不同探测距离传感器的搜索策略研究

提出了一种针对侦察任务隐蔽点的搜索策略,主要应用于装备了不同探测距离传感器的地面无人机动平台,解决了已有随机搜索策略效率不高的问题. 该策略使用基于传感器的导向快速随机搜索树方法进行目标方向的路径规划;在路径规划的过程中若遇到障碍物形成的狭窄通道时,基于启发式A*方法规划路径以提高效率,最终完成搜索策略. 将搜索策略命名为:组合传感器与规划组合搜索策略. 设计仿真试验对所提出的搜索策略进行了验证. 结果表明在直径500米有遮挡的仿真场景下,所提出的搜索策略相比于短探测距离传感器的路径变形策略快速随机搜索树策略效率平均提升了3.11倍,规划的道路长度缩短了9.63%,所提出的搜索策略相比于长探测距离传感器导向搜索策略效率平均提升了3.53倍,规划的道路长度缩短了12.06%,证明了CP&CS搜索策略在侦察任务中隐蔽点搜索上的优越性. 

Search Strategy Based on Sensors with Different Detection Distances

In order to improve the efficiency of existing random search methods, a search strategy was proposed to reconnoiter a hidden-object of the reconnaissance mission. The strategy, called combined planning path and combined sensor search method (CP&CS), was designed mainly to be applied to unmanned ground vehicles equipped with different detection range sensors. In CP&CS, a sensor-based goaled rapidly-exploring random tree was arranged to plan the global path towards the hidden object. Besides, a heuristic A* method was utilized to deal with the narrow channel formed by obstacles. A simulation experiment was designed to validate the proposed strategy. The results show that in a simulation environment with 250 meter radius and occlusions, compared with the path deformation strategy with short-range sensors, the CP&CS method can improve the search efficiency by 3.11 times and shortens the length of planned path by 9.63%. Compared with goaled RRT search strategy with long-range sensors, the CP&CS method can improve the search efficiency by 3.53 times and shortens the length of planned path by 12.06%. The experimental results prove the superiority of the proposed CP&CS strategy in hidden-object search of reconnaissance mission.