JCCO: Jointly Optimizing the Computational and Communication Costs for Resource Allocation in Energy-Efficient Swarm Robotics

Abstract

This paper presents a joint communication–computation optimization (JCCO) framework for energy-efficient resource allocation in edge-assisted swarm robotics. The proposed framework jointly optimizes task offloading, bandwidth allocation, transmission power, and computational workload under latency and energy constraints. A distributed optimization strategy combined with a lightweight deep reinforcement learning controller enables adaptive and scalable decision making for resource-constrained robotic swarms. The simulation results demonstrate that the proposed method reduces the total swarm energy consumption by up to 41% while maintaining more than 99% deadline satisfaction across varying swarm sizes and communication conditions. The framework further achieves improved fairness, lower communication overhead, and efficient embedded deployment suitability for TinyML-enabled robotic platforms.