative perception for providing safer operation monitoring and collision warnings on busy jobsites, and (3) integrated perception and navigation at both the algorithm, system, and dataset levels. The research will be validated in real construction jobsites through industry partners, and the resulting software, hardware design, and dataset will be open source to stimulate future research.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.