MANET plays an irreplaceable task by providing quality aware broadcast routing technique for small infrastructure- less field of application. Depending upon arising interference, transmission power of individual node can be done by Ant Colony Optimization (ACO) algorithm. In addition, nodes probability link connectivity is estimated by using Bayesian pursuit (BPST) algorithm that determines probability to control adjustment in nodes power. In this paper, proposed to control topology with obstacle detection using RDPSO – GBA (Robotic Darwin Particle Swarm Optimization – Graph Based Algorithm) in mobile adhoc network. The robotics DPSO is to be represented with the help of the multiple swarms of various testing results when each swarm particularly operates an ordinary PSO method with various set of rules which are governing the gather of swarms. In the graph based algorithm, the nodes are to be presented in the MANET which has been separated into the two various classes that is leaders and the followers. The leader has the base node and the explorer node and the follower has the autonomous robots which provide the communication broadcasting. The simulation and result shows that there is the analysis of the various performances which is compared with conventional methods.

MANET通过为无基础设施的小型应用领域提供质量意识的广播路由技术，担负着不可替代的任务。根据产生的干扰，可以通过蚁群优化（ACO）算法来完成单个节点的传输功率。此外，使用贝叶斯追踪（BPST）算法估计节点概率链路连接性，该算法确定控制节点功率调整的概率。本文提出了在移动自组织网络中使用RDPSO – GBA（机器人达尔文粒子群优化–基于图的算法）来通过障碍物检测控制拓扑的方法。当每个群集特别操作具有多种规则来管理群集聚集的普通PSO方法时，将通过各种测试结果的多个群集来表示DPSO机器人。在基于图的算法中，节点将在MANET中呈现，该节点已分为领导者和跟随者两个不同的类。领导者具有基础节点和浏览器节点，跟随者具有提供通信广播的自主机器人。仿真和结果表明，与常规方法相比，可以对各种性能进行分析。