Target search using a swarm of robots is a classic research topic that poses challenges, particularly in conducting multi-target searching in unknown environments. Key challenges include high communication cost among robots, unknown positions of obstacles, and the presence of multiple targets. To address these challenges, we propose a novel Robotic Flow Direction Algorithm (RFDA), building upon the modified Flow Direction Algorithm (FDA) to suit the characteristics of the robot’s motion. RFDA efficiently reduces the communication cost and navigates around unknown obstacles. The algorithm also accounts for scenarios involving isolated robots. The pipeline of the proposed RFDA method is outlined as follows: (1). Learning strategy: a neighborhood information based learning strategy is adopted to enhance the FDA’s position update formula. This allows swarm robots to systematically locate the target (the lowest height) in a stepwise manner. (2). Adaptive inertia weighting: An adaptive inertia weighting mechanism is employed to maintain diversity among robots during the search and avoid premature convergence. (3). Sink-filling process: The algorithm simulates the sink-filling process and moving to the aspect slope to escape from local optima. (4). Isolated robot scenario: The case of an isolated robot (a robot without neighbors) is considered. Global optimal information is only required when the robot is isolated or undergoing the sink-filling process, thereby reducing communication costs. We not only demonstrate the probabilistic completeness of RFDA but also validate its effectiveness by comparing it with six other competing algorithms in a simulated environment. Experiments cover various aspects such as target number, population size, and environment size. Our findings indicate that RFDA outperforms other methods in terms of the number of required iterations and the full success rate. The Friedman and Wilcoxon tests further demonstrate the superiority of RFDA.

使用一群机器人进行目标搜索是一个经典的研究课题，但也带来了挑战，特别是在未知环境中进行多目标搜索时。主要挑战包括机器人之间的高通信成本、未知的障碍物位置以及多个目标的存在。为了解决这些挑战，我们提出了一种新颖的机器人流向算法（RFDA），该算法基于改进的流向算法（FDA）以适应机器人运动的特征。 RFDA 有效降低了通信成本并绕过了未知的障碍。该算法还考虑了涉及孤立机器人的场景。所提出的 RFDA 方法的流程概述如下： (1)．学习策略：采用基于邻里信息的学习策略来增强FDA的位置更新公式。这使得群体机器人能够以逐步的方式系统地定位目标（最低高度）。 （2）。自适应惯性加权：采用自适应惯性加权机制来在搜索过程中保持机器人之间的多样性并避免过早收敛。 （3）。汇填充过程：算法模拟汇填充过程并移动到坡向坡度以逃避局部最优。 （4）。孤立机器人场景：考虑孤立机器人（没有邻居的机器人）的情况。仅当机器人处于孤立状态或进行水槽填充过程时才需要全局最优信息，从而降低通信成本。我们不仅证明了 RFDA 的概率完整性，还通过在模拟环境中将其与其他六种竞争算法进行比较来验证其有效性。 实验涵盖目标数量、种群大小、环境大小等各个方面。我们的研究结果表明，RFDA 在所需迭代次数和完全成功率方面优于其他方法。 Friedman 和 Wilcoxon 测试进一步证明了 RFDA 的优越性。