Modeling Proprioceptive Sensing for Locomotion Control of Hexapod Walking Robot in Robotic Simulator

Abstract

Proprioceptive sensing encompasses the state of the robot given by its overall posture, forces, and torques acting on its body. It is an important source of information, especially for multi-legged walking robots because it enables efficient locomotion control that adapts to morphological and environmental changes. In this work, we focus on enhancing a simplified model of the multi-legged robot employed in a realistic robotic simulator to provide high-fidelity proprioceptive sensor signals. The proposed model enhancements are based on parameter identification and static and dynamic modeling of the robot. The enhanced model enables the V-REP robotic simulator to be used in real-world deployments of multi-legged robots. The performance of the developed simulation has been verified in the parameter search of dynamic locomotion gait to optimize the locomotion speed according to the limited maximal torques and self-collision free execution.