基于改进生物启发神经网络的机器人目标搜索方法

Robot Target Searching Method Based on Improved Biologically Inspired Neural Network

针对未知环境下机器人目标搜索的问题,按照机器人能力不同对搜索区域进行划分,目标点在自己运动的过程中会在局部范围内留下信息素并且这些信息素会随着时间的流失而减少,机器人可以探测到这些信息素的多少进而影响机器人下一个搜索位置的选择。本文采用改进生物启发神经网络选取机器人探索范围内活性值最大的点作为下一个搜索位置。为了防止在连续的时间段内多次选择相同的点,引入禁忌搜索,把多次选择相同的点放入禁忌表中,可以有效防止陷入局部最优点。与随机搜索方式和原始的生物启发神经网络进行对比,验证了该方法对动态目标的搜索具有良好的效果。

Aiming at the problem of robot target search in an unknown environment,the search area is divided according to the robot's ability. The target points will leave pheromones in the local range during their motion,and these pheromones will decrease with time. The robot can detect the size of these pheromones and affect the next robot's location. In this paper,the active value in the exploration range of robot is selected by improving the biologically inspired neural network. In order to prevent multiple choices of the same point in a continuous time period,a tabu search is introduced,and the same points are selected in the tabu table,which can be effectively prevented from getting into the local best advantage. Compared with the random search method,the method is proved to have a good effect on the target search.