基于粒子群的多毫米波安防机器人环境感知方法

安防机器人常工作于昏暗、烟雾等环境,毫米波有探测这类环境的能力,但其点云是稀疏的,可将多毫米波的点云融合 以提高环境感知的能力。 点云融合时需要精确的结构参数,针对测量法获取结构参数存在误差的问题,在分析多毫米波点云坐 标的基础上,利用粒子群算法对毫米波雷达结构参数进行搜索,并根据搜索结果进行点云融合以及环境地图的构建;同时提出 稀疏点云地图的评价指标,对毫米波感知效果进行定量评价。 利用安防机器人在昏暗环境下开展实验,结果表明与结构参数由 测量法获取的多毫米波感知系统对比,点云数量有所增加,地图边界空洞数量平均减少 55% ,边界噪声率平均下降 12. 9% ,物 体点云离散度平均下降约 0. 06,中心位置的偏移量均有所减小。

Environment perception method based on PSO for Multi-millimeter wave security robot

Security robots frequently operate in the severe environments with dim and smoke, etc. Such an environment can be detected using millimeter wave, but its point cloud is sparse. Correspondingly the multi-millimeter waves point clouds can be fused to improve the ability to perceive environment. Accurate structural parameters are required for point cloud fusion to address the error in obtaining structural parameters through measurement. By analyzing the coordinates of multi-millimeter wave point cloud, particle swarm algorithm is utilized to search the structural parameters of millimeter wave radar, and the point cloud fusion as well as the construction of environment map are carried out according to the search results. Simultaneously, the evaluation critical of sparse point cloud map is proposed to quantitatively assess the millimeter wave sensing effect. Experiments were carried out in a darkened environment with a security robot, the results of which are as follows. The number of point clouds increases. The number of map boundary holes decreases by 55% on average. The boundary noise rate is reduced by 12. 9% on average. The dispersion of the object point clouds decreases by about 0. 06 on average. There is a decrease in the offsets of center positions in all experiments, when compared to the multi-millimeter-wave perception system where the structural parameters were obtained by the measurement method.