Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach
It addresses data collection bottlenecks for edge intelligence systems in complex environments, representing an incremental improvement over existing robot-assisted methods.
This paper tackles the challenge of reliable and efficient robot-assisted data collection in non-line-of-sight environments for edge intelligence by proposing a communication-and-learning dual-driven autonomous navigation scheme, which simulation results show achieves superior performance in collision avoidance, data collection, and model training compared to benchmarks.
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.