Bioinspired event-driven collision avoidance algorithm based on optic flow
Bioinspired event-driven collision avoidance algorithm based on optic flow
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DOI：
10.1109/ebccsp.2015.7300673
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Moritz B. Milde;O. Bertrand;R. Benosman;M. Egelhaaf;E. Chicca
中科院分区：
文献类型：
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作者：
Moritz B. Milde;O. Bertrand;R. Benosman;M. Egelhaaf;E. Chicca
任何移动的代理，无论是生物还是机器人，都需要避免与障碍物碰撞。昆虫，如蜜蜂和苍蝇，使用光流来估计相对接近障碍物。由自我运动引起的光流由平移和旋转分量组成。两种成分的分离在计算上是昂贵的，因此在能量上是昂贵的。苍蝇和蜜蜂通过行为，即通过采用飞行和凝视控制的扫视策略，主动地分离旋转和平移光流分量。虽然机器人系统能够模仿这种凝视策略，但从标准相机图像计算光流场仍然是耗时和耗能的。为了克服这个问题，我们使用了动态视觉传感器（DVS），它提供了基于事件的信息，随着时间的推移，在每个像素位置的对比度变化。为了从这些信息中提取光流，使用平面拟合算法估计小时空长方体中的相对速度。利用视网膜的局部特性，从平移光流导出深度结构。然后，根据环境的基于事件的深度结构来计算碰撞避免方向。该系统已成功地在开环机器人平台上进行了测试。
Any mobile agent, whether biological or robotic, needs to avoid collisions with obstacles. Insects, such as bees and flies, use optic flow to estimate the relative nearness to obstacles. Optic flow induced by ego-motion is composed of a translational and a rotational component. The segregation of both components is computationally and thus energetically expensive. Flies and bees actively separate the rotational and translational optic flow components via behaviour, i.e. by employing a saccadic strategy of flight and gaze control. Although robotic systems are able to mimic this gaze-strategy, the calculation of optic-flow fields from standard camera images remains time and energy consuming. To overcome this problem, we use a dynamic vision sensor (DVS), which provides event-based information about changes in contrast over time at each pixel location. To extract optic flow from this information, a plane-fitting algorithm estimating the relative velocity in a small spatio-temporal cuboid is used. The depth-structure is derived from the translational optic flow by using local properties of the retina. A collision avoidance direction is then computed from the event-based depth-structure of the environment. The system has successfully been tested on a robotic platform in open loop.