Hangzhou Dianzi University
在基于概率地图的移动机器人目标搜索规划中, 目标在工作环境中的存在概率通常被设置服从离散均匀分布, 进而采用路径长度指标优化搜索任务的全局路径。然而，真实工作空间中的概率分布绝大多数并不服从均匀分布，这将导致所获搜索策略并非预期的最短时间。针对该问题，本文根据实际工作环境构建了概率测算模型，并基于该模型构建概率地图，进而提出了一种以预期最短时间为优化指标的机器人目标搜索路径规划方法。该方法采用了分层规划模式，在上层拓扑地图中进行拓扑点序列规划，而在下层特征地图中进行拓扑点间局部路径规划。实验结果表明该方法可显著缩短移动机器人目标搜索的期望时间, 更适用于目标不服从均匀分布的工作环境.
In the path planning of target search based on probabilistic maps, the target is usually set to obey discrete uniform distribution in the working space, and then the length of the path is employed as an index to optimize the global path of the search task. However, most of the probability distributions of targets in real working environment do not satisfy uniform distribution, which will lead to the search path not being the one with the shortest expected time. In order to solve this problem, a probability calculation model is designed according to actual working environments, and based on which, a probability map is designed. With the probability map, an expected-time optimal target search path planning method for robots is proposed, in which, a sequence planning method is used in the upper topology map to obtain the observation point search sequence of the optimal expected time. Finally, a local path planning method is used in the lower feature map to obtain the collision-free path between the observation points. The experimental results show that this method can significantly shorten the expected time of target search, and is more suitable for working situation where the target does not obey uniform distribution.