通过迭代压缩地图，让机器人在通信受限时仍能高效导航。
Communication-Aware Iterative Map Compression for Online Path-Planning
- 逐轮选择最优压缩方案，兼顾通信与导航需求
- 通信量减少98%，规划成本基本不变
- 适合资源受限的异构机器人团队协作
本文针对异构、资源感知型机器人团队在未知环境中协同导航时的通信优化问题，提出一种任务驱动、通信感知的迭代地图压缩框架。移动机器人将本地地图压缩后传输给另一机器人以实现目标定位。核心挑战在于压缩过程需平衡网络负载并仅传递对导航至关重要的信息。所提方法引入可迭代估计地图的解码器，利用卡尔曼滤波处理噪声，计算速度更快，支持更大压缩模板集，适用于更复杂环境。仿真结果显示，在火星倾斜地形图和地球地图上，相比直接传输原始数据，通信量降低98%，同时保持相近的路径规划开销；相较当前最优方法，计算时间显著减少。
原文摘要 · Abstract (English)
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.
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