An End-to-End Framework for Optimizing Foot Trajectory and Force in Dry Adhesion Legged Wall-Climbing Robots

TL;DR

This work addresses stable dry-adhesion wall-climbing with foot-end trajectory optimization by proposing the Foot Trajectory and Force Optimization Framework (FTFOF). FTFOF combines a universal three-segment $C^2$ Bezier trajectory with foot-structure constraints, data-driven mappings from trajectory to forces using a GRU with the DILATED loss, and a multi-objective NSGA-II optimization whose Pareto front is navigated by a Redundancy Hierarchical Strategy to yield a single optimal trajectory. A mobile generalized data-acquisition platform collects diverse force data, enabling robust mappings for $F_d$ and $F_p$ across surfaces, surfaces, and tasks. Experimental results on MST-M3F show substantial improvements over conventional trajectories (e.g., a 28% reduction in maximum detachment force and an 82% reduction in body jitter), and successful generalization to MST-Q demonstrate portability and practical impact for diverse dry-adhesive climbing robots.

Abstract

Foot trajectory planning for dry adhesion legged climbing robots presents challenges, as the phases of foot detachment, swing, and adhesion significantly influence the adhesion and detachment forces essential for stable climbing. To tackle this, an end-to-end foot trajectory and force optimization framework (FTFOF) is proposed, which optimizes foot adhesion and detachment forces through trajectory adjustments. This framework accepts general foot trajectory constraints and user-defined parameters as input, ultimately producing an optimal single foot trajectory. It integrates three-segment $C^2$ continuous Bezier curves, tailored to various foot structures, enabling the generation of effective climbing trajectories. A dilate-based GRU predictive model establishes the relationship between foot trajectories and the corresponding foot forces. Multi-objective optimization algorithms, combined with a redundancy hierarchical strategy, identify the most suitable foot trajectory for specific tasks, thereby ensuring optimal performance across detachment force, adhesion force and vibration amplitude. Experimental validation on the quadruped climbing robot MST-M3F showed that, compared to commonly used trajectories in existing legged climbing robots, the proposed framework achieved reductions in maximum detachment force by 28 \%, vibration amplitude by 82 \%, which ensures the stable climbing of dry adhesion legged climbing robots.

Figures (17)

• Figure 1: Legged Climbing Robot Foot Optimization Framework.
• Figure 2: Foot Trajectories for Legged Climbing Robots.
• Figure 3: Mobile Detachment Force Acquisition Platform.
• Figure 4: GRU Network Model Utilizing the DILATE Loss Function.
• Figure 5: Schematic diagram of the bending phenomenon of Bezier curves.