Multimedia  

 

Volume 27 Issue 11-12 - Publication Date: 1 November 2008
 
Motion Planning for Legged Robots on Varied Terrain
 
Kris Hauser Department of Computer Science Stanford University Stanford, CA 94305-5447, USA, Timothy Bretl University of Illinois at Urbana-Champaign, Urbana, IL 61801-2935, USA, Jean-Claude Latombe Department of Computer Science Stanford University Stanford, CA 94305-5447, USA, Kensuke Harada Humanoid Research Group Intelligent Systems Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba, Ibaraki 305-8568, Japan, and Brian Wilcox Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109
 
In the classical image-based visual servoing framework, error signals are directly computed from image feature parameters, allowing, in principle, control schemes to be obtained that need neither a complete three-dimensional (3D) model of the scene nor a perfect camera calibration. However, when the computation of control signals involves the interaction matrix, the current value of some 3D parameters is required for each considered feature, and typically a rough approximation of this value is used. With reference to the case of a point feature, for which the relevant 3D parameter is the depth Z, we propose a visual servoing approach where Z is observed and made available for servoing. This is achieved by interpreting depth as an unmeasurable state with known dynamics, and by building a non-linear observer that asymptotically recovers the actual value of Z for the selected feature. A byproduct of our analysis is the rigorous characterization of camera motions that actually allow such observation. Moreover, in the case of a partially uncalibrated camera, it is possible to exploit complementary camera motions in order to preliminarily estimate the focal length without knowing Z. Simulations and experimental results are presented for a mobile robot with an on-board camera in order to illustrate the benefits of integrating the depth observation within classical visual servoing schemes.
 
Multimedia Key
= Video = Data = Code = Image
 
Extension
Type
Description
1
Example 1: HRP-2 taking a single step to place a foot
2
Example 2: HRP-2 taking a single step to remove a foot
3
Example 3: ATHLETE walking with an alternating tripod gait on smooth terrain (feasible).
4
Example 4: ATHLETE walking with an alternating tripod gait on uneven terrain (infeasible). When the chassis is green, the configuration is feasible. When the chassis changes color, one or more constraints have been violated.
5
Example 5: ATHLETE walking with no fixed gait on smooth terrain.
6
Example 6: ATHLETE walking with no fixed gait on uneven terrain.
7
Example 7: ATHLETE rappelling down an irregular 60° slope with no fixed gait on smooth terrain.
8
Example 8: HRP-2 on slightly uneven terrain (planar walking primitive).
9
Example 9: HRP-2 climbing a ladder with uneven rungs (ladder-climbing primitive).
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Example 10: HRP-2 traversing large boulders (side-step primitive).
11
Example 11: HRP-2 on steep and uneven terrain (multiple primitives).
 
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