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

arXiv: https://arxiv.org/abs/2109.04285 · HTML: https://mlsanity.com/paper/2109.04285/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Autonomous Systems -- An Architectural Characterization](https://mlsanity.com/paper/1811.10277/) (2018)
- [Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors](https://mlsanity.com/paper/2010.15507/) (2020)
- [Asynchronous Corner Tracking Algorithm based on Lifetime of Events for DAVIS Cameras](https://mlsanity.com/paper/2010.15510/) (2020)

## Built on by (most cited first, 3 of 3)

- [When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making](https://mlsanity.com/paper/2603.16673/) (2026)
- [MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science](https://mlsanity.com/paper/2409.06558/) (2024)
- [TAPAS: Throughput-adaptive Perception for Autonomous Systems](https://mlsanity.com/paper/2607.17317/) (2026)

## Abstract (arXiv)

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.