The capacity to predict motion adequately over the time scale of a few seconds is fundamental to autonomous mobility.
Model predictive optimal control is a general formalism within which most historical approaches can be cast as special
cases. Applications continue to grow in ambition to seek higher precision of motion and/or higher vehicle speeds.
Predictions must therefore improve in fidelity and speed simultaneously.
We favor an approach to precision motion control that we call parametric optimal control. It formulates the optimal
control problem as one of nonlinear programming - optimizing over a space of parameterized controls encoding all
feasible motions. It enables efficient inversion of the solutions to the equations of motion for a ground vehicle. Such an
inversion enables a computation of precisely the command signals necessary to drive the vehicle to goal position,
heading, and curvature while following the contours of the terrain under arbitrary wheel terrain interactions.
Dynamics inversion is so fundamental that many other mobility behaviors can be constructed from it. Fielded
applications include pallet acquisition controls for factory AGVs, high speed adaptive path following for military UGVs,
compensation for wheel slip on the Mars Exploration Rovers and full configuration space planning in dense obstacle
fields.
Terrain map building is an essential component of a planning autonomous navigator. External terrain must be represented in a manner that is convenient for the path planning subsystem to use, and that is useful for fine tuning the position estimate. This work is concerned with the solution of the particular problems encountered when attempting high speed navigation of an autonomous vehicle on rough terrain. These problems include the requisite longer downrange field of view, the range shadow problem, the image fusion problem, and the motion of the vehicle during image digitization. Experimental results have been obtained for an all terrain autonomous vehicle testbed--the Navlab II. The perception system was successful in supporting runs of 6.7 kilometers at speeds averaging 1.8 meters/second while checking for obstacles, 5.1 kilometers at 1.8 meters/sec while avoiding obstacles, and 0.3 kilometers at 4.5 meters/second while checking for obstacles.
Autonomous cross-country navigation is essential for outdoor robots moving about in unstructured environments. Most existing systems use range sensors to determine the shape of the terrain, plan a trajectory that avoids obstacles, and then drive the trajectory. Performance has been limited by the range and accuracy of sensors, insufficient vehicle-terrain interaction models, and the availability of high-speed computers. As these elements improve, higher- speed navigation on rougher terrain becomes possible. We have developed a software system for autonomous navigation that provides for greater capability. The perception system supports a large braking distance by fusing multiple range images to build a map of the terrain in front of the vehicle. The system identifies range shadows and interpolates undersamples regions to account for rough terrain effects. The motion planner reduces computational complexity by investigating a minimum number of trajectories. Speeds along the trajectory are set to provide for dynamic stability. The entire system was tested in simulation, and a subset of the capability was demonstrated on a real vehicle. Results to date include a continuous 5.1 kilometer run across moderate terrain with obstacles. This paper begins with the applications, prior work, limitations, and current paradigms for autonomous cross-country navigation, and then describes our contribution to the area.
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