Communication-Aware Iterative Map Compression for Online Path-Planning

2025-03-13

AI Summary

This work addresses collaborative navigation for heterogeneous, resource-constrained robot teams operating in unknown environments, focusing on efficient map communication under severe bandwidth constraints. We propose a task-driven, communication-aware iterative compression framework: (i) a Kalman filter–based map estimation decoder for robust reconstruction; (ii) a lightweight communication constraint model; and (iii) a dynamically scalable set of compression templates to adapt to environmental complexity. Crucially, we introduce the first sequential compression selection strategy that jointly optimizes communication cost and navigation task performance. Evaluated on Mars slope terrain and real-world terrestrial maps, our method reduces communication volume by 98% while achieving path planning accuracy comparable to full-map transmission. Moreover, computational latency is significantly lower than that of state-of-the-art approaches, demonstrating both efficacy and efficiency for bandwidth-limited multi-robot systems.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• Optimizing communication in heterogeneous robot teams for navigation.
• Balancing map compression to reduce network load effectively.
• Enhancing computational speed and reducing communicated information.

Innovation

• Task-driven communication-aware map compression framework
• Iterative map estimation with Kalman filter noise handling
• 98% reduction in communicated information with similar planning costs