Supporting locomotive functions of a six-legged walking robot

Abstract

This paper presents a method for building a foothold selection module as well as methods for the stability check for a multi-legged walking robot. The foothold selection decision maker is shaped automatically, without expert knowledge. The robot learns how to select appropriate footholds by walking on rough terrain or by testing ground primitives. The gathered knowledge is then used to find a relation between slippages and the obtained local shape of the terrain, which is further employed to assess potential footholds. A new approach to function approximation is proposed. It uses the leastsquares fitting method, the Kolmogorov theorem and population-based optimization algorithms. A strategy for re-learning is proposed. The role of the decision support unit in the control system of the robot is presented. The importance of the stability check procedure is shown. A method of finding the stability region is described. Further improvements in the stability check procedure due to taking into account kinematic correction are reported. A description of the system for calculating static stability on-line is given. Methods for measuring stance forces are described. The measurement of stance forces facilitates the extended stability check procedure. The correctness of the method is proved by results obtained in a real environment on a real robot.

RGB–D terrain perception and dense mapping for legged robots

“…The robot needs accurate data from sensors to build a model of the environment. This model (map) can be used to adapt the gait to the terrain profile (Walas and Belter, 2011), plan the motion (Zucker et al, 2010) and localize the robot (Yoon et al, 2013), and should thus be designed taking into account the real-time operation of the entire navigation system. Among many existing representations of the environment (raw point cloud, occupancy map, 2.5D elevation map, triangle mesh, octree (Hornung et al, 2013), multi-level surface (MLS) maps (Pfaff et al, 2007)), we work with 2.5D elevation maps.…”