The Ragno and Messor robots tries to predictively avoid obstacles. It requires a model of the environment, and a control algorithm that takes this model into account when planning footsteps and leg movements. This research addresses the issues of terrain perception and modeling, and foothold selection in a walking robot. An integrated system is presented that allows a legged robot to traverse previously unseen, uneven terrain using only on-board perception, provided a reasonable general path is known. An efficient method for real-time building of a local elevation map from sparse 2D range measurements of the miniature 2D laser scanner is described. The terrain mapping module supports a foothold selection algorithm, which employs unsupervised learning to create an adaptive decision surface. The robot can learn from realistic simulations, therefore no a priori expert-given rules or parameters are used. The usefulness of our approach is demonstrated in experiments with the six-legged robot Messor. We discuss the lessons learned in field tests, and the modifications to our system that turned out to be essential for successful operation under real-world conditions.
D. Belter, Adaptive foothold selection for a hexapod robot walking on rough terrain, 7th Workshop on Advanced Control and Diagnosis, Zielona Góra, cd-rom, 19-20 November 2009 (pdf)
D. Belter, P. Łabęcki, P. Skrzypczyński, Map-based Adaptive Foothold Planning for Unstructured Terrain Walking, 2010 IEEE International Conference on Robotics and Automation, May 3-8, Anchorage, Alaska, USA, pp. 5256-5261, 2010 (pdf)
K. Walas, D. Belter, Supporting locomotive functions of a six-legged walking robot, International Journal of Applied Mathematics and Computer Science, vol. 21(2), pp. 363-377, 2011 (pdf)
D. Belter, P. Skrzypczyński, Rough terrain mapping and classification for foothold selection in a walking robot, Journal of Field Robotics, vol. 28(4), pp. 497-528, 2011 (pdf)