A two-stage optimal motion planner for autonomous agricultural vehicles

A two-stage optimal motion planner for autonomous agricultural vehicles This paper presents a two-stage motion planning algorithm which can compute low-cost motions for autonomous agricultural vehicles, for a given cost function defined over the entire path (e.g., shortest path, maximum clearance, etc.). In the first stage, the algorithm utilizes randomized motion planning to explore the space of possible motions and computes a feasible sub-optimal trajectory. In the second stage, the optimization of the stage-1 motion is formulated within the optimal control framework and function-space gradient descent is used to minimize the cost of the entire motion. The numerical results suggest that the two-stage motion planner can compute optimal or quasi-optimal motions in free space very quickly. In the presence of obstacles however, the execution time increases significantly. Furthermore, kino-dynamic, or dynamic motion models seem to be necessary in order to produce smooth motion trajectories. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

A two-stage optimal motion planner for autonomous agricultural vehicles

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Publisher
Kluwer Academic Publishers-Plenum Publishers
Copyright
Copyright © 2006 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-006-9022-9
Publisher site
See Article on Publisher Site

Abstract

This paper presents a two-stage motion planning algorithm which can compute low-cost motions for autonomous agricultural vehicles, for a given cost function defined over the entire path (e.g., shortest path, maximum clearance, etc.). In the first stage, the algorithm utilizes randomized motion planning to explore the space of possible motions and computes a feasible sub-optimal trajectory. In the second stage, the optimization of the stage-1 motion is formulated within the optimal control framework and function-space gradient descent is used to minimize the cost of the entire motion. The numerical results suggest that the two-stage motion planner can compute optimal or quasi-optimal motions in free space very quickly. In the presence of obstacles however, the execution time increases significantly. Furthermore, kino-dynamic, or dynamic motion models seem to be necessary in order to produce smooth motion trajectories.

Journal

Precision AgricultureSpringer Journals

Published: Sep 14, 2006

References

  • Automatic guidance for agricultural vehicles in Europe
    Keicher, R; Seufert, H

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