Antagonist Inhibition Control in Redundant Tendon-Driven Structures Based on Human Reciprocal Innervation for Wide Range Limb Motion of Musculoskeletal Humanoids

Abstract

The body structure of an anatomically correct tendon-driven musculoskeletal humanoid is complex, and the difference between its geometric model and the actual robot is very large because expressing the complex routes of tendon wires in a geometric model is very difficult. If we move a tendon-driven musculoskeletal humanoid by the tendon wire lengths of the geometric model, unintended muscle tension and slack will emerge. In some cases, this can lead to the wreckage of the actual robot. To solve this problem, we focused on reciprocal innervation in the human nervous system, and then implemented antagonist inhibition control (AIC) based on the reflex. This control makes it possible to avoid unnecessary internal muscle tension and slack of tendon wires caused by model error, and to perform wide range motion safely for a long time. To verify its effectiveness, we applied AIC to the upper limb of the tendon-driven musculoskeletal humanoid, Kengoro, and succeeded in dangling for 14 minutes and doing pull-ups.

Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

“…This algorithm collects the time-series errors of the position and velocity of the end-effector to form a dataset and takes the result of multiplying the error by the gain term into the next iteration, which avoids the real-time inverse dynamics solution process for redundant systems. Jäntsch et al [85] proposed a scalable joint space control scheme based on the computed torque control. This method first uses a multilayer perceptron to establish the mapping from muscle force to joint torque and then uses a proportional-integral-derivative controller to calculate the joint torque required to complete the motion task.…”

A feedforward compensation approach for cable-driven musculoskeletal systems

“…When the target joint angle is qMS , and the current joint angle is q MS , J c (q MS ) • (q MS − q MS ) indicates whether each muscle is an agonist or an antagonist's muscle when moving to that position. In the literature [16], antagonistic muscle relaxation τ r was expressed as:…”

“…On the other hand, the skeletal structure can be considered as consisting of connecting rods connected sequentially through rotating joints. Several scholars have studied the friction problem of ordinary robots [12][13][14][15][16]. Based on the LuGre dynamic model, Lischinsky developed a friction model for a hydraulically driven manipulator [12].…”

“…In addition, some scholars such as Katz pointed out that friction is influenced by motor torque [14]. When the robot arm cannot move to the desired position due to the wrong model, the cable appears overloaded/slack and even damages the robot [16].…”

Design principles of a human mimetic humanoid: Humanoid platform to study human intelligence and internal body system

“…Not only muscle space, but also joint-space torque controller for flexible and adaptive environmental contact was implemented (45). On the basis of human reciprocal innervation that suppresses co-contraction in muscle antagonism, we implemented antagonist inhibition control that contributed to arm movement in a wide range of motions (46).…”