uncertain dynamic environments 不确定的动态环境
This thesis researches on mainly two aspects, obstacle-avoiding of mobile robot in real-time and the kalman filtering prediction of the state of obstacles in dynamic uncertain environments.
本文的研究工作主要是针对动态不确定环境下，移动机器人的实时动态避障和对障碍物状态信息的卡尔曼滤波预测两个方面的研究展开的。
To overcome the drawbacks of the conventional artificial potential fields in motion planning of mobile robots for dynamic uncertain environments, an artificial coordinating field (ACF) is proposed.
为克服传统人工势场在动态未知环境下机器人避碰规划中存在的缺陷，提出人工协调场法(acf)。
Organic organizations are best matched with dynamic and uncertain environments.
而有机式组织在动态的、不确定的环境下最有效。
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