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Related Experiment Video

Updated: Jul 4, 2026

Force and Position Control in Humans - The Role of Augmented Feedback

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Published on: June 19, 2016

Proprioceptive Feedback Control Improves Peristaltic Turning in Confined Environments.

Shane A Riddle¹, William Robert Pillers Nourse², Kathryn A Daltorio³

¹Mechanical & Aerospace Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, Ohio, 44106-7078, United States.

Bioinspiration & Biomimetics

|July 2, 2026

Summary

This summary is machine-generated.

Soft robots can now navigate complex pipe systems using a new adaptive turning algorithm. This closed-loop controller uses contact force feedback to adjust turning, enabling efficient movement through challenging confined spaces.

Keywords:

Active Turning Adaptive Control Confined Spaces Peristalsis Proprioceptive Feedback

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Last Updated: Jul 4, 2026

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Area of Science:

Robotics
Soft Robotics
Control Systems

Background:

Soft and flexible robots are ideal for confined spaces where traditional robots struggle.
Undulatory and peristaltic robots navigate enclosed tunnels effectively but face challenges with sharp turns and T-junctions.

Purpose of the Study:

To develop the first closed-loop, peristaltic turning algorithm for soft robots in confined spaces.
To enable soft robots to navigate complex geometries like sharp turns and T-junctions autonomously.

Main Methods:

Developed a closed-loop peristaltic turning algorithm that scales turning angle based on contact force feedback.
Verified the controller using a dynamic model of a soft worm robot in pipe bends of varying radii, diameters, and geometries.
Compared open-loop and closed-loop controllers using speed and cost of transport as metrics.

Main Results:

The adaptive algorithm maintained high speed performance across all tested bends, with only a 1.6% average speed loss compared to optimal fixed strategies.
The adaptive turning enabled traversal of small radius bends (0.5-0.6 m) where straight-line locomotion failed.
Adaptive turning demonstrated the lowest cost of transport in most pipe bend geometries and autonomously transitioned between turn directions and spiral turns.

Conclusions:

The developed adaptive turning algorithm significantly enhances the maneuverability of soft robots in confined environments.
This controller equips worm robots with advanced turning capabilities for diverse unstructured and confined spaces.
The adaptive algorithm represents a breakthrough for autonomous navigation in challenging subterranean or internal structures.