Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach
Authors: Tingting Huang, Yingyang Chen, Sixian Qin, Zhijian Lin, Jun Li, Li Wang
Organizations: College of Information Science and Technology, Jinan University, Guangzhou 510632, China · College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China · School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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.
Figures & tables
Fig. 1 : An edge intelligence scenario within an industrial factory, where multiple sensor devices and obstacles exist. One intelligent robot is designed to autonomously collect distributed data and transmit it to the edge server for model training upon arrival at the target location.
Fig. 2 : Autonomous navigation trajectories under different schemes.
Fig. 3 : The data amount collected by the robot under different schemes
Fig. 4 : Model classification errors under different schemes
Institute for Experiential Robotics, Northeastern University, Boston, MA. · Khoury College of Computer Science, Northeastern University, Seattle, WA, USA.
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan · Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan