基于增强转移网络（ATN）的室外移动机器人道路图像理解

道路图像理解是室外移动机器人视觉导航自主驾驶研究中的一个关键技术 ,由于基于视觉导航的室外移动机器人自主驾驶时 ,对实时性和鲁棒性要求很高 ,因此 ,为了满足室外移动机器人自主驾驶的实时性和鲁棒性要求 ,将人工智能研究句法分析中的一个形式体系——增强转移网络 (ATN )成功地应用于室外移动机器人的道路理解中 ,进而提出了基于 ATN的室外移动机器人道路图像理解算法 ,该算法在统一的 ATN构建思想指导下 ,针对不同的道路情况 ,不仅可以灵活地构建出不同的道理理解 ATN网络 ,还可达到本质上的统一及应用上的灵活。经实验检验 ,该算法在满足系统要求的鲁棒性条件下 ,具有非常高的实时性 ,即能充分地满足自主移动机器人高速自主导航的需要

Road Image Recognition Based on ATN for Outdoor Mobile Robot

Road image recognition is a key technique for vision navigation of outdoor mobile robot. Because vision navigation of outdoor mobile robot requires good robustness and rigid time limitation for real time autonomous driving, in order to satisfy the request of good robustness and rigid time limitation, in this paper, we applied successfully Augmented Transition Networks (ATN), which is a kind of method used for sentence parsing in the artificial intelligence field, to the road image recognition of outdoor mobile robot, and proposed a road image interpretation algorithm based on the ATN. The characteristic of the ATN is, aiming at the different road condition, the algorithm can make up of different ATN, but all the ATN are constructed by the uniform construction rules. Therefore, the construction of the ATN is very flexible in application, and the essence of the ATN is coherent yet. The algorithm has good robustness and real time performance, and has been verified by the experiment that it can meet the needs for high speed autonomous vision navigation of outdoor mobile robot.