Soft Robotics | Eugenio Frias-Miranda

Adaptation and Training Effects from a Passive Wearable Resistance Device During Exercise

Mon, 16 Dec 2024 00:00:00 +0000

The integration of technology into exercise regimens has emerged as a strategy to enhance normal human capabilities and return human motor function after injury or illness by enhancing motor learning. Much research has focused on how active devices, whether confined to a lab or made into a wearable format, can apply forces at set times and conditions to optimize injury prevention and proper movement. As a result, these devices tend to be confined to single movements or simple interventions. A focus on active forces, however, ignores the potential of continuous passive interactions. In this paper, we investigate how passive device behaviors by themselves can contribute to the process of training proper movement. Using a wearable resistance (WR) device, which is outfitted with elastic bands, we apply a force field that passively changes in response to full-body movements. We first develop a method to measure the produced forces from the device without impeding the function and we characterize the device’s force generation. We then present a study assessing the impact of the WR device on overhead squat form compared to visual or no feedback. Our findings suggest that the force fields produced while training with the WR device could improve performance in full-body exercises more consistently compared to direct visual feedback, with effects seen on cross-body asymmetry. Our results provide insights into the application of passive wearable resistance technology in practical exercise settings.

Anisotropic Stiffness and Programmable Actuation for Soft Robots Enabled by an Inflated Rotational Joint

Fri, 25 Oct 2024 00:00:00 +0000

Adaptation and Training Effects from a Passive, Wearable Resistance Device During Exercise

Sun, 21 Apr 2024 00:00:00 +0000

Vine Robot Localization Via Collision

Sun, 01 Oct 2023 00:00:00 +0000

Localization of Vine Robot Through Obstacle Collision

Sun, 25 Dec 2022 00:00:00 +0000

Collisions in robot navigation are conventionally avoided. However, complete obstacle avoidance is not always feasible within unstructured, unknown environments, like search and rescue, making life harder for rigid-bodied robots as they can become damaged by unintended collisions. On the other hand, robots with soft bodies which have compliant and deformable structures, can safely interact with the environment they are moving in. One specific soft robot, the vine robot, has exhibited excellent performance while moving through a constrained and unpredictable environment. The ability for these robots to safely interact with obstacles and use them for navigation has already been proved. In spite of this, localizing the robot remains an open challenge. In this research, we use our understanding of the nature of vine robot motion and the mathematical models developed for the same to be able to predict the tip position of the robot at every instant.

Using this localization model, we analyze the performance of the tip position prediction by observing its interaction with three obstacles. Through these interactions we found results with high quality tracking. Less than 5 percent error was observed on the path prediction (magnitude of error with respect to total length) of a 1.5-meter-long vine robot. It was also found that the accuracy of the localization model decreases with changes in pivot point. This highlights the needs for (1) understanding the pivot position errors better, and (2) improving the pivot position detection by improving the contact sensor cap. This is accomplished by (1) an experimental setup allowing for the adjustment of individual variables which determine the pivot point, and (2) redesign of the robot cap with improved sensing.