室外未知环境下的AGV地貌主动探索感知

AGV active landform exploration and perception in an unknown outdoor environment

智能机器人对复杂地貌环境的识别一直是机器人应用领域研究的前沿问题,移动机器人在不同的地貌上采取的运动方式并非一成不变,所以选择的运动方式对于迅速准确识别所处地貌的类型至关重要。针对该问题本文提出了一种基于贝叶斯框架的主动感知探索方法,使移动机器人能够主动探索有兴趣的运动方式并且感知识别和运动之间的匹配关系,可以优化在地貌识别之中的模糊不确定性;为了进一步验证实验的可靠性,还使用了被动感知策略来比较和分析不同策略之间的差异。实验结果表明:主动感知方法能够规划出有效的地貌识别动作序列,能够引导移动机器人主动感知目标地貌,该框架对于室外未知环境下主动感知后的地貌识别效果优于被动感知。

The recognition of a complex landform environment by an intelligent robot has been the frontier problem of research in the field of robotics applications.The motion modes adopted by mobile robots differ between landforms,so the selected motion mode is crucial for quickly and accurately identifying the type of landform.To solve this problem,an active perception exploration method is proposed in this paper based on a Bayesian framework.It enables mobile robots to actively explore interesting motion modes and recognize the matching relationship between landform and movement.It can optimize the fuzzy uncertainty in landform recognition.To further verify the reliability of the experiment,we also use a passive recognition strategy to compare and analyze the differences between different strategies.The experimental results show that the active perception method can plan effective landform recognition action sequences and guide mobile robots to actively perceive the target landform.The landform recognition effect of active perception is better than that of passive perception in an unknown outdoor environment.