Two-Dimensional Robot Motion Planning by Detecting Local Minimum Points of Oval Potential Field.

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  • 楕円ポテンシャル場の局所最小点検出による２次元移動ロボットの動作計画法

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This paper studies two-dimensional robot motion planning based on the oval potential field. The drawback to the traditional oval potential method is that local minimum points might be generated and the robot may stick at those points if obstacles are located nearby or are concave. This paper discusses an improvement on the traditional method. Local minimum points are detected by calculating the gaussian curvature and mean curvature of the potential field, and are combined to the nearest obstacle as vertexes, and then the obstacle is virtually modified to a convex polygon. As a result, local minimum points, where a robot may stick, are reduced. The effectiveness of the present method is shown through numerical simulations.

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