AI summary
Problem
Friction-based inchworm robots must balance anchoring force with body extension to prevent slipping, but traditional methods require offline tuning for each specific terrain, limiting adaptability.
Approach
The authors designed a 6-DoF inchworm robot with embedded optical slip sensors and a controller that automatically shifts the robot's center of mass to increase friction on anchoring pads whenever slippage is detected.
Key results
- Matches offline-tuned locomotion speed across four terrain types
- Surpasses offline tuning on select surfaces via dynamic load adjustment
- Integrates 2D turning with real-time slip-aware gait adaptation
- Experimentally validated on a 6-DoF robot using three inchworm gaits
Why it matters
Provides a robust, terrain-agnostic locomotion strategy for friction-based inchworm robots, reducing manual calibration needs for inspection and exploration applications.
Abstract
In this paper, we study adaptive locomotion for inchworm-like robots that move using friction-based pads in- spired by snake scales. The robot moves by alternating extension and contraction phases, which require enough friction at the rear and front pads. Locomotion speed depends on how far the body extends, but it is limited by the friction available on the pads. Since the pads are passive, friction can only be controlled by shifting body weight onto them. The challenge is to balance friction and extension length to achieve fast movement on different terrains. Previous work relied on offline tuning for specific terrain types. We propose a new adaptive controller that automatically adjusts the friction requirements by detecting slippage. We demonstrate the approach using a six- degree-of-freedom inchworm-like robot and three locomotion strategies adapted from the literature, which were tested across four terrain types. The locomotion performance is measured by the achieved average locomotion speed, cost of transport, and reliability. Based on the experimental results, the proposed adaptive locomotion achieves performance similar to offline- tuned locomotion and even surpasses it on certain terrains.