An Integrated Framework for Slip Detection and Recovery in Hexapod Robot Force Control

Hexapod robots demonstrate significant advantages in payload capacity and stability, making them an excellent choice for complex terrain applications such as disaster rescue and mountain transportation. However, operating on low-friction surfaces often leads to foot slippage, risking instability and performance degradation. In this work, we propose a novel integrated slip detection and recovery framework for actively force-controlled hexapod robots. We first analyze the fundamental characteristics of foot-ground interaction, revealing that slippage arises when the desired foot force exceeds the actual friction limits due to inaccuracies in estimated ground parameters. Based on this analysis, we introduce a velocity-based slip detection strategy, enabling prompt identification of slip events. Upon detection, we employ a Back Propagation (BP) neural network to accurately re-identify ground parameters, including unit normal vectors and friction coefficients at the contact points. These refined parameters are then integrated into a Quadratic Programming (QP) framework via an interpolation scheme, ensuring smooth transitions in foot force distribution. Both simulation and physical experimental validation demonstrate that our framework successfully detects slippage events, accurately identifies ground parameters, and achieves foot stabilization through corrected force distribution. This work offers a comprehensive solution to the mismatch between estimated and actual ground parameters, enabling hexapod robots to achieve stable locomotion in challenging low-friction environments.