The ability of mobile robots to generate feasible trajectories online is an important requirement for their autonomous operation in unstructured environments. Many path generation techniques focus on generation of time-or distance-optimal paths while obeying dynamic constraints, and often assume precise knowledge of robot and/or environmental (i.e. terrain) properties. In uneven terrain, it is essential that the robot mobility over the terrain be explicitly considered in the planning process. Further, since significant uncertainty is often associated with robot and/or terrain parameter knowledge, this should also be accounted for in a path generation algorithm. Here, extensions to the rapidly exploring random tree (RRT) algorithm are presented that explicitly consider robot mobility and robot parameter uncertainty based on the stochastic response surface method (SRSM). Simulation results suggest that the proposed approach can be used for generating safe paths on uncertain, uneven terrain.
Planetary surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.
The ability of mobile robots to quickly and accurately analyze their dynamics is critical to their safety and efficient operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this must be considered in an analysis of robot motion. Here a Multi-Element generalized Polynomial Chaos (MEgPC) approach is presented that explicitly considers vehicle parameter uncertainty for long term estimation of robot dynamics. It is shown to be an improvement over the generalized Askey polynomial chaos framework as well as the standard Monte Carlo scheme, and can be used for efficient, accurate prediction of robot dynamics.
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