Energy Efficiency Deep Reinforcement Learning for URLLC in 5G Mission-Critical Swarm Robotics

5G network provides high-rate, ultra-low latency, and high-reliability connections in support of wireless mobile robots with increased agility for factory automation. In this paper, we address the problem of swarm robotics control for mission-critical robotic applications in an automated grid-based...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management 2024-10, Vol.21 (5), p.5018-5032
Main Authors: Ho, Tai Manh, Nguyen, Kim-Khoa, Cheriet, Mohamed
Format: Article
Language:English
Subjects:
5G mobile communication
5G network
Algorithms
Artificial neural networks
Automatic control
Automation
cloud robotics
Codes
Collision avoidance
Decoding
Deep learning
deep reinforcement learning
Energy consumption
Energy efficiency
industrial automation
Machine learning
Multiagent systems
Network latency
Optimal control
Robot control
Robot kinematics
Robot learning
Robot sensing systems
Robotics
Robots
Service robots
Swarm robotics
Task analysis
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Summary:5G network provides high-rate, ultra-low latency, and high-reliability connections in support of wireless mobile robots with increased agility for factory automation. In this paper, we address the problem of swarm robotics control for mission-critical robotic applications in an automated grid-based warehouse scenario. Our goal is to maximize long-term energy efficiency while meeting the energy consumption constraint of the robots and the ultra-reliable and low latency communication (URLLC) requirements between the central controller and the swarm robotics. The problem of swarm robotics control in the URLLC regime is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short blocklength are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm outperforms the baselines in terms of decoding error probability and energy efficiency.
ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2024.3406350