Energy-Efficient Autonomous Navigation Benchmarking

[Rocky_Shao]

Powersense_RC_Car: Energy-Efficient Autonomous Navigation Benchmarking

Project Overview

With generous help from Dr. Marco Brocanelli and his lab, I am building a research-oriented framework designed to profile the energy-to-performance trade-offs of autonomous platforms.

By integrating high-fidelity sensors (Intel RealSense & RPLidar) with variable computational and network constraints, this project aims to identify the sweet spot for power efficiency in edge robotics. (Hardware setup is generously provided by the lab and is mostly complete!)

Hardware Setup:

• Chassis: RC Chassis — Mobile platform
• Vision: Intel RealSense D455 — Stereo depth & VSLAM
• Lidar: RPLidar A2M* — 2D Obstacle detection
• Compute: Jetson Nano — Onboard inference
• Power Mon.: To be determined — Real-time power/current sensing

About Me & This Log

I’m Rocky Shao, a freshman at The Ohio State University majoring in Computer Engineering.

To keep myself on track with building this project, I will be posting my daily progress right here. Follow along on my journey as I dive deep into the world of robotics!

Check out the GitHub Repository

[Rocky_Shao]

March 14

My current idea:

• Get raw data from sensors (RealSense, LiDAR) and publish them to ROS topics.
• Run a policy node that uses the camera image, depth map, IMU data, and LiDAR scan to auto-navigate.
• Attach a power-measuring device to the battery.
• Tweak CPU frequency, offload edge computing, and log the data.

What I did today: I spent some time wrestling with mixed dependencies between my Python virtual environment and the system-installed ROS 2 Jazzy. I also struggled to find my running ROS nodes, only to realize I just needed to run ros2 daemon stop to get them listening to each other.

After sorting out those dependency headaches, I successfully got the RealSense color image and depth map working:

Armed with a better understanding of the setup, getting the RPLIDAR A2M8 running was fairly quick:

So right now, I’ve got the raw sensor data successfully publishing via ROS topics!

Tomorrow’s Plan: I plan to dive deeper into researching the hardware setup, specifically focusing on the power supply for the RC chassis.

Stay tuned!