Purpose

Heterogeneous teams consisting of unmanned ground vehicles and unmanned aerial vehicles are being used for different types of missions such as surveillance, tracking and exploration. Exploration missions with heterogeneous robot teams (HeRTs) should acquire a common map for understanding the surroundings better. The purpose of this paper is to provide a unique approach with cooperative use of agents that provides a well-detailed observation over the environment where challenging details and complex structures are involved. Also, this method is suitable for real-time applications and autonomous path planning for exploration.

Design/methodology/approach

Lidar odometry and mapping and various similarity metrics such as Shannon entropy, Kullback–Leibler divergence, Jeffrey divergence, K divergence, Topsoe divergence, Jensen–Shannon divergence and Jensen divergence are used to construct a common height map of the environment. Furthermore, the authors presented the layering method that provides more accuracy and a better understanding of the common map.

Findings

In summary, with the experiments, the authors observed features located beneath the trees or the roofed top areas and above them without any need for global positioning system signal. Additionally, a more effective common map that enables planning trajectories for both vehicles is obtained with the determined similarity metric and the layering method.

Originality/value

In this study, the authors present a unique solution that implements various entropy-based similarity metrics with the aim of constructing common maps of the environment with HeRTs. To create common maps, Shannon entropy–based similarity metrics can be used, as it is the only one that holds the chain rule of conditional probability precisely. Seven distinct similarity metrics are compared, and the most effective one is chosen for getting a more comprehensive and valid common map. Moreover, different from all the studies in literature, the layering method is used to compute the similarities of each local map obtained by a HeRT. This method also provides the accuracy of the merged common map, as robots’ sight of view prevents the same observations of the environment in features such as a roofed area or trees. This novel approach can also be used in global positioning system-denied and closed environments. The results are verified with experiments.

中文翻译：

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使用各种基于熵的相似性度量构建通用的高度图，并为异构机器人团队利用分层方法

目的

由无人地面飞行器和无人飞行器组成的异构团队被用于不同类型的任务，例如监视，跟踪和探索。异构机器人团队（HeRT）的探索任务应获取一张通用地图，以更好地了解周围的环境。本文的目的是提供一种与代理商合作使用的独特方法，该方法可在涉及挑战性细节和复杂结构的环境中提供详细的观察。而且，此方法适用于实时应用和用于探索的自主路径规划。

设计/方法/方法

激光雷达里程计和制图以及各种相似性度量（例如，香农熵，Kullback-Leibler散度，Jeffrey散度，K散度，Topsoe散度，Jensen-Shannon散度和Jensen散度）用于构建环境的通用高度图。此外，作者提出了一种分层方法，该方法可以提供更高的准确性和对通用地图的更好理解。

发现

总而言之，通过实验，作者观察到位于树木或屋顶顶部区域及其下方的特征，而无需全球定位系统信号。此外，使用确定的相似性度量和分层方法，可以获得更有效的通用地图，该地图可以为两个车辆规划轨迹。

创意/价值

在这项研究中，作者提出了一种独特的解决方案，该解决方案实现了各种基于熵的相似性度量，目的是使用HeRT构建环境的通用图。要创建通用地图，可以使用基于Shannon熵的相似性度量，因为它是唯一精确把握条件概率链规则的度量。比较了七个不同的相似性指标，并选择了最有效的一个以获取更全面和有效的通用地图。此外，与文献中的所有研究不同，分层方法用于计算通过HeRT获得的每个局部地图的相似度。这种方法还可以提供合并后的普通地图的准确性，因为机器人的视线会阻止对屋顶或树木等地物进行相同的环境观察。这种新颖的方法也可以在全球定位系统被拒绝和封闭的环境中使用。实验验证了结果。