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Power-Efficient Autonomous Mobile Robots

Liangkai Liu, Weisong Shi, Kang G. Shin

TL;DR

pNav presents a holistic power-management framework for AMRs that co-optimizes cyber and physical subsystems. By combining millisecond-level power prediction, environment-aware locality modeling, collision forecasting, and a centralized coordinator, it achieves substantial end-to-end power reductions while preserving navigation safety and performance. The Donkey platform enables fine-grained power profiling, and implementation in the ROS Navigation Stack demonstrates a 38.1% reduction in total power with >96% power-prediction accuracy and modest latency and localization trade-offs. This work highlights the importance of CPS-level co-design and offers a practical blueprint for energy-efficient, safe autonomous navigation in resource-constrained robots.

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.

Power-Efficient Autonomous Mobile Robots

TL;DR

pNav presents a holistic power-management framework for AMRs that co-optimizes cyber and physical subsystems. By combining millisecond-level power prediction, environment-aware locality modeling, collision forecasting, and a centralized coordinator, it achieves substantial end-to-end power reductions while preserving navigation safety and performance. The Donkey platform enables fine-grained power profiling, and implementation in the ROS Navigation Stack demonstrates a 38.1% reduction in total power with >96% power-prediction accuracy and modest latency and localization trade-offs. This work highlights the importance of CPS-level co-design and offers a practical blueprint for energy-efficient, safe autonomous navigation in resource-constrained robots.

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.

Paper Structure

This paper contains 44 sections, 12 equations, 17 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Autonomous Mobile Robots
  4. Motivation
  5. Challenges in Achieving CPS Power-Efficiency
  6. Variabilities of System Power Consumption Breakdown
  7. Environment-Dependent Navigation Locality
  8. Coordination of C and P Subsystems
  9. Design of pNav
  10. System Overview
  11. Technical Challenges
  12. Power Predictor
  13. Motor Power Consumption
  14. Power Consumption by Cyber/Embedded Subsystems
  15. Integration into Path Planner
  16. ...and 29 more sections

Figures (17)

  • Figure 1: pNav --- holistic power-management system
  • Figure 2: A general software pipeline for AMRs.
  • Figure 3: Power-consumption breakdown.
  • Figure 4: View overlap with linear/angular speed.
  • Figure 5: Power timeline for AMR.
  • ...and 12 more figures