CAREER: Dynamic Modeling and Fabrication of Compliant Material Systems for On-Demand Specialist Robots

该学院早期职业发展（CAREER）补助金将使开发具有成本效益的“专家”机器人成为可能，这些机器人可以在几个小时内由非专家制作原型。该项目的目标是使机器人更加无处不在，可访问，可调的新人机器人和应用在工业，教育和学术研究。实现这一目标需要转向更经济实惠的材料，可访问的制造策略和辅助设计软件，这些软件考虑了具有成本效益的电机和材料的特定动态以及特定应用的需求。该项目的结果将影响需要专业化的领域，例如老年人辅助机器人，定制农业应用和智能城市中的垃圾收集。通过这个项目开发的机器人也将作为一个负担得起的起点，让孩子们参加由当地以青年为重点的非营利组织组织举办的课后机器人比赛。此外，为该项目开发的模型和模板将被集成到大学水平的机器人课程，使学生能够在学习早期更深入地探索先进的机器人主题。该项目的基础研究贡献在于考虑并将顺应性材料系统集成到一个统一的设计框架中，以支持动态机器人系统的专业化和优化。该研究计划涉及为材料系统的非线性力学开发简化模型，以允许更实惠的机器人设计，并考虑在模拟中的合规性，以定制，建模和优化其性能。这种方法将研究机器学习技术，以自动调整参数，模板驱动的设计，以平衡速度，有效载荷和效率等竞争性性能权衡。该项目的研究目标包括合规性的表示，合规性的利用和理解，合规系统的优化，以及通过原型设计，实验验证和示例用例来验证框架。该方法的通用性将通过对双足机器人的几个子系统进行建模和优化来证明。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) grant will make it possible to develop cost-effective, “specialist” robots that can be prototyped by a non-expert in a matter of hours. The goal of this project is to make robots more ubiquitous, accessible, and tunable for newcomers to robotics and for applications in industry, education, and academic research. Achieving this goal requires a shift towards more affordable materials, accessible fabrication strategies, and assistive design software that considers the particular dynamics of cost-efficient motors and materials, as well as the needs of specific applications. The results of this project will impact fields in which specialization is desirable, such as assistive robotics for the elderly, custom agricultural applications, and trash pickup in smart cities. The robots developed through this project will also serve as an affordable starting point for children to compete in after-school robotics competitions organized by local, youth-focused nonprofits. In addition, the models and templates developed for this project will be integrated into college-level robotics curricula, permitting students to venture deeper into advanced robotics topics earlier in their studies.The fundamental research contribution of this project lies within the consideration and integration of compliant material systems into a unified design framework that supports the specialization and optimization of dynamical robotic systems. The research plan involves developing reduced models for the nonlinear mechanics of material systems that permit more affordable robot designs, and considering that compliance in simulations to customize, model, and optimize their performance. This approach will investigate machine learning techniques to automatically tune parametric, template-driven designs in a way that balances competing performance trade-offs like speed, payload, and efficiency. The project’s research objectives include the representation of compliance, the utilization and understanding of compliance, the optimization of compliant systems, and validation of the framework through prototyping, experimental validation, and exemplar use-cases. The generality of the approach will be demonstrated by modeling and optimizing several subsystems of a bipedal robot.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.