Power-Efficient Autonomous Mobile Robots

TL;DR

This paper introduces pNav, a power-management system for autonomous mobile robots that optimizes power use by jointly managing cyber and physical subsystems, achieving over 96% prediction accuracy and 38.1% power savings.

Contribution

The paper presents a novel integrated power-management system, pNav, that jointly optimizes cyber and physical subsystems for enhanced power efficiency in AMRs.

Findings

Achieves over 96% accuracy in power consumption prediction.

Reduces power consumption by 38.1% without affecting navigation accuracy.

Demonstrates effectiveness in real robot and simulation environments.

Abstract

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.