一句话：提出 MulBERRY 多智能体鲁棒学习框架，通过算法-硬件协同优化，在低电压位错误下提升无人机集群能效与任务成功率。
自主无人机集群需在严格的机载功耗约束下协作导航并运行强化学习模型。降低电压可节能，却会引发片上位错误，威胁任务安全与性能。为此，作者提出 MulBERRY 多智能体鲁棒学习框架，支持离线与端上学习，并通过自适应、协作式 agent-server 优化实现算法-硬件协同，首次展示多 UAV 系统鲁棒低电压运行的可行性。系统级实验结合算法级鲁棒学习与硬件级位错误、热和功耗表征，表明 MulBERRY 实现鲁棒性-性能-效率协同优化，并泛化到多种故障模式、环境与 UAV 配置。结果：飞行能耗最高降低 18.97%，成功任务数增加 22.07%，处理能耗降低 4.16 倍。
The adoption of autonomous swarms, consisting of a multitude of unmanned aerial vehicles (UAVs), operating in a collaborative manner, has become prevalent in mainstream application domains for both military and civilian purposes. These swarms are expected to collaboratively carry out navigation tasks and employ complex reinforcement learning (RL) models within the stringent onboard size, weight, and power constraints. While techniques such as reducing onboard operating voltage can improve the energy efficiency of both computation and flight missions, they can lead to on-chip bit failures that are detrimental to mission safety and performance. To this end, we propose MulBERRY, a multi-agent robust learning framework to enhance bit error robustness and energy efficiency for resource-constrained autonomous UAV swarms. MulBERRY supports multi-agent robust learning, both offline and on-device, with adaptive and collaborative agent-server optimizations. For the first time, MulBERRY demonstrates the practicality of robust low-voltage operation on multi-UAV systems leading to energy savings in both compute and mission quality-of-flight. We conduct extensive system-level experiments on autonomous multi-UAV navigation by leveraging algorithm-level robust learning techniques, and hardware-level bit error, thermal, and power characterizations. Through evaluations, we demonstrate that MulBERRY achieves robustness-performance-efficiency co-optimizations for autonomous UAV swarms. We also show that MulBERRY generalizes well across fault patterns, environments, UAV types, UAV agent numbers, and RL policies, with up to 18.97% reduction in flight energy, 22.07% increase in the number of successful missions, and 4.16× processing energy reduction.
@inproceedings{WanCSCBRR24,
title = {MulBERRY: Enabling Bit-Error Robustness for Energy-Efficient Multi-Agent Autonomous Systems},
author = {Zishen Wan and Nandhini Chandramoorthy and Karthik Swaminathan and Pin-Yu Chen and Kshitij Bhardwaj and Vijay Janapa Reddi and Arijit Raychowdhury},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640420}
}