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首页> 外文期刊>IEEE Transactions on Robotics >Bioinspired Approaches for Autonomous Small-Object Detection and Avoidance
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Bioinspired Approaches for Autonomous Small-Object Detection and Avoidance

机译：生物启发的自主小物体检测和避免方法

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Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement-based solutions suffer either from restricted fields-of-view or significant computational complexity and are, hence, not easily portable to small platforms. In order to overcome these drawbacks, in this paper, two novel bioinspired approaches are proposed to extract information about small-field objects contained in planar optic flow. The first approach, which is analogous to the small-field extraction process hypothesized to occur in the lobula plate of the fly visual system, is based on the Fourier residual analysis of instantaneous optic flow. Alternatively, the flow-of-flow method is the engineering analogue of the small-field extraction process thought to occur in the fruit-fly's medulla, and extracts high-frequency content of optic flow by means of an elementary motion detector array. Both approaches extract instantaneous relative range and bearing of small-field obstacles from planar optic flow in a local environment characterized by small and wide-field obstacles, which is then combined with an artificial potential function-based low-order steering control law. The proposed sensing and control scheme is experimentally validated with a quadrotor vehicle that is able to effectively navigate an unknown environment laden with small-field clutter. This bioinspired approach is computationally efficient, which renders extraction of vehicle velocity and local environment structure superfluous, and thus, serves as a robust, reflexive solution to the problem of small-object detection, and avoidance for small autonomous robots.
机译：对于通常高度敏捷且有效载荷和计算处理能力受到限制的小型自动驾驶汽车，未知环境中的小物体检测和避免是一项重大挑战。典型的基于机器视觉和范围测量的解决方案要么视野受限，要么计算量大，因此难以移植到小型平台上。为了克服这些缺点，在本文中，提出了两种新颖的生物启发方法来提取有关平面光学流中包含的小视场对象的信息。第一种方法类似于假设在飞行视觉系统的小叶板中发生的小视场提取过程，是基于瞬时光流的傅立叶残差分析。另外，流量法是认为在果蝇的延髓中发生的小视场提取过程的工程模拟，并通过基本运动检测器阵列提取光流的高频成分。两种方法都可以从以小范围和宽范围障碍物为特征的局部环境中的平面光学流中提取小范围障碍物的瞬时相对范围和方位，然后将其与基于人工势函数的低阶转向控制定律相结合。所提出的传感和控制方案已通过四旋翼飞行器进行了实验验证，该四旋翼飞行器能够有效地导航充满小场杂波的未知环境。这种受生物启发的方法在计算上是有效的，这使提取车速和本地环境结构变得多余，因此，它是解决小物体检测问题以及避免使用小型自主机器人的可靠且自反的解决方案。

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