Muscular Damping Distribution Strategy for Bio-Inspired, Soft Motion Control at Variable Precision.

Bio-inspired and compliant control approaches have been studied by roboticists for decades to achieve more natural robot motion. Independent of this, medical and biological researchers have discovered a wide variety of muscular properties and higher-level motion characteristics. Although both disciplines strive to better understand natural motion and muscle coordination, they have yet to meet. This work introduces a novel robotic control strategy that bridges the gap between these distinct areas. By applying biological characteristics to electrical series elastic actuators, we developed a simple yet efficient distributed damping control strategy. The presented control covers the entire robotic drive train, from abstract whole-body commands to the applied current. The functionality of this control is biologically motivated, theoretically discussed, and finally evaluated through experiments on the bipedal robot Carl. Together, these results demonstrate that the proposed strategy fulfills all requirements that are necessary to continue developing more complex robotic tasks based on this novel muscular control philosophy.

Analysis and Improvements in AprilTag Based State Estimation.

In this paper, we analyzed the accuracy and precision of AprilTag as a visual fiducial marker in detail. We have analyzed error propagation along two horizontal axes along with the effect of angular rotation about the vertical axis. We have identified that the angular rotation of the camera (yaw angle) about its vertical axis is the primary source of error that decreases the precision to the point where the marker system is not potentially viable for sub-decimeter precise tasks. Other factors are the distance and viewing angle of the camera from the AprilTag. Based on these observations, three improvement steps have been proposed. One is the trigonometric correction of the yaw angle to point the camera towards the center of the tag. Second, the use of a custom-built yaw-axis gimbal, which tracks the center of the tag in real-time. Third, we have presented for the first time a pose-indexed probabilistic sensor error model of the AprilTag using a Gaussian Processes based regression of experimental data, validated by particle filter tracking. Our proposed approach, which can be deployed with all three improvement steps, increases the system's overall accuracy and precision by manifolds with a slight trade-off with execution time over commonly available AprilTag library. These proposed improvements make AprilTag suitable to be used as precision localization systems for outdoor and indoor applications.

Exploiting the intrinsic deformation of a prosthetic foot to estimate the center of pressure and ground reaction force.

The properties of the foot deployed in a bipedal robot that targets the rendering of a human-like dynamic gait are crucial. Firstly, it has to implement a set of mechanical mechanisms/properties that improve the efficiency of the locomotion. Secondly, it has to integrate a sensory system that captures the interaction with the ground with suitable precision. Both systems-the mechanical and the sensory system-have to be integrated as tightly as possible to keep the overall dimensions and weight low. Being the most distal element of the leg, especially the latter is crucial for favorable leg dynamics. Regarding the structural properties, a modern prosthetic foot poses a good solution and has hence been adopted in various bipeds. Their elaborated structures-mostly made from carbon fiber composites-are designed to imitate the mechanisms of the anthropomorphic counterpart. The following presents a concept to estimate the ground interaction based on the intrinsic deformation of a commercially available prosthesis. To measure the deformation, strain gauges are applied to its main structural elements. Using this information, the center of pressure and the normal force acting on it are estimated. The performance of two approaches-linear regression and neural networks-is presented and compared. Finally, the accuracy of the strain-based estimation is evaluated in two experiments and compared to a conventional force/torque sensor (FTS)-based system and a pressure insole. While the presented work is initially motivated by robotics research, it might as well be transferred to the design of a modern actively actuated prosthesis.

Design of the musculoskeletal leg [Formula: see text] based on the physiology of mono-articular and biarticular muscles in the human leg.

In a lower extremity musculoskeletal leg, the actuation kinematics define the interaction of the actuators with each other and the environment. Design of such a kinematic chain is challenging due to the existence of the redundant biarticular actuators which simultaneously act on two joints, generating a parallel mechanism. Actuator kinematics is mainly dependent on the moment arm profile of the actuation system. It is a common practice to select a constant moment arm value for robotic actuation system; nevertheless, biological muscles feature a distinctive nonlinear moment arm profile that has been ignored in the design of the musculoskeletal robots. In this paper, we propose a design paradigm for compliant robotic leg [Formula: see text] based on the direct replication of the human leg anatomy. The resulting mechanical system should (a) demonstrate a similar moment arm profile as in leg musculature, (b) exhibit expected physiological behavior of the muscles, (c) provide insight into the interaction of the actuators and possible improvement in the efficiency of the movements. We provide a comprehensive analysis of the moment arm profile of the leg musculature. The actuator kinematics of the designed leg is validated by comparing the contraction velocities of the muscles and actuators. The biological characteristics of the actuators are analyzed using the jump experiment data conducted on the previous version of the leg. The major physiological characteristics of the biarticular muscles, ligamentous action, and distal power transfer, is successfully demonstrated by the robotic leg. Our analysis demonstrates that the proposed structural design of the actuation system can improve the mechanical efficiency of this particular jump experiment up to [Formula: see text] compared to the leg without actuator redundancy. Compared to the previous version of the leg, by only modifying the moment arm profiles, we can achieve an efficiency improvement of approximately [Formula: see text].

A Variable Stiffness Actuator Module With Favorable Mass Distribution for a Bio-inspired Biped Robot.

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio.

Design of variable-damping control for prosthetic knee based on a simulated biped.

This paper presents the development of a variable-damping controller for a prosthetic knee using a simulated biped in a virtual environment before real tests are conducted on humans. The simulated biped incorporates several features of human walking, such as functional morphology, exploitation of inherent dynamics, hierarchical control network, combination of feed-forward and feedback controllers and phase-dependent modulation. Based on this virtual model of human walking, we have studied biomechanical aspects of the knee joint during walking. Observing the damping profile developed by the simulated biped throughout a gait cycle, we designed a controller for the knee joint. This controller has been evaluated on a modified version of the simulated biped, in which the model of a real prosthetic leg was incorporated. Results of such experiments for walking on flat and rough terrains have provided satisfactory outputs, including improved robustness.