Planning load-effective dynamic motions of highly articulated human model for generic tasks

Energy expenditure of a biped walking robot: instantaneous and degree-of-freedom-based instrumentation with human gait implications

Energy expenditure (EE) is an important criterion for design and control of biped walking robots. However, the cause-effect analyses enabled by total EE, which is lumped over a time duration and all system degrees-of-freedom (DOFs), are limited. In this study, robotic gait energetics is evaluated through a DOF-based instrumentation system designed for instantaneous evaluation of bidirectional current and applied voltage at each joint actuator. The instrumentation system includes a dual-module arrangement of buffers and attenuators, and accommodates and synchronizes the voltage and current measurements from multiple actuators. For illustrative purposes, this system is implemented at each DC servomotor in a biped robot, DARwIn-OP, to analyze the electrical EE rates for walking at various speeds. In addition, a DOF-based model of instantaneous human EE rate is employed to enable quantitative characterization of robotic walking EE relative to that of humans. The robot's instantaneous lower-body EE rates are consistent with its periodic walking cycle, and their relative trends between single and double support phases are analogous to those of humans. The robotic cost of transport (COT) curve as a function of normalized speed is also consistent with the human COT in terms of its convexity. Conversely, the contrasting distributions of EE throughout the robot and human DOFs and the robotic COT curve's considerably larger magnitudes, smaller speed ranges, and higher sensitivity to speed illustrate the energetic consequences of stable but inefficient static walking in the biped robot relative to the more efficient dynamic walking of humans. These energetic characteristics enable the identification of the joints and gait cycle phases associated with inefficiency in biped robotic gait, and reflect the noticeable differences in the system parameters (rigid and flat versus segmented feet) and gait control strategies (bent versus straight knees, instants of peak ankle actuator torques, static versus dynamic balance stability). The proposed general instrumentation provides a quantitative approach to benchmarking human gait as well as general guidelines for the development of energy-efficient walking robots.

A joint-space numerical model of metabolic energy expenditure for human multibody dynamic system

Metabolic energy expenditure (MEE) is a critical performance measure of human motion. In this study, a general joint-space numerical model of MEE is derived by integrating the laws of thermodynamics and principles of multibody system dynamics, which can evaluate MEE without the limitations inherent in experimental measurements (phase delays, steady state and task restrictions, and limited range of motion) or muscle-space models (complexities and indeterminacies from excessive DOFs, contacts and wrapping interactions, and reliance on in vitro parameters). Muscle energetic components are mapped to the joint space, in which the MEE model is formulated. A constrained multi-objective optimization algorithm is established to estimate the model parameters from experimental walking data also used for initial validation. The joint-space parameters estimated directly from active subjects provide reliable MEE estimates with a mean absolute error of 3.6 ± 3.6% relative to validation values, which can be used to evaluate MEE for complex non-periodic tasks that may not be experimentally verifiable. This model also enables real-time calculations of instantaneous MEE rate as a function of time for transient evaluations. Although experimental measurements may not be completely replaced by model evaluations, predicted quantities can be used as strong complements to increase reliability of the results and yield unique insights for various applications.

Passive and dynamic gait measures for biped mechanism: formulation and simulation analysis

Understanding and mimicking human gait is essential for design and control of biped walking robots. The unique characteristics of normal human gait are described as passive dynamic walking, whereas general human gait is neither completely passive nor always dynamic. To study various walking motions, it is important to quantify the different levels of passivity and dynamicity, which have not been addressed in the current literature. In this paper, we introduce the initial formulations of Passive Gait Measure (PGM) and Dynamic Gait Measure (DGM) that quantify passivity and dynamicity, respectively, of a given biped walking motion, and the proposed formulations will be demonstrated for proof-of-concepts using gait simulation and analysis. The PGM is associated with the optimality of natural human walking, where the passivity weight functions are proposed and incorporated in the minimization of physiologically inspired weighted actuator torques. The PGM then measures the relative contribution of the stance ankle actuation. The DGM is associated with the gait stability, and quantifies the effects of inertia in terms of the Zero-Moment Point and the ground projection of center of mass. In addition, the DGM takes into account the stance foot dimension and the relative threshold between static and dynamic walking. As examples, both human-like and robotic walking motions during single support phase are generated for a planar biped system using the passivity weights and proper gait parameters. The calculated PGM values show more passive nature of human-like walking as compared with the robotic walking. The DGM results verify the dynamic nature of normal human walking with anthropomorphic foot dimension. In general, the DGMs for human-like walking are greater than those for robotic walking. The resulting DGMs also demonstrate their dependence on the stance foot dimension as well as the walking motion; for a given walking motion, smaller foot dimension results in increased dynamicity. Future work on experimental validation and demonstration will involve actual walking robots and human subjects. The proposed results will benefit the human gait studies and the development of walking robots.