The Structure, Design, and Closed-Loop Motion Control of a Differential Drive Soft Robot

Wu Pang, Wang Jiangbei, Yanqiong Fei

Soft Robotics 2017 article cited by 65

importance 3/10 ai assisted Soft Robotics and Applications soft robotics stage: research

Problem

Steering an inchworm-inspired pneumatic soft robot is difficult because its motion is nonlinear, making closed-loop automatic control hard to achieve.

Why it matters

Nonlinear behavior blocks reliable steering, limiting where and how soft robots can be used.

What the AI did

Radial basis function neural networks trained the robot's turning ability on three surfaces, building a model linking friction coefficient, deflection angle, and inflation time for closed-loop control.

Results

Control model verified in linear and turning movement experiments; no quantitative metrics stated

Impact

Enabled closed-loop automatic control of the robot's linear and turning movements, per reported experiments.

Record

Venue	Soft Robotics
Published	2017
Work type	article
Authors	Wu Pang, Wang Jiangbei, Yanqiong Fei
Citations	65 (OpenAlex, at import time)
References	16
Model or method	radial basis function neural networks
Organization	not stated in source
Primary source	https://doi.org/10.1089/soro.2017.0042
Open access	closed
Keywords	soft robot, pneumatic actuator, radial basis function, closed-loop control, inchworm

Importance rating

3 / 10

incremental / niche

Abstract addresses closed-loop control of a single inchworm-inspired soft robot; no scale or broader impact stated; niche robotics advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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