Obstacle avoidance of autonomous mobile robots

C. Wu
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Abstract

Robots have been widely used in industry to replace human being for repeated and dangerous works. Typically, the robot is used to pick and place workpieces or to transport material in a way of unmanned vehicle. This gives rise to the issue of obstacle avoidance. There are model-based and sensor-based methods for this purpose. In a model-based system, one uses artificial intelligence search algorithms to find free path in the working environment. In a sensor-based system, the sensed data is recorded in a grid map. Obstacles are represented by cells in the map. The value in the cell determines whether there exists an obstacle or not. We use the ultrasonic sensors to detect obstacles. The sensors are installed every thirty degrees at the center of robot. The surrounding information can be obtained at once. However, the poor range capability of the sensor and the shape of the obstacle may influence the performance of obstacle avoidance, we also use two CCD cameras to identify and locate target, obstacles. The pattern recognition is essentially done by the calculation. Of the first and second moment of the image. The obstacle is located by the comparison of the above calculations from two cameras. The detailed methods and experimental results will be presented with video tape.
自主移动机器人的避障研究
机器人已被广泛应用于工业,以取代人类从事重复和危险的工作。通常,机器人被用于取放工件或以无人驾驶车辆的方式运输材料。这就产生了避障问题。为此,有基于模型和基于传感器的方法。在基于模型的系统中,人们使用人工智能搜索算法来寻找工作环境中的自由路径。在基于传感器的系统中,感知到的数据被记录在网格图中。障碍物在地图上用单元格表示。单元格中的值确定是否存在障碍物。我们用超声波传感器探测障碍物。传感器每隔30度安装在机器人的中心。可以立即获得周围的信息。然而,传感器的测距能力差和障碍物的形状可能会影响避障性能,我们也使用两个CCD相机来识别和定位目标、障碍物。模式识别基本上是通过计算来完成的。图像的第一和第二时刻。通过比较两个摄像机的上述计算结果来定位障碍物。详细的方法和实验结果将以录像带的形式呈现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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