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针对传统人工势场中存在局部陷阱问题,提出一种基于灰色定性理论的人工势场算法.首先将环境中自由空间分解为一组凸多边形,以凸多边形的顶点和邻接关系作为关键信息,并分别构成灰色定性基本元和灰色定性关系,由灰色定性关系推理从起始点到目标点需经过的凸多边形序列,再用广义白化函数计算凸多边形序列中的势场.理论分析和实验均表明该算法能够确保机器人在有限的时间内安全到达目标点.
In order to solve the problem of local traps in traditional artificial potential field, an artificial potential field algorithm based on gray qualitative theory is proposed. Firstly, the free space in the environment is decomposed into a set of convex polygons, taking the vertices and adjacency relations of convex polygons as the key information Respectively, constitute the gray qualitative relationship between the basic element and the gray, the gray qualitative relationship between reasoning from the starting point to the target point to go through the convex polygon sequence, and then use the generalized whitening function to calculate the potential field in the convex polygon sequence. Theoretical analysis and experiments show that the The algorithm ensures that the robot reaches the target safely within a limited time.