基于深度强化学习的群体机器人移动感知计算卸载
Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning
AI总结：
针对群体机器人资源有限、规模受限的问题，提出基于边缘服务器侧移动感知深度强化学习的计算卸载方案，可在满足延迟要求的同时以最少机器人能耗保障计算精度。
摘要：
群体机器人有望实现大量肮脏、危险且枯燥任务的自动化。但机器人的能量、计算能力和通信资源均有限，因此当前群体机器人的机器人数量较少，仅能提供有限的时空信息。本文提出利用移动边缘计算减轻计算负担，在边缘服务器侧基于移动感知深度强化学习模型开发了一种有效的计算调度与资源分配方案。结果表明，所提方法能够满足延迟要求，并以最少的机器人能量保证计算精度。
英文摘要：
Swarm robotics is envisioned to automate a large number of dirty, dangerous, and dull tasks. Robots have limited energy, computation capability, and communication resources. Therefore, current swarm robotics have a small number of robots, which can only provide limited spatio-temporal information. In this paper, we propose to leverage the mobile edge computing to alleviate the computation burden. We develop an effective solution based on a mobility-aware deep reinforcement learning model at the edge server side for computing scheduling and resource. Our results show that the proposed approach can meet delay requirements and guarantee computation precision by using minimum robot energy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
