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Cooperative Search by Combining Simulated and Real Robots in a Swarm under the View of Multibody System Dynamics

机译:多体系统动力学视角下的群内模拟机器人与真实机器人相结合的协同搜索

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摘要

This paper presents a new approach for cooperative search of a robot swarm. After modeling the robot, the mechanical Particle Swarm Optimization method is conducted based on physical robot properties. Benefiting from the effective localization and navigation by sensor data fusion, a mixed robot swarm which contains both simulated and real robots is then successfully used for searching a target cooperatively. With the promising results from experiments based on different scenarios, the feasibility, the interaction of real and simulated robots, the fault tolerance, and also the scalability of the proposed method are investigated.
机译:本文提出了一种新的机器人群体协同搜索方法。在对机器人建模之后,根据机器人的物理属性进行机械粒子群优化方法。得益于传感器数据融合的有效定位和导航功能,包含模拟机器人和真实机器人的混合机器人群随后成功地用于协作搜索目标。利用基于不同场景的实验的有希望的结果,研究了该方法的可行性,真实和模拟机器人的交互作用,容错能力以及可扩展性。

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