I-Corps: Smart Robotics Assistants

这个I-Corps项目的更广泛的影响/商业潜力包括提高美国制造商的竞争力，使他们能够在高混合和低产量的应用中使用机器人。在这些制造应用中使用机器人有望消除对人类执行符合人体工程学的挑战性任务的需要，并减少工人受伤的可能性。机器人使用的增加也有望减少对触摸劳动的需求，提高人类的生产力。这可用于在技术熟练的操作人员可用性有限的应用程序中提高生产率并减少订单履行时间。机器人有望提高制造操作的一致性和质量。这减少了返工和报废的需要。在自动化小批量制造挑战性任务中使用机器人将使人类操作员能够专注于高级决策和任务的创造性方面，而机器人助手将执行低级乏味的任务。这个I-Corps项目将利用作为美国国家科学基金会资助的南加州大学研究的一部分而开发的软件技术。该项目团队开发了规划算法和软件，使机器人能够根据任务描述对自己进行编程。这包括刀具的路径规划技术、机械手的点对点轨迹规划技术、机械手的路径约束轨迹规划技术、机械手的安装规划技术、复杂作业的综合任务和运动规划技术、基于视觉和接触的工件配准、自主学习技术和安全轨迹执行。实验室测试表明，开发的技术可以将机器人自动化的编程和工作单元设计时间减少十倍。该项目将着重于确定所开发技术的应用，并确定所开发软件的产品市场。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact/commercial potential of this I-Corps project involves improving the competitiveness of US manufacturers by enabling them to utilize robots on the high mix and low production volume applications. Use of robots on these manufacturing applications is expected to eliminate the need for humans to perform ergonomically challenging tasks and reduce the possibility of worker injury. The increased use of robots also is expected to reduce the need for touch labor and increase human productivity. This can be used to increase the production rate and reduce the order fulfillment time in applications where the availability of skilled human operators is limited. Robots are expected to improve the consistency and quality of the manufacturing operations. This reduces the need for rework and scrap. The use of robots in automating low-volume manufacturing challenging tasks will enable the human operators to focus on high-level decision making and the creative aspects of the tasks, while the robotic assistants will perform the low-level tedious tasks. This I-Corps project will leverage the software technology developed as part of NSF-funded research at the University of Southern California. The project team has developed planning algorithms and software that enable robots to program themselves from the task description. This includes path planning technology for tools, point-to-point trajectory planning technology for manipulators, path-constrained trajectory planning technology for manipulators, setup planning technologies for manipulators, integrated task and motion planning technologies for complex operations, vision and contact-based workpiece registration, self-directed learning technologies, and safe trajectory execution. Testing in the laboratory has demonstrated that the developed technology may reduce programming and work cell design time for robotic automation by a factor of ten. This project will focus on identifying the applications of the developed technologies, and determining the product-market fit for the developed software.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.