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Target Position Estimation Aided Swarm Robotic Search under Conditions of Relative Localization Mechanism

机译:目标位置估计在相对定位机制的条件下,辅助群体机器人搜索

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Swarm robotic which search target with swarm intelligence are controlled in a coordinated way, the robot's own common knowledge and his group experience guide his behaviors. In essence, the group experience is the best one in the group of all robots' own common knowledge. To improve the speed of searching target, swarm robotic add the estimated the value of target's position under the relative localization mechanism, in order to give full play to the advantages of group decision. First, swarm robotic can take the model of extended particle swarm optimization (PSO) as a controlled tool. Then, based on the similarity between swarm robotic and wireless sensor networks and the nature of swarm robotic estimated the value of target's position with Received Signal Strength Indicator (RSSI), swarm robotic combine their experience and group decision-making to searching the target. When the robot can estimate the target position, the value of target's position will be introduced into the extended PSO model, Otherwise, it use the original model and they control the robots alternatingly. The experiment result proves that the new model is better than the old one among the success rates, the steps for searching target and energy consumption.
机译:群体机器人以群体智能搜索到群体,以协调的方式控制,机器人自己的共同知识和他的团体经验指导他的行为。实质上,小组经验是所有机器人自己的常识中最好的。为了提高搜索目标的速度,群体机器人在相对定位机制下增加了目标位置的估计值,以便充分发挥团队决策的优势。首先,群体机器人可以将扩展粒子群优化(PSO)的模型作为控制工具。然后,基于群体机器人和无线传感器网络之间的相似性以及群体机器人的性质估计了目标信号强度指示器(RSSI)的目标位置,群体机器人将其经验和群体决策结合起来以搜索目标。当机器人可以估计目标位置时,将引入目标位置的值将被引入扩展的PSO模型,否则,它使用原始模型,并且它们交替控制机器人。实验结果证明,新模型比成功率的旧模型更好,搜索目标和能源消耗的步骤。

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