Improving Gecko Adhesive Performance in Robotic Systems Through Trajectory Optimization

Abstract

Gecko-inspired adhesives have attracted considerable attention due to their unique combination of strong, yet reversible adhesion to diverse surfaces. However, their integration into robotic systems remains limited due to sensitivity to contact alignment, typically requiring near-perpendicular engagement. Yet, many robotic tasks involve varying approach and detachment angles, highlighting 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 optimization? 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 trajectories. 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 applications, including grippers and climbers.