NRI: FND: Human-Guided Robot Teams for Manipulating Large Flexible Sheets in Manufacturing Applications

大型柔性板在许多制造操作中被处理，包括复合板铺叠、金属板制造和纺织品制造。这种片材的手动操作是重复的、有压力的并且在人体工程学上具有挑战性，并且手动操作还可能导致不一致的产品质量。通过部署人工引导的机器人团队，可以显著提高物料搬运作业的吞吐量、质量和安全性。这使得人类操作员能够指导团队操作，而机器人则执行繁琐的物理任务。该奖项支持决策的根本性进步，使机器人团队能够与人类操作员协作，在小批量生产应用中操纵大型柔性板材。基本进步的效用将通过将协作机器人应用于复合板铺层来证明。这项研究的结果将使机器人能够在新的制造应用中引入，并有望使美国制造业在全球市场上更具成本竞争力。研究与研究生和本科生课程的整合将增强机器人和制造课程，丰富参与学生的学习经验。外联活动将教育和告知K-12学生有关机器人和制造业的就业机会。 在包括人类和机器人的混合工作单元中执行复杂任务需要代理之间的协作。这些单元的操作需要协调的规划、监视和控制，以确保代理可以共享工作空间，并通过适当地适应其他团队成员执行的任务来有效地协作。该奖项将使计算的进步，以促进代理之间的合作，在共享的网络操纵灵活的表。混合单元将使用规划器来确定任务并将这些任务分配给单元中的代理。混合单元将在人类指导下使用审慎规划来设计单元布局，生成操作序列，选择抓取位置并为机器人生成标称轨迹。将通过主动考虑控制器和人类在执行过程中的干预来开发协同操纵的深思熟虑的规划算法，以处理突发事件。将开发实时算法，用于在执行过程中监控任务进度，并启动干预措施，以确保零件以安全有效的方式制造。该奖项反映了NSF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。

Large flexible sheets are processed in many manufacturing operations, including composite sheet layup, sheet metal fabrication, and textile manufacturing. The manual manipulation of such sheets is repetitive, stressful and ergonomically challenging and manual operations can also lead to inconsistent product quality. Throughput, quality, and safety of material handling operations can be significantly improved by deploying human-guided robot teams. This enables the human operator to guide the team operation, with robots performing tedious physical tasks. This award supports fundamental advances in decision making to enable a team of robots to work collaboratively with a human operator to manipulate large flexible sheets in small production volume manufacturing applications. The utility of fundamental advances will be demonstrated by applying collaborative robotics to composite sheet layup. Results from this research will enable the introduction of robots in new manufacturing applications and are expected to make the US manufacturing industry more cost competitive in the global market. The integration of the research with graduate and undergraduate courses will enhance the robotics and manufacturing curricula and enrich learning experiences of the participating students. Outreach activities will educate and inform K-12 students about career opportunities in robotics and manufacturing. Performing complex tasks in hybrid work cells that include both humans and robots requires collaboration among agents. Operation of such cells requires coordinated planning, monitoring, and control to ensure that agents can share the workspace and collaborate efficiently by appropriately adapting to the tasks performed by other team members. This award will make computational advances to facilitate collaboration among agents in shared workspaces for manipulating a flexible sheet. The hybrid cell will use a planner to determine the tasks and assign those tasks to agents in the cell. The hybrid cell will use deliberative planning under human guidance to design cell layout, generate a sequence of operations, select grasp locations and generate nominal trajectories for robots. Deliberative planning algorithms will be developed for collaborative manipulation by proactively accounting for interventions by controllers and humans during execution to handle contingencies. Real-time algorithms will be developed for monitoring of the task progress during the execution and initiating interventions to ensure that the part is manufactured in a safe and efficient manner.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.