Communication-Aware Map Compression for Online Path-Planning: A Rate-Distortion Approach

2025-06-25

AI Summary

In bandwidth-constrained collaborative navigation of dual-robot systems (Seeker/Supporter) within unknown environments, real-time map communication suffers from severe bottlenecks—specifically, the Supporter must dynamically decide *when* to transmit, *which region* of its local map to transmit, and *at what resolution* to encode it, so as to maximize Seeker’s path planning performance under minimal bandwidth cost. Method: We propose a task-driven rate-distortion optimization compression framework. It introduces a closed-form reverse-waterfilling solution for content-aware, resolution-adaptive quantization; enforces binary codeword-length constraints; and integrates convex optimization with task-relevance-weighted quantization to enable Seeker-side autonomous inference of optimal compression policies—eliminating feedback communication. Results: Experiments under stringent bandwidth constraints demonstrate significant improvements in path planning success rate and efficiency, with low computational overhead and real-time online deployability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• 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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper addresses the problem of collaborative navigation in an unknown environment, where two robots, referred to in the sequel as the Seeker and the Supporter, traverse the space simultaneously. The Supporter assists the Seeker by transmitting a compressed representation of its local map under bandwidth constraints to support the Seeker's path-planning task. We introduce a bit-rate metric based on the expected binary codeword length to quantify communication cost. Using this metric, we formulate the compression design problem as a rate-distortion optimization problem that determines when to communicate, which regions of the map should be included in the compressed representation, and at what resolution (i.e., quantization level) they should be encoded. Our formulation allows different map regions to be encoded at varying quantization levels based on their relevance to the Seeker's path-planning task. We demonstrate that the resulting optimization problem is convex, and admits a closed-form solution known in the information theory literature as reverse water-filling, enabling efficient, low-computation, and real-time implementation. Additionally, we show that the Seeker can infer the compression decisions of the Supporter independently, requiring only the encoded map content and not the encoding policy itself to be transmitted, thereby reducing communication overhead. Simulation results indicate that our method effectively constructs compressed, task-relevant map representations, both in content and resolution, that guide the Seeker's planning decisions even under tight bandwidth limitations.

Problem

• Compress local map for collaborative robot navigation efficiently
• Optimize map transmission under bandwidth constraints using rate-distortion
• Enable real-time path-planning with task-relevant map compression

Innovation

• Rate-distortion optimization for map compression
• Variable quantization based on path-planning relevance
• Closed-form reverse water-filling solution enables real-time