Theory of Human Augmentation using Variable Stiffness Technology
The goal of this research is to enable human performance augmentation without external energy. We develop a special type of spring that can change stiffness – a variable stiffness spring – to enable human limbs to supply more energy and exert greater force as required to run faster or jump higher.
A. Sutrisno and D. J. Braun, How to Run 50% Faster without External Energy, Science Advances, 2020.
Novel Compliant Actuators for Robots & Human Assistance
Compliant actuators are typically designed to possess a tunable positive stiffness characteristic in order to generate restoring force upon displacement. These actuators either require two independent motor units or closed-loop control to change both their equilibrium position and output stiffness. The introduction of negative stiffness, in combination with tunable positive stiffness, may reduce the complexity and extend the capability of these actuators in unexpected ways. In this paper, we present a compliant actuator that employs a passive negative stiffness mechanism in conjunction with an effectively tunable positive stiffness mechanism. We show that such actuator enables open-loop stiffness modulation and equilibrium position control using a single motor unit, as opposed to more conventional variable stiffness and series elastic actuators. The paper presents the theoretical foundation of positive-negative stiffness actuators and demonstrates low-power stiffness modulation and equilibrium position control achievable with a prototype positive-negative stiffness actuator.
D.J. Braun, V. Chalvet and A. Dahiya, Positive-Negative Stiffness Actuators, IEEE Transactions on Robotics, 2018.
Data-driven Optimal Control of Robots
Optimal control provides a systematic approach to control robots. However, optimal controllers computed using inexact model information lead to significant performance degradation. One way to avoid this limitation is to completely discard model information. However, model-free optimal control approaches are computationally expensive (models can significantly speed up the optimal control computation).
Y. Chen and D.J. Braun, Hardware-in-the-Loop Iterative Optimal Feedback Control without Model-based Future Prediction, IEEE Transactions on Robotics, 2019.
Reverse-Engineering Nature for Locomotion Control in Robots and Humans
The goal of this research is to discover the physical reasons and control principles that govern movements of humans and animals and to utilize these findings to build next generation robotic devices. We have focused on the control aspect of this problem by (1) developing an impedance controller that is capable of generating autonomous self-organized locomotion, (2) extracting controllers form experimental data consistent with human locomotion, (3) developing physics base simulation tools to predict the motion of robot locomotion, (4) developing optimization based constrained impedance controllers that simultaneously provide cyclic limb coordination and upper body stabilization during the inherently unstable task of bipedal locomotion.
D.J. Braun, Jason E. Mitchell and M. Goldfarb, Actuated Dynamic Walking in a Seven-Link Biped Robot, IEEE/ASME Transactions on Mechatronics, vol. 17, no.1, pp. 147-156, 2012.
Numerical Modeling, Simulation and Control of Robots
We conduct physics-based simulation and optimization-based control of walking robots treated as constrained dynamical systems that interact with their environment. Simulation of constrained dynamical systems, modeled with differential algebraic equations (DAEs), are required in many engineering disciplines. However unlike ordinary differential equations (ODEs), that can be effectively integrated with explicit numerical methods (e.g., Runge-Kutta), integration of DAEs often requires sophisticated implicit integrators enhanced with iterative constraint stabilization.
Y. Li, H. Yu, and D.J. Braun, Algorithmic Resolution of Multiple Impacts in Non-smooth Mechanical Systems with Switching Constraints, IEEE International Conference on Robotics and Automation, 2019.