This paper addresses the problem of the communication of optimally compressed information for mobile robot path-planning. In this context, mobile robots compress their current local maps to assist another robot in reaching a target in an unknown environment. We propose a framework that sequentially selects the optimal compression, guided by the robot's path, by balancing the map resolution and communication cost. Our approach is tractable in close-to-real scenarios and does not necessitate prior environment knowledge. We design a novel decoder that leverages compressed information to estimate the unknown environment via convex optimization with linear constraints and an encoder that utilizes the decoder to select the optimal compression. Numerical simulations are conducted in a large close-to-real map and a maze map and compared with two alternative approaches. The results confirm the effectiveness of our framework in assisting the robot reach its target by reducing transmitted information, on average, by approximately 50% while maintaining satisfactory performance.

翻译：本文针对移动机器人路径规划中最优压缩信息的通讯问题展开研究。在此背景下，移动机器人压缩其当前局部地图，以协助另一机器人在未知环境中抵达目标。我们提出了一种框架，通过平衡地图分辨率与通讯成本，沿机器人路径依次选择最优压缩策略。该方法在接近真实场景中具有可解性，且无需预先掌握环境知识。我们设计了一种新型解码器，利用压缩信息通过带线性约束的凸优化估计未知环境，同时设计了一种编码器，通过该解码器选择最优压缩方案。在大型接近真实地图与迷宫地图上进行的数值仿真，并与两种替代方法进行了对比。结果表明，该框架能有效协助机器人抵达目标，平均减少约50%的传输信息量，同时保持令人满意的性能。