| Swarm robotics was one kind of Multi-robot system, it was Tableed by anumber of simple robots which had a limited ability to perceive the environment.In this system, all robots’ structures and functional roles were isomorphic. Bythe local interaction between robot with robot and environment, under the selforganization mechanism, the system emerged intelligent behavior, it cancomplete the task that single robot can not complete or was at low executionefficiency.In the system of swarm robotics, collaboration was essential. Theresearches on collaborative mainly were the following several aspects: swarmsystem structure, learning, conflict resolution. Results had general significance,these studies were divided into several benchmark problems, such as handling,Tableation, searching, hunting. Above these problems, the searching problemhad a very important position. Compared to other benchmark problems, targetsearching problem was more basic, it is other tasks’ premise, such as hunting,handling.Each member of swarm robots was guided by its own cognition and socialexperience to move for swarm robotic search, following the extended particleswarm optimization (EPSO) model. For search efficiency enhancement usinggroup decision, target position estimation with multiple robots under globallocalization and relative localization framework were introduced into theexisting model based on mapping between swarm robots and wireless sensornetwork (WSN). When the relative position relations of robots were met toestimate target position with Received Signal Strength Indicator (RSSI) method,EPSO model can be modified by substituting estimation for social experience;otherwise, it remained. Alternatively, robots were controlled with the twomodels to search．Under global localization and relative localization framework, results fromsimulations show that our method dominates over pure EPSO in aspects ofsuccess rate, search efficiency and energy consumption when swarm size is big enough. |
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