Ing. Jiří Kubík

In this work-in-progress report, we present experimental results of lightweight learning-based leg-contact detection methods for a small hexapod walking robot with position feed- back only. The detection of the leg contact with the surface is addressed as anomaly detection using predicted and measured positions of the leg’s joints in the leg swing phase. A polynomial regressor and three-layer neural network are evaluated regarding the prediction error and computational requirements using realistic datasets collected with the real hexapod walking robot.

Rough terrain locomotion is a domain where multi-legged robots benefit from their relatively complex morphology compared to the wheeled or tracked robots. Efficient rough terrain locomotion requires the legged robot sense contacts with the terrain to adapt its behavior and cope with the terrain irregularities. Usage of inverse dynamics to estimate the leg state and detect the leg contacts with the terrain suffers from computational complexity. Furthermore, it requires a precise analytical model identification that does not cope with adverse changes of the leg parameters such as friction changes due to the joint wear, the increased weight of the leg due to the mud deposits, and possible leg morphology change due to damage. In this paper, we report the experimental study on the locomotion performance with machine learning-based inverse dynamics model learning. Experimental examining three different learning models show that a simplified model is sufficient for leg collision detection learning. Moreover, the learned model is faster for calculation and generalizes better than more complex models when the leg parameters change.

Proprioceptive terrain sensing is essential for rough terrain traversal because it helps legged robots to negotiate individual steps by reacting to terrain irregularities. In this work, we propose to utilize inertial data in the detection of the contact between the leg and the terrain during the stride phase of the leg. We show that relatively cheap accelerometers can be utilized to reliably detect a foot-strike, and thus allow the robot to crawl irregular terrains. The continuous data processing is compared with the interrupt mode in which data are provided only around the foot-strike event. The interrupt mode exhibits significantly better performance, and it also supports generalization of the foot-strike event detector learned from data collected in slow locomotion to faster locomotion where the signals slightly change. The proposed solution is experimentally validated using a real hexapod walking robot for which the walking speed has been improved in comparison to the previous adaptive motion gait based on a force threshold-based position controller for the foot-strike detection.