pNav系统通过协同优化软硬件，让移动机器人省电38%以上。
Power-Efficient Autonomous Mobile Robots
- 融合毫秒级能耗预测，实时监控软硬件状态
- 能耗预测准确率达96%以上，功耗降低38.1%
- 适合关注机器人能效与续航的开发者与研究者
本文提出pNav，一种新型电源管理系统，通过联合优化自主移动机器人（AMR）的物理/机械与网络子系统，显著提升其功耗与能效表现。通过对AMR功耗特性分析，发现三个涉及网络（C）与物理（P）子系统的挑战：（1）系统功耗构成的波动性，（2）环境感知下的导航局部性，（3）C与P子系统的协同问题。pNav采用多维度方法实现高效能：首先，集成毫秒级的C与P子系统功耗预测；其次，引入新型实时空间-时间导航局部性建模与监控机制；第三，支持导航、检测等软件与电机、DVFS驱动等硬件配置的动态协同。pNav基于ROS导航栈、2D LiDAR与摄像头进行原型实现。真实机器人与Gazebo仿真环境的深入评估显示，其功耗预测精度超过96%，在不牺牲导航准确性与安全性的前提下，实现38.1%的功耗降低。
原文摘要 · Abstract (English)
This paper presents pNav, a novel power-management system that significantly enhances the power/energy-efficiency of Autonomous Mobile Robots (AMRs) by jointly optimizing their physical/mechanical and cyber subsystems. By profiling AMRs' power consumption, we identify three challenges in achieving CPS (cyber-physical system) power-efficiency that involve both cyber (C) and physical (P) subsystems: (1) variabilities of system power consumption breakdown, (2) environment-aware navigation locality, and (3) coordination of C and P subsystems. pNav takes a multi-faceted approach to achieve power-efficiency of AMRs. First, it integrates millisecond-level power consumption prediction for both C and P subsystems. Second, it includes novel real-time modeling and monitoring of spatial and temporal navigation localities for AMRs. Third, it supports dynamic coordination of AMR software (navigation, detection) and hardware (motors, DVFS driver) configurations. pNav is prototyped using the Robot Operating System (ROS) Navigation Stack, 2D LiDAR, and camera. Our in-depth evaluation with a real robot and Gazebo environments demonstrates a >96% accuracy in predicting power consumption and a 38.1% reduction in power consumption without compromising navigation accuracy and safety.
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