Research/ PLANETARY ROBOTICS

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

Rowan McAllister, Thierry Peynot, Robert Fitch, Salah Sukkarieh

IROS 2012/Oct 1, 2012/ 15 citations

Technique

Motion Planning & NavigationOpenAlex topic: Robotic Path Planning Algorithms · Perception & State EstimationOpenAlex topic: Robotics and Sensor-Based Localization · Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics

Indexed as

OpenAlexRobotic Path Planning Algorithms · Reinforcement Learning in Robotics · Robotics and Sensor-Based Localization

DOI Semantic Scholar OpenAlex

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.