[Paper Review] Robotic Communications for 5G and Beyond: Challenges and Research Opportunities

Yuanwei Liu, Xiao Liu+5 more|arXiv (Cornell University)|Dec 9, 2020

UAV Applications and Optimization14 references4 citations

TL;DR

This paper proposes a 5G/B5G-enabled robotic communications framework emphasizing signal and spatial modeling, dynamic trajectory design, and resource allocation. It introduces the SLARM framework for joint localization and radio mapping, and employs reinforcement learning to enable robots to optimize trajectories and communications in real time, achieving improved reliability and reduced hardware costs in dynamic indoor and outdoor environments.

ABSTRACT

The ongoing surge in applications of robotics brings both opportunities and challenges for the fifth-generation (5G) and beyond (B5G) of communication networks. This article focuses on 5G/B5G-enabled terrestrial robotic communications with an emphasis on distinct characteristics of such communications. Firstly, signal and spatial modeling for robotic communications are presented. To elaborate further, both the benefits and challenges derived from robots' mobility are discussed. As a further advance, a novel simultaneous localization and radio mapping (SLARM) framework is proposed for integrating localization and communications into robotic networks. Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.

Motivation & Objective

• Address the challenge of integrating localization and communication in 5G/B5G-enabled robotic networks to reduce hardware complexity and improve accuracy.
• Overcome limitations of conventional robotic communication systems by enabling dynamic self-decision making in response to uncertain, changing environments.
• Design adaptive trajectory and resource allocation strategies for indoor and outdoor robots under heterogeneous mobility and QoS requirements.
• Enable low-latency, reliable communication for safety-critical robotic applications such as remote control and collision avoidance.
• Develop a machine learning-driven approach to optimize multi-objective performance in robotic networks, including fuel efficiency and communication quality.

Proposed method

• Proposes a novel Simultaneous Localization and Radio Mapping (SLARM) framework that integrates localization and radio map construction using existing cellular infrastructure.
• Employs reinforcement learning (RL) to enable robots to learn optimal control policies from real-time environmental feedback and historical experience.
• Utilizes radio maps—digital representations of signal propagation in indoor environments—to train ML models for dynamic trajectory and resource allocation.
• Applies transfer learning (TL) to improve sample efficiency and avoid local optima in RL training, enabling faster convergence to optimal trajectories.
• Introduces a robotic-to-infrastructure (R2I) communication framework to coordinate resource allocation and trajectory planning in real time.
• Combines continuous state space (for power allocation) and discrete state space (for path selection) in an RL model to solve the coupled optimization problem.

Experimental results

Research questions

• RQ1 How can localization and communication be jointly optimized in 5G/B5G-enabled robotic networks to reduce hardware cost and improve accuracy?
• RQ2 What role does reinforcement learning play in enabling robots to dynamically adapt their control policies in uncertain, time-varying environments?
• RQ3 How can radio maps be leveraged to train ML models for efficient trajectory and resource allocation without physical deployment?
• RQ4 What are the performance gains of using transfer learning in RL-based robotic trajectory planning for communication reliability?
• RQ5 How can multi-objective optimization (e.g., fuel efficiency and communication quality) be achieved in robotic networks under dynamic constraints?

Key findings

• The SLARM framework successfully integrates localization and radio mapping, reducing dependency on high-cost onboard sensors and improving positioning accuracy.
• Reinforcement learning enables robots to learn optimal policies for dynamic environments, achieving improved adaptation and decision-making without human intervention.
• Transfer learning significantly enhances sample efficiency in RL training, allowing robots to converge faster to optimal trajectories while avoiding local optima.
• Radio maps enable effective training of ML models for trajectory and resource allocation in indoor environments without physical hardware deployment or venue costs.
• The proposed R2I framework ensures reliable communication links during robot movement, maintaining quality of service even in high-density traffic scenarios.
• Case studies show that robots using the proposed framework reduce fuel consumption by optimizing paths based on real-time traffic data from base stations, especially under high vehicle density.