Musculoskeletal Modeling of the Human Lower Limb Stiffness for Robotic Applications

This research work presents a physiologically accurate and novel computationally fast neuromusculoskeletal model of the human lower limb stiffness. The proposed computational framework uses electromyographic signals, motion capture data and ground reaction forces to predict the force developed by 43 musculotendon actuators. The estimated forces are then used to compute the musculotendon stiffness and the corresponding joint stiffness. The estimations at each musculotendon unit is constrained to simultaneously satisfy the joint angles and the joint moments of force generated with respect to five degrees of freedom, including: Hip Adduction-Abduction, Hip Flexion-Extension, Hip Internal-External Rotation, Knee Flexion-Extension, and Ankle Plantar-Dorsi Flexion. Advanced methods are used to perform accurate muscle-driven dynamic simulations and to guarantee the dynamic consistency between kinematic and kinetic data. This study presents also the design, simulation and prototyping of a small musculoskeletal humanoid made for replicating the human musculoskeletal structure in an artificial apparatus capable to maintain a quiet standing position using only a completely passive elastic actuation structure. The proposed prototype has a total mass of about 2 kg and its height is 40 cm. It comprises of four segments for each leg and six degrees of freedom, including: Hip Adduction-Abduction, Hip Flexion-Extension, Knee Flexion-Extension, Ankle Plantar-Dorsi Flexion, Ankle Inversion-Eversion, and Toe Flexion-Extension. In order to reconstruct the continuous state space parameters proper of the assembly's control of quiet standing, a hybrid non-linear Extended Kalman Filter based technique is proposed to combine a base-excited inverted pendulum kinematic model of the robot with the discrete-time position measurements. This research work provides effective solutions and readily available software tools to improve the human interaction with robotic assistive devices, advancing the research in neuromusculoskeletal modeling to better understand the mechanisms of actuation provided by human muscles and the rules that govern the lower limb joint stiffness regulation. The obtained results suggest that the neuromusculoskeletal modeling technology can be exploited to address the challenges on the development of musculoskeletal humanoids, new generation human-robot interfaces, motion control algorithms, and intelligent assistive wearable devices capable to effectively ensure a proper dynamic coupling between human and robot

Modeling, Dynamics and Control of an Extended Elastic Actuator in Musculoskeletal Robot System

The conventional actuator of robot needs to be improved since the bandwidth of motor is limited and it cannot provide enough flexibility to perform the compliance in robot locomotion interacted with environment. In this paper, we present a novel elastic actuator so as to enhance the range of robot activities for adaptability. Considering the characteristics of elasticity and the demands in reality, a feasible study model is developed and constructed. According to the theory of Newton-Euler dynamics equations, the dynamics of model is mathematically described. To avoid unpredictable errors and manage joint oscillation in advance, we also employ a feedforward controller to operate the actuator. Moreover, the actuator can be regarded as the robotic "muscle-tendon" for its function is similar to the muscle-tendon model in human body. Therefore, we apply this actuation to a virtual robot arm based on the Musculoskeletal Robot System (MRS) to evaluate the performances of elastic actuators. The results of experiments indicate that this actuation is effective and contributed to realize the compliant locomotion