Lower-limb powered exoskeletons have the potential to help patients who suffer from mobility impairments to regain their independence. Unlike traditional exoskeletons with predefined gait patterns, some recently developed exoskeletons are capable of planning gait patterns autonomously based on environmental information and human perception. However, patients may have difficulty navigating through unfamiliar environments due to the slow or miscommunication between the exoskeletons and users. Verbal or manual adjustments cannot continuously or adaptively manage the mechanical motions either. In this study, we propose and demonstrate an autonomous walking pattern generator based on shared visual information between human and a powered lower-limb exoskeleton. This control scheme is to understand the user's intention during walking and help the patient who has trouble walking to overcome obstacles. Human gazing positions in a 3-D environment are measured and evaluated via an eye tracker system to detect objects in the walking path. A model predictive controller (MPC) is formulated, which generates and modifies the footsteps and the center of mass trajectory of the powered exoskeleton in real time considering the visual feedback. Preliminary experiments are performed by having a human subject walking toward a target sign indicating a physical obstacle. Our results show the feasibility of integrating the walking pattern generator with visual feedbacks to achieve autonomous control for a lower-limb powered exoskeleton.

Control systems for powered prosthetic legs typically divide the gait cycle into several periods with distinct controllers, resulting in dozens of control parameters that must be tuned across users and activities. To address this challenge, this paper presents a control approach that unifies the gait cycle of a powered knee-ankle prosthesis using a continuous, user-synchronized sense of phase. Virtual constraints characterize the desired periodic joint trajectories as functions of a phase variable across the entire stride. The phase variable is computed from residual thigh motion, giving the amputee control over the timing of the prosthetic joint patterns. This continuous sense of phase enabled three transfemoral amputee subjects to walk at speeds from 0.67 to 1.21 m/s and slopes from -2.5 to +9.0 deg. Virtual constraints based on task-specific kinematics facilitated normative adjustments in joint work across walking speeds. A fixed set of control gains generalized across these activities and users, which minimized the configuration time of the prosthesis.

This paper presents the experimental validation of a novel control strategy that unifies the entire gait cycle of a powered knee-ankle prosthetic leg without the need to switch between controllers for different periods of gait. Current control methods divide the gait cycle into several sequential periods each with independent controllers, resulting in many patient-specific control parameters and switching rules that must be tuned for a specific walking speed. The single controller presented is speed-invariant with a minimal number of control parameters to be tuned. A single, periodic virtual constraint is derived that exactly characterizes the desired actuated joint motion as a function of a mechanical phase variable across walking cycles. A single sensor was used to compute a phase variable related to the residual thigh angle's phase plane, which was recently shown to robustly represent the phase of non-steady human gait. This phase variable allows the prosthesis to synchronize naturally with the human user for intuitive, biomimetic behavior. A custom powered knee-ankle prosthesis was designed and built to implement the control strategy and validate its performance. A human subject experiment was conducted across multiple walking speeds (1 to 3 miles/hour) in a continuous sequence with the single phase-based controller, demonstrating its adaptability to the user's intended speed.