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

在许多制造操作中都会加工大型柔性板材，包括复合材料板材铺层、金属板材制造和纺织制造。人工操作这类薄片是重复的、有压力的，对人体工程学具有挑战性，而且人工操作也可能导致产品质量不一致。通过部署人工引导的机器人团队，可以显著提高材料搬运操作的吞吐量、质量和安全。这使得操作员能够指导团队操作，由机器人执行繁琐的体力任务。该奖项支持决策方面的基本进步，使机器人团队能够与人类操作员协作，在小批量制造应用中操纵大型柔性板材。将通过将协作机器人应用于复合材料板材铺层来展示基本进步的效用。这项研究的结果将使机器人能够在新的制造业应用中引入，并有望使美国制造业在全球市场上更具成本竞争力。将研究与研究生和本科课程相结合，将加强机器人和制造课程，并丰富参与学生的学习经验。外展活动将教育和告知K-12学生在机器人和制造领域的职业机会。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合作。此类单元的操作需要协调的规划、监控和控制，以确保代理可以通过适当地适应其他团队成员执行的任务来共享工作空间和高效协作。该奖项将在计算方面取得进展，以促进共享工作空间中的代理之间的协作，以操纵灵活的工作表。混合单元将使用计划器来确定任务，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深思熟虑的规划来设计单元布局，生成操作序列，选择抓取位置并生成机器人的标称轨迹。将开发用于协作操纵的慎重规划算法，通过主动考虑执行期间管理员和人类的干预来处理突发事件。将开发实时算法，用于在执行和启动干预过程中监控任务进度，以确保以安全和高效的方式制造部件。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势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.