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首页> 外文会议>OCEANS 2017 - Aberdeen >Pattern formation for a fleet of AUVs based on optical sensor
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Pattern formation for a fleet of AUVs based on optical sensor

机译：基于光学传感器的AUV阵列的图案形成

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During the past decades, multi-robot systems have been studied deeply and have demonstrated their advantages in conducting autonomous missions, particularly in underwater environment. When using multiple robots, high cost single sensor can be replaced by many coordinated low cost sensors, such as optical sensors used in our work for acquiring relevant data and coordinating the fleet of robots. In this paper, we propose a coordination algorithm for pattern formation (the shape of the fleet), which includes a new predefined pyramid pattern independent on the number of robots and an associated formation control strategy for building and maintaining this pyramid without collision among robots and without external assistant in non-obstacle situation. The formation control strategy consists of three steps: (1) a common frame is built after the exchange of local information extracted from visual servoing (VS) and compass of each robot; (2) a pyramid frame is established from the distribution of robots, and whose orientation is defined by one of the two following methods: one based on principal component analysis (PCA) and the other one based on an intermediate circle pattern (CP); (3) the kernel part is the collision avoidance strategy (CA) which is realized by optimally matching up positions in the distribution and the pyramid, and planning the straight non-intercrossed trajectories. At last, robots build the pyramid along the straight trajectory. During the whole process, except the information exchange for establishing the common frame, robots do not need communication as they have the same algorithms. Until now the performance of the formation control strategy is demonstrated with 4 to 6 robots in Blender based on dynamic equations of real underwater robots.
机译：在过去的几十年中，对多机器人系统进行了深入研究，并证明了它们在执行自主任务（尤其是在水下环境）中的优势。当使用多个机器人时，高成本的单个传感器可以由许多协调的低成本传感器代替，例如在我们的工作中用于获取相关数据和协调机器人机群的光学传感器。在本文中，我们提出了一种用于模式形成（车队形状）的协调算法，该算法包括一个独立于机器人数量的新预定义金字塔图案，以及一种相关的形成控制策略，用于构建和维护该金字塔而不会在机器人之间碰撞。在没有障碍的情况下无需外部助手。编队控制策略包括三个步骤：（1）在交换从每个机器人的视觉伺服（VS）和罗盘中提取的局部信息后，建立一个公共框架； （2）根据机器人的分布情况建立金字塔框架，并通过以下两种方法之一定义其方向：一种基于主成分分析（PCA），另一种基于中间圆圈图案（CP）； （3）核心部分是防撞策略（CA），它是通过最佳地匹配分布和金字塔中的位置并规划直线非交叉轨迹来实现的。最后，机器人沿着直线轨迹建立金字塔。在整个过程中，除了用于建立公共框架的信息交换外，机器人不需要通信，因为它们具有相同的算法。到目前为止，在Blender中有4到6个机器人根据真实水下机器人的动力学方程式演示了编队控制策略的性能。

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