This paper addresses the problem of optimizing communicated information among heterogeneous, resource-aware robot teams to facilitate their navigation. In such operations, a mobile robot compresses its local map to assist another robot in reaching a target within an uncharted environment. The primary challenge lies in ensuring that the map compression step balances network load while transmitting only the most essential information for effective navigation. We propose a communication framework that sequentially selects the optimal map compression in a task-driven, communication-aware manner. It introduces a decoder capable of iterative map estimation, handling noise through Kalman filter techniques. The computational speed of our decoder allows for a larger compression template set compared to previous methods, and enables applications in more challenging environments. Specifically, our simulations demonstrate a remarkable 98% reduction in communicated information, compared to a framework that transmits the raw data, on a large Mars inclination map and an Earth map, all while maintaining similar planning costs. Furthermore, our method significantly reduces computational time compared to the state-of-the-art approach.

翻译：本文研究了异构资源感知机器人团队间通信信息的优化问题，以促进其导航任务。在此类操作中，一个移动机器人压缩其局部地图，以协助另一机器人在未知环境中抵达目标。核心挑战在于确保地图压缩步骤能够在平衡网络负载的同时，仅传输对有效导航最为关键的信息。我们提出了一种通信框架，该框架以任务驱动、通信感知的方式顺序选择最优的地图压缩方案。该框架引入了一个能够进行迭代地图估计的解码器，并通过卡尔曼滤波技术处理噪声。我们解码器的计算速度使其能够使用比先前方法更大的压缩模板集，并适用于更具挑战性的环境。具体而言，我们的仿真实验表明，在大型火星地形图和地球地图上，与传输原始数据的框架相比，我们的方法在保持相近规划成本的同时，实现了高达98%的通信信息量削减。此外，与现有最先进方法相比，我们的方法显著减少了计算时间。