用机器人自适应采集数据，兼顾导航、通信和学习效率。
Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach
- 结合环境传播特性和非点质量机器人模型，统一优化三类性能。
- 仿真显示在避障、采样和训练上均优于基准方法。
- 可灵活调节权重，适配不同应用场景需求。
随着边缘智能系统对大规模高质量数据的需求增长，移动机器人被越来越多地用于主动采集数据，尤其是在复杂环境中。然而，现有机器人辅助数据采集方法在非视距（NLoS）环境下难以实现可靠高效的性能。本文提出一种通信与学习双驱动（CLD）的自主导航方案，融合区域感知传播特性与非点质量机器人表示，实现导航、通信与学习性能的同步优化。针对该非凸、非光滑的CLD问题，设计基于分治-最小化（MM）的高效算法求解。仿真结果表明，所提方案在避障导航、数据采集和模型训练方面均显著优于基准方法。同时，通过灵活调整导航、通信与学习目标间的权重因子，CLD方案能适应不同场景需求。
原文摘要 · Abstract (English)
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.
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