Publications.

This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN), our method aims to maximize lead time for fall prediction while minimizing false positive rates. The proposed algorithm uniquely integrates the detection of various fault types and estimates the lead time for potential falls. Our contributions include the development of an algorithm capable of detecting abrupt, incipient, and intermittent faults in full-sized robots, its implementation using both simulation and hardware data for a humanoid robot, and a method for estimating lead time. Evaluation metrics, including false positive rate, lead time, and response time, demonstrate the efficacy of our approach. Particularly, our model achieves impressive lead times and response times across different fault scenarios with a false positive rate of 0. The findings of this study hold significant implications for enhancing the safety and reliability of bipedal robotic systems.

For legged robots to operate in complex terrains, they must be robust to the disturbances and uncertainties they encounter. This paper contributes to enhancing robustness through the design of fall detection/prediction algorithms that will provide sufficient lead time for corrective motions to be taken. Falls can be caused by abrupt (fast-acting), incipient (slow-acting), or intermittent (non-continuous) faults. Early fall detection is a challenging task due to the masking effects of controllers (through their disturbance attenuation actions), the inverse relationship between lead time and false positive rates, and the temporal behavior of the faults/underlying factors. In this paper, we propose a fall detection algorithm that is capable of detecting both incipient and abrupt faults while maximizing lead time and meeting desired thresholds on the false positive and negative rates.

Lower-limb exoskeletons provide people who suffer from lower limb impairments with an opportunity to stand up and ambulate. Standing up is a crucial task for lower-limb exoskeletons as it allows the user to transfer to the exoskeleton from a wheelchair, with no assistance, and can be a precursor to walking. Achieving a safe sit-to-stand motion for the exoskeleton + user system can be challenging because of the need to balance user comfort while respecting hardware bounds and being robust to changes in the user characteristics and the user's environment. We successfully achieve safe sit-to-stand motions by using constrained optimization to generate two types of dynamic sit-to-stand motions based on two hybrid system descriptions for the exoskeleton, Atalante. Due to the highly constrained nature of the equations of motions, we introduce a method to systematically design virtual constraints for highly constrained systems. We also design two quadratic program-based computed-torque controllers to achieve the sit-to-stand motion and to safely come to a stop in a standing position. We then analyze the closed-loop behaviors of the two sit-to-stand motions under the two controllers using physically motivated robustness tests. The criteria used to determine a successful sit-to-stand motion are: tracking error, the pitch acceleration of the torso, the amount of user force needed to perform the motion, and the adherence to the Zero Moment Point (ZMP), friction, and joint constraints.