Lately, robot swarm has widely employed in many applications like search and rescue missions, fire forest detection and navigation in hazard environments. Each robot in a swarm is supposed to move without collision and avoid obstacles while performing the assigned job. Therefore, a formation control is required to achieve the robot swarm three tasks. In this article, we introduce a decentralized formation control algorithm based on the potential field method for robot swarm. Our formation control algorithm is proposed to achieve the three tasks: avoid obstacles in the environment, keep a fixed distance among robots to maintain a formation and perform an assigned task. An artificial neural network is engaged in the online optimization of the parameters of the potential force. Then, real-time experiments are conducted to confirm the reliability and applicability of our proposed decentralized formation control algorithm. The real-time experiment results prove that the proposed decentralized formation control algorithm enables the swarm to avoid obstacles and maintain formation while performing a certain task. The swarm manages to reach a certain goal and tracks a given trajectory. Moreover, the proposed decentralized formation control algorithm enables the swarm to escape from local minima, to pass through two narrow placed obstacles without oscillation near them. From a comparison between the proposed decentralized formation control algorithm and the traditional PFM, we obtained that NN-swarm successes to reach its goal with average accuracy 0.14 m compared to 0.22 m for the T-swarm. The NN-swarm also keeps a fixed distance between robots with a higher swarming error reaches 34.83%, while the T-swarm reaches 23.59%. Also, the NN-swarm is more accurate in tracking a trajectory with a higher tracking error reaches 0.0086 m compared to min. error of T-swarm equals to 0.01 m. Besides, the NN-swarm maintains formation much longer than T-swarm while tracking trajectory reaches 94.31% while the T-swarm reaches 81.07% from the execution time, in environments with different numbers of obstacles.

近来，机器人群已广泛应用于许多应用中，例如搜索和救援任务，在危险环境中的防火林检测和导航。群中的每个机器人在执行分配的作业时均应无碰撞移动并避开障碍物。因此，需要编队控制以实现机器人群的三个任务。在本文中，我们介绍了一种基于势场方法的机器人群分散编队控制算法。我们提出的编队控制算法可实现以下三个任务：避免环境中的障碍物；在机器人之间保持固定的距离以维护编队并执行分配的任务。人工神经网络参与了势力参数的在线优化。然后，进行实时实验以确认我们提出的分散式地层控制算法的可靠性和适用性。实时实验结果表明，所提出的分散编队控制算法能够在执行一定任务的同时，使蜂群避开障碍物并保持编队。群设法达到某个目标并跟踪给定的轨迹。此外，所提出的分散式编队控制算法使虫群能够脱离局部极小值，通过两个狭窄的障碍物而不会在它们附近振荡。通过将拟议的分散编队控制算法与传统PFM进行比较，我们获得了NN群成功达到其目标，平均精度为0.14 m，而T群则为0.22 m。机器人群之间的NN群也保持固定距离，群体误差较高，达到34.83％，而T群则达到23.59％。同样，NN群更精确地跟踪轨迹，与min相比，跟踪误差更高，达到0.0086 m。T群的误差等于0.01 m。此外，在障碍物数量不同的环境中，NN群的形成时间比T群的要长得多，而从执行时间开始，跟踪轨迹达到94.31％，而T群达到81.07％。