Motion of robotic devices is achieved by enabling the movement of its joints by devices called actuators. This National Robotics Initiative (NRI) project seeks to understand how to design compliant actuators for human-interactive robots that are energy-efficient and safe across a wide variety of tasks and situations. Unlike rigid actuators, compliant actuators can store and release mechanical energy for greater efficiency and absorb shocks for greater safety, which has made them especially popular in wearable robots (i.e., prostheses and exoskeletons). However, the compliant element of the actuator (e.g., a spring between the motor and the robot joint) must be carefully chosen to achieve these benefits, which has restricted previous implementations to specific use cases. The mathematical framework in this project will enable design of compliant actuators that change their physical properties to guarantee safety and efficiency as interactions vary from gentle to forceful. Compliant actuators that are robust to a wide range of conditions can be used for many applications, allowing mass production at lower cost. The energy efficiency of these actuators will increase the battery range of mobile co-robots and allow the use of smaller, lighter batteries in wearable robots. This work is significant to the ubiquity of compliant actuator technology for safe, energy-efficient interactions between robots and humans in uncertain real-world situations outside the laboratory.
This project will establish a robust convex optimization framework for designing series elastic actuators (SEAs) that globally minimize electrical energy consumption while satisfying actuator/safety constraints. When designing an SEA, a parametric representation of the elastic element (e.g., the stiffness of a linear spring) is typically optimized for a single task (trajectory and load). However, this paradigm has two key limitations: 1) solutions are only optimal within the given space of parameters, and 2) the benefits of the elasticity (i.e., efficiency and compliance) can be entirely lost outside of specific operating conditions. A nonlinear series elastic element can potentially solve these problems by providing different stiffness characteristics at different operating points, but the ideal parameterization of the spring is unknown. A non-parametric, robust optimization framework is therefore needed to develop SEA technology that can achieve a variety of tasks in a variety of situations (customizability) for ubiquitous interaction with humans (scalability). Tools exist to solve convex optimization problems with uncertainty in their parameters, but the design of SEAs is not currently known to be a convex problem. The overall goals of this project are then to 1) understand the convexity of SEA energy consumption as a function of stiffness characteristics, 2) understand how to design nonlinear series elastic elements to achieve maximal efficiency while satisfying constraints, and 3) understand how to design SEAs that are robust to uncertainties.
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.
