Name
Abstract | ||
|---|---|---|
Although legged locomotion over a moderately rugged terrain can be accomplished by employing simple reactions to the ground contact information, a more effective approach, which allows predictively avoiding obstacles, requires a model of the environment and a control algorithm that takes this model into account when planning footsteps and leg movements. This article 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 onboard perception, provided that a reasonable general path is known. An efficient method for real-time building of a local elevation map from sparse two-dimensional (2D) range measurements of a 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. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1002/rob.20397 | Safety Security and Rescue Robotics |
Keywords | DocType | Volume |
terrain mapping module,terrain perception,effective approach,rugged terrain,rough terrain mapping,walking robot,wiley periodicals,foothold selection,uneven terrain,legged robot,six-legged robot,control algorithm,laser scanner,real time,image classification,robot learning,image sensors | Journal | 28 |
Issue | ISSN | ISBN |
4 | 1556-4959 | 978-1-4244-8899-5 |
Citations | PageRank | References |
13 | 0.96 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominik Belter | 1 | 100 | 16.31 |
| Piotr Skrzypczynski | 2 | 148 | 25.07 |