Motion planning and stochastic control with experimental validation on a planetary rover

Publication Type:
Conference Proceeding
IEEE International Conference on Intelligent Robots and Systems, 2012, pp. 4716 - 4723
Issue Date:
Filename Description Size
06386229.pdfPublished version1.38 MB
Adobe PDF
Full metadata record
Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This model is used to construct a control policy for navigation to a goal region in a terrain map built using an on-board RGB-D camera. The terrain includes flat ground, small rocks, and non-traversable rocks. We report the results of 200 simulated and 35 experimental trials that validate the approach and demonstrate the value of considering control uncertainty in maintaining platform safety. © 2012 IEEE.
Please use this identifier to cite or link to this item: