Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach

TL;DR

This paper introduces a novel autonomous navigation scheme for robots that enhances data collection efficiency in edge intelligence, especially in complex, non-line-of-sight environments, by jointly optimizing navigation, communication, and learning.

Contribution

The paper proposes a communication-and-learning dual-driven scheme with an efficient algorithm to improve robot-assisted data collection in challenging environments.

Findings

Superior collision-avoidance navigation performance

Enhanced data collection and model training efficiency

Flexible adaptation to different scenarios

Abstract

With the growing demand for large-scale and high-quality data in edge intelligence systems, mobile robots are increasingly deployed to collect data proactively, particularly in complex environments. However, existing robot-assisted data collection methods face significant challenges in achieving reliable and efficient performance, especially in non-line-of-sight (NLoS) environments. This paper proposes a communication-and-learning dual-driven (CLD) autonomous navigation scheme that incorporates region-aware propagation characteristics and a non-point-mass robot representation. This scheme enables simultaneous optimization of navigation, communication, and learning performance. An efficient algorithm based on majorization-minimization (MM) is proposed to solve the non-convex and non-smooth CLD problem. Simulation results demonstrate that the proposed scheme achieves superior performance in collision-avoidance navigation, data collection, and model training compared to benchmark methods. It is also shown that CLD can adapt to different scenarios by flexibly adjusting the weight factor among navigation, communication and learning objectives.