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

机译:相对定位机制条件下的目标位置估计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:以群智能为搜索目标的群机器人以协调的方式进行控制,机器人自身的常识和小组经验指导着他的行为。本质上,小组经验是所有机器人自己的常识中最好的一种。为了提高搜索目标的速度,群体机器人在相对定位机制下将估计的目标位置值相加,以充分发挥群体决策的优势。首先,群机器人可以将扩展粒子群优化(PSO)模型作为受控工具。然后,基于群机器人与无线传感器网络之间的相似性,以及群机器人通过接收信号强度指示器(RSSI)估算目标位置的值,群机器人将他们的经验和团队决策结合起来以寻找目标。当机器人可以估计目标位置时,会将目标位置的值引入扩展的PSO模型中;否则,它将使用原始模型并交替控制机器人。实验结果表明,新模型在成功率,目标搜索步骤和能耗等方面均优于旧模型。

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