Communication-Aware Map Compression for Online Path-Planning

TL;DR

This paper introduces a communication-aware map compression framework for mobile robot path-planning that reduces transmitted data by about 50% without sacrificing performance, enabling efficient collaboration in unknown environments.

Contribution

The paper presents a novel sequential compression selection method guided by robot paths, with a new decoder and encoder design that optimize environment estimation and communication efficiency.

Findings

Reduced communication load by approximately 50% in simulations.

Effective environment estimation using convex optimization with compressed data.

Framework performs well in large and maze-like maps.

Abstract

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.