The objective of area exploration is to traverse the area effectively and random walk methods are the most commonly used searching strategy for swarm robots. Existing research mainly compares the effectiveness of various random walk methods through experimental verification, which has relatively large limitations. In order to make the application of the random walk methods more convenient, this paper quantitatively analyzes the searching efficiency (SE) of random walk methods. Firstly, the formula of the mean square displacement (MSD) of the robot position is given, and it is shown that the mean and the variance of the random step length are the factors that affect the SE. In addition, in order to produce the suitable step length, a truncated random walk method is constructed to make the generated step lengths follow a given distribution and the step lengths are within a specified range, thereby improving the SE. Thirdly, the correlations between the step length threshold (SLT), the area of the region, and the number of robots are provided based on MSD and truncated random walk method. When the area of region and the number of robots are fixed, there exists a SLT. When the expectation of the step length is greater than SLT, the swarm robots can achieve the highest SE. The area exploration task of swarm robots are carried out in simulation experiments and the coverage ratio is used to evaluate the SE of each random walk method. The experimental results show that when the area and the number of robots are given, there exist an optimal step length, which can enable the robots to achieve the optimal search.

区域探索的目的是有效地穿越区域，而随机游走方法是群体机器人最常用的搜索策略。现有研究主要通过实验验证比较各种随机游走方法的有效性，存在较大的局限性。为了使随机游动方法的应用更加方便，本文定量分析了随机游动方法的搜索效率。首先，给出了机器人位置的均方位移（MSD）公式，结果表明，随机步长的均值和方差是影响SE的因素。另外，为了产生合适的步长，构造了一种截断的随机游动方法，以使生成的步长遵循给定的分布，并且步长在指定范围内，从而提高了SE。第三，基于MSD和截断的随机游走方法，提供了步长阈值（SLT），区域面积和机器人数量之间的相关性。当区域的面积和机械手的数量固定时，就存在一个SLT。当期望的步长大于SLT时，群机器人可以达到最高SE。群体机器人的区域探索任务在模拟实验中进行，覆盖率用于评估每种随机行走方法的SE。实验结果表明，当给出机器人的面积和数量时，存在最佳步长，