sensitivity-enhancement-soft-capacitive-pressure-sensors-using

Sensitivity Enhancement of Soft Capacitive Pressure Sensors Using a Solvent Evaporation-Based Porosity Control Technique

Soft pressure sensors play a significant role in developing &#34;man-machine&#34; tactile sensation in soft robotics and haptic interfaces. Specifically, ...

Zhejiang University

Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A   type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.

Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot.

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.

Article	Year
Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot. Frontiers in robotics and AI\| PubMed ID: 33501128	2019
Adaptive Locomotion Control of a Hexapod Robot via Bio-Inspired Learning. Frontiers in neurorobotics\| PubMed ID: 33574748	2021

Haozhen Chi

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