Event Location: NSH 1507

Abstract: We present work to transfer decentralized neuromuscular control strategies of human locomotion to powered segmented robotic legs. State-of-the-art robotic locomotion control approaches, like centralized planning and tracking in fully robotic systems and predefined motion pattern replay in prosthetic systems, do not enable the dynamism and reactiveness of able-bodied humans. Animals largely realize dexterous segmented leg performance with leg-encoded biomechanics and local feedback controls that bypass central processing. A decentralized neuromuscular controller was recently developed that enables robust locomotion for a simulated multi-segmented planar humanoid. A portion of this controller was used in an active ankle-foot prosthesis to modulate ankle torque during stance, enabling level and inclined ground walking. While these results suggest that the neuromuscular controller is a promising alternative control method for both fully robotic systems and powered prostheses, it is unclear if the controller can be transferred to multi-segmented robotic legs. The goal of this thesis is to investigate the feasibility of controlling a multi-segmented robotic leg with the proposed neuromuscular control approach, which may enable robots and powered prostheses to react to locomotion disturbances dynamically and in a human-like way.

To transfer neuromuscular controls to powered segmented robotic legs, we use a model-based design approach. The initial transfer is focused on neuromuscular swing-leg controls, important for maintaining dynamic stability of both fully robotic systems and powered prostheses in the presence of unexpected locomotion disturbances, such as trips and pushes. We first present the design of RNL, a three segment, cable-driven, antagonistically actuated robotic leg with joint compliance. The robot’s size, weight, and actuation capabilities correspond to dynamically scaled human values. Next, a high-fidelity simulation of the robot is created to investigate the feasibility of transferring neuromuscular controls, pre-tune hardware gains via optimization, and serve as a benchmark for hardware experiments. An idealized version of the swing-leg controller with mono-articular actuation is then transferred to RNL and evaluated with foot placement experiments. The results suggest that the swing-leg controller can accurately regulate foot placement of robotic legs during undisturbed and disturbed motions, although discrepancies exist between human motions and those executed by the robot. In parallel to this control transfer, a synthesis method for compact nonlinear springs with user-defined torque deflection profiles that use rubber as their compliant element is presented to explore methods for improving RNL’s series elastic actuators.

The proposed work focuses on realizing and evaluating full neuromuscular swing-leg control on RNL. First, performance of the idealized swing-leg controller will be compared to state-of-the-art locomotion control approaches. Next, the robot will be modified to include bi-articular actuators. Their inclusion will enable the robot to meet its actuation targets while maintaining a human-like mass distribution and enable us to transfer the full neuromuscular swing-leg controller to RNL. The model-based design approach will be extended to this setup, culminating in hardware foot placement experiments to validate the controller’s performance. We hypothesize that the neuromscular swing-leg controller will improve the robot’s performance over the idealized swing-leg controller by generating more human-like leg behavior. Additionally, we will extend the nonlinear spring design work and investigate if shortcomings that result from using rubber as a compliant element can be overcome with state estimation methods to account for rubber hysteresis.

Committee:Hartmut Geyer, Chair

Steve Collins

Ralph Hollis

Herman van der Kooij, University of Twente, Netherlands