Improving Gecko Adhesive Performance in Robotic Systems through Trajectory Optimization
Dror Kobo, Goren Gordon, Bat-El Pinchasik
AI summary
Problem
Gecko-inspired adhesives are highly sensitive to contact alignment and struggle with varying approach angles and real-world surface irregularities, limiting their reliable use in robotic grippers and climbers.
Approach
A gecko-adhesive-equipped robotic arm was tested across multiple surfaces and orientations, using an online particle swarm optimization algorithm to dynamically find the optimal pitch and trajectory angles for attachment and detachment.
Key results
- Up to 17-fold improvement in adhesion strength and detachment efficiency
- Surface-specific optimization effectiveness dictated by counter-surface geometry
- Successful identification of optimal pitch and trajectory angles via particle swarm optimization
- Validated performance in practical object picking and inverted surface attachment tasks
Why it matters
This method overcomes critical alignment limitations of bio-inspired adhesives, enabling more reliable and energy-efficient robotic manipulation and climbing on complex real-world surfaces.
Abstract
Gecko-inspired adhesives have attracted considerable attention due to their unique combination of strong, yet reversible adhesiontodiversesurfaces.However,theirintegrationintorobotic systems remains limited due to sensitivity to contact alignment, typ- ically requiring near-perpendicular engagement. Yet, many robotic tasks involve varying approach and detachment angles, highlight- ing the need for adhesion that operates reliably across different orientations and loading conditions. This study addresses two key questions: Can the adhesion strength of gecko-inspired adhesives, integrated into robotic systems, be optimized using trajectory op- timization? And is this optimization surface-dependent? A gecko- inspired adhesive was integrated on a robotic arm’s end-effector, which attached to and detached from surfaces along various tra- jectories. The arm’s energy expenditure for each attachment- detachment cycle, along with the corresponding adhesion strength, were measured. Online particle swarm optimization algorithm was applied to identify conditions that improve adhesion strength and facilitate detachment. Results show that trajectory optimization significantly improves both adhesion strength and detachment efficiency up to 17-fold, with surface-specific effectiveness. These findings underscore the importance of considering both the forces generated by gecko-inspired adhesives and the energy required by the robot to attach and detach from surfaces at various angles and positions. Using optimization, this study helps overcome current limitations in the use of gecko-inspired adhesives for robotic appli- cations, including grippers and climbers.