Force-Sensor-free Contact Estimation for Lower Limb Exoskeleton Robots Based on Probabilistic Modeling and Fusion

Abstract

Lower limb exoskeletons (LLEs) play a crucial role in assisting paraplegic patients with walking in outdoor environments characterized by complex terrains, including various stairs, slopes, and uneven grounds. However, most existing control methods for LLEs rely on predefined joint angles, lacking the flexibility to adapt to diverse terrains. This deficiency often leads to unexpected contacts between the feet of the LLEs and the ground, thereby disrupting the walking balance of the LLEs. In this paper, a novel force-sensor-free contact estimation method is proposed to tackle this problem. This method utilizes only the sensors already present on the LLEs, eliminating the need for any additional force sensors. The proposed approach is founded on the probabilistic modeling of gait phases, knee joint torques, foot heights, and the displacement of the center of mass. Moreover, Kalman filtering is employed to enhance the contact estimation accuracy by integrating multiple probabilistic models. Experiments were carried out on both robot simulation platforms and real exoskeleton robots. The experimental results demonstrate that the proposed approach can accurately estimate contacts during walking on flat ground and stairs. Specifically, it achieves an accuracy of 99% with a time deviation of 8 ms on the flat ground and an accuracy of 95% with a time deviation of 10 ms on stairs.