Title :
Predictable mobility
Author :
Ishigami, Genya ; Kewlani, Gaurav ; Iagnemma, Karl
Author_Institution :
Massachusetts Inst. of Technol., Cambridge, MA, USA
fDate :
12/1/2009 12:00:00 AM
Abstract :
In this article, a statistical mobility prediction for planetary surface exploration rovers has been described. This method explicitly considers uncertainty of the terrain physical parameters via SRSM and employs models of both vehicle dynamics and wheel-terrain interaction mechanics. The simulation results of mobility prediction using three different techniques, SMC, LHSMC, and SRSM, confirms that SRSM significantly improves the computational efficiency compared with those conventional methods. The usefulness and validity of the proposed method has been confirmed through experimental studies of the slope traversal scenario in two different terrains. The results show that the predicted motion path with confidence ellipses can be used as a probabilistic reachability metric of the rover position. Also, for the slope-traversal case, terrain parameter uncertainty has a larger influence on the lateral motion of the rover than on longitudinal motion. Future directions of this study will apply the proposed technique to the path-planning problem. Here, confidence ellipses will be used to define collision-free areas, which will provide useful criteria for generating safe trajectories.
Keywords :
Monte Carlo methods; aerospace control; aerospace robotics; collision avoidance; computational complexity; mechanical contact; mobile robots; planetary rovers; space vehicles; vehicle dynamics; wheels; LHSMC; Latin hypercube sampling Monte Carlo method; SRSM; confidence ellipses; path-planning problem; planetary surface exploration rovers; probabilistic reachability metric; standard Monte Carlo method; statistical mobility prediction; stochastic response surface method; terrain parameter uncertainty; terrain physical parameters; vehicle dynamics; wheel-terrain interaction mechanics; Computational efficiency; Deformable models; Mobile robots; Monte Carlo methods; Predictive models; Sampling methods; Statistical analysis; Uncertainty; Vehicle dynamics; Wheels; Field robots; space robotics; wheeled robots;
Journal_Title :
Robotics & Automation Magazine, IEEE
DOI :
10.1109/MRA.2009.934823