Energy-Efficient Mobile Robot Control via Run-time Monitoring of Environmental Complexity and Computing Workload

TL;DR

This paper presents a run-time adaptive control system for mobile robots that optimizes energy consumption by jointly adjusting mechanical and computational actuators based on environmental complexity, significantly saving battery energy.

Contribution

It introduces a fast hill-climbing algorithm for real-time joint control of CPU and motor settings, improving energy efficiency in vision-based mobile robots.

Findings

Average energy savings of 50.5%, 41%, and 30% across different environment complexities.

Joint control of mechanical and computational actuators outperforms independent control strategies.

Real-time optimization adapts to environmental changes, enhancing energy efficiency.

Abstract

We propose an energy-efficient controller to minimize the energy consumption of a mobile robot by dynamically manipulating the mechanical and computational actuators of the robot. The mobile robot performs real-time vision-based applications based on an event-based camera. The actuators of the controller are CPU voltage/frequency for the computation part and motor voltage for the mechanical part. We show that independently considering speed control of the robot and voltage/frequency control of the CPU does not necessarily result in an energy-efficient solution. In fact, to obtain the highest efficiency, the computation and mechanical parts should be controlled together in synergy. We propose a fast hill-climbing optimization algorithm to allow the controller to find the best CPU/motor configuration at run-time and whenever the mobile robot is facing a new environment during its travel. Experimental results on a robot with Brushless DC Motors, Jetson TX2 board as the computing unit, and a DAVIS-346 event-based camera show that the proposed control algorithm can save battery energy by an average of 50.5%, 41%, and 30%, in low-complexity, medium-complexity, and high-complexity environments, over baselines.