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Ant-based distributed protocol for coordination of a swarm of robots in demining mission

机译:基于蚂蚁的分布式协议,用于排雷任务中机器人群的协调

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Coordination among multiple robots has been extensively studied, since a number of practical real problem s can be performed using an effective approach. In this paper is investigated a collective task that requires a multi-robot system to search for randomly distributed mines in an unknown environment and disarm them cooperatively. The communication among the swarm of robots influences the overall performance in terms of time to execute the task or consumed energy. To address this problem, a new distributed recruiting protocol to coordinate a swarm of robots in demining mission, is described. This problem is a multi-objective problem and two bio inspired strategies are used. The novelty of this approach lies in the combination of direct and indirect communication: on one hand an indirect communication among robots is used for the exploration of the environment, on the other hand a novel protocol is used to accomplish the recruiting and coordination of the robots for demining task. This protocol attempts to tackle the question of how autonomous robot can coordinate themselves into an unknown environment relying on simple low-level capabilities. The strategy is able to adapt the current system dynamics if the number of robots or the environment structure or both change. The proposed approach has been implemented and has been evaluated in several simulated environments. We analyzed the impact of our approach in the overall performance of a robot team. Experimental results indicated the effectiveness and efficiency of the proposed protocol to spread the robots in the environment.
机译:由于可以使用有效的方法来执行许多实际的实际问题,因此已经广泛研究了多个机器人之间的协调。本文研究了一项集体任务,该任务需要一个多机器人系统来搜索未知环境中的随机分布的地雷,并协同解除它们的武装。机器人群之间的通信会影响执行任务或消耗能量的时间方面的整体性能。为了解决这个问题,描述了一种新的分布式募集协议,以协调扫雷任务中的机器人群。这个问题是一个多目标问题,使用了两种生物启发策略。这种方法的新颖之处在于直接和间接通信的结合:一方面,机器人之间的间接通信用于探索环境,另一方面,一种新颖的协议用于完成机器人的募集和协调用于排雷任务。该协议试图解决自主机器人如何依靠简单的低级功能将自己协调到未知环境中的问题。如果机器人的数量或环境结构或两者都发生变化,则该策略能够适应当前的系统动态。所提出的方法已经实现,并已在几种模拟环境中进行了评估。我们分析了我们的方法对机器人团队整体绩效的影响。实验结果表明了该协议在环境中传播机器人的有效性和效率。

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