Wyatt Felt

Body-In-The-Loop: Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost

This paper demonstrates methods for the online optimization of assistive robotic devices such as powered prostheses, orthoses and exoskeletons. Our algorithms estimate the value of a physiological objective in real-time (with a body “in-the-loop”) and use this information to identify optimal device parameters. To handle sensor data that are noisy and dynamically delayed, we rely on a combination of dynamic estimation and response surface identification. We evaluated three algorithms (Steady-State Cost Mapping, Instantaneous Cost Mapping, and Instantaneous Cost Gradient Search) with eight healthy human subjects. Steady-State Cost Mapping is an established technique that fits a cubic polynomial to averages of steady-state measures at different parameter settings. The optimal parameter value is determined from the polynomial fit. Using a continuous sweep over a range of parameters and taking into account measurement dynamics, Instantaneous Cost Mapping identifies a cubic polynomial more quickly. Instantaneous Cost Gradient Search uses a similar technique to iteratively approach the optimal parameter value using estimates of the local gradient. To evaluate these methods in a simple and repeatable way, we prescribed step frequency via a metronome and optimized this frequency to minimize metabolic energetic cost. This use of step frequency allows a comparison of our results to established techniques and enables others to replicate our methods. Our results show that all three methods achieve similar accuracy in estimating optimal step frequency. For all methods, the average error between the predicted minima and the subjects’ preferred step frequencies was less than 1% with a standard deviation between 4% and 5%. Using Instantaneous Cost Mapping, we were able to reduce subject walking-time from over an hour to less than 10 minutes. While, for a single parameter, the Instantaneous Cost Gradient Search is not much faster than Steady-State Cost Mapping, the Instantaneous Cost Gradient Search extends favorably to multi-dimensional parameter spaces.

Contraction Sensing With Smart Braid McKibben Muscles

The inherent compliance of soft fluidic actuators makes them attractive for use in wearable devices and soft robotics. Their flexible nature permits them to be used without traditional rotational or prismatic joints. Without these joints, however, measuring the motion of the actuators is challenging. Actuator-level sensors could improve the performance of continuum robots and robots with compliant or multi-degree-of-freedom joints. We make the reinforcing braid of a pneumatic artificial muscle (PAM or McKibben muscle) “smart” by weaving it from conductive insulated wires. These wires form a solenoid-like circuit with an inductance that more than doubles over the PAM contraction. The reinforcing and sensing fibers can be used to measure the contraction of a PAM actuator with a simple linear function of the measured inductance, whereas other proposed self-sensing techniques rely on the addition of special elastomers or transducers, the technique presented in this paper can be implemented without modifications of this kind. We present and experimentally validate two models for Smart Braid sensors based on the long solenoid approximation and the Neumann formula, respectively. We test a McKibben muscle made from a Smart Braid in quasi-static conditions with various end loads and in dynamic conditions. We also test the performance of the Smart Braid sensor alongside steel.

Smart braid: Air muscles that measure force and displacement

In this paper, we introduce a novel method to measure the contraction length and force output of Pneumatic Artificial Muscles. Our key innovation is to make the braid of these fiber-reinforced actuators out of insulated wires that form a single electric circuit. Changes in the inductance and resistance of this circuit can be related to muscle contraction length and muscle force. We implemented the proposed approach in two McKibben muscles that were evaluated under a variety of air pressures (0-200 kPa) and loads (0-45 N). The experiments confirmed our theoretical prediction of a roughly linear relationship between the measured inductance and the contraction length, as well as a strong correlation between the measured resistance and the actuator force. We were able to determine force with a resolution of 5N and contraction length with a resolution of 0.5 mm. This technique can be used to create flexible, precise, and robust self-sensing actuators that benefit a multitude of robotic applications.

Sensing the motion of bellows through changes in mutual inductance

Bellows-like actuators are popular in soft robotic systems. Sensing the movement of these actuators with traditional sensors is challenging. This work proposes and tests a sensing system based on the changing mutual inductance of wire coils on bellows. A method for modeling the changes in mutual inductance between coils of tightly-packed wires is introduced. Changes in mutual inductance are measured through the self-inductance of a circuit made up of the coils in series. The self-inductance of the circuit measures the bellows bend-angle. The experiments show an approximately quadratic relationship between the bend angle and the measured inductance. From a bend angle of 121 ° to -67 ° the inductance of the circuit increases by 19 %. The bias-inducing effects of shear strain, torsional strain, non-uniform bending, and nearby metal are also explored.

An Inductance-Based Sensing System for Bellows-Driven Continuum Joints in Soft Robots

In this work we present a novel, inductance-based system to measure and control the motion of bellows-driven continuum joints in soft robots. The sensing system relies on coils of wire wrapped around the minor diameters of each bellows on the joint. As the bellows extend, these coils of wire become more distant, decreasing their mutual inductance. Measuring this change in mutual inductance allows us to measure the motion of the joint. By dividing the sensing of the joint into two sections and measuring the motion of each section independently, we are able to measure the overall deformation of the joint with a piece-wise constant-curvature approximation. This technique allows us to measure lateral displacements that would be otherwise unobservable. When measuring bending, the inductance sensors measured the joint orientation with an RMS error of \(1.1^\circ \). The inductance sensors were also successfully used as feedback to control the orientation of the joint. The sensors proposed and tested in this work provided accurate motion feedback that would be difficult to achieve robustly with other sensors. This sensing system enables the creation of robust, self-sensing soft robots based on bellows-driven continuum joints.