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Vibration-Resistant State Estimation for a Construction Robot using IMU

Institute
Lehrstuhl für Fördertechnik Materialfluss Logistik
Type
Semester Thesis / Master's Thesis /
Content
experimental / constructive /  
Description

Introduction and Motivation

Accurate state estimation is a prerequisite for autonomy in mobile robots. For construction robots, this is particularly difficult due to the harsh operating environment. A major challenge is the vibration generated by the engine and tracks. This vibration corrupts signals from Inertial Measurement Units (IMUs), making it difficult to distinguish the robot's true motion from sensor noise. Raw IMU data in such conditions, therefore, needs advanced processing.

This thesis will directly address this problem. The goal is to develop and validate a signal processing pipeline that can filter and compensate for heavy vibration, allowing for the extraction of a clean and meaningful motion estimate from an IMU mounted on a tracked dumper.

Objectives

The primary goal is to design, implement, and validate a robust signal processing system that provides an accurate state estimate from an IMU operating in a high-vibration environment.

The key objectives are:

  • Data Acquisition Pipeline: Develop a reliable, time-synchronized data acquisition system using the Robot Operating System (ROS) to collect data from sensors as well as control inputs simultaneously
  • Characterize the Vibration Environment: Systematically analyze the IMU data to understand the frequency and magnitude of vibrations under different operating conditions (e.g., engine idle vs. moving).
  • Develop a Calibration and Compensation Pipeline: Design and implement filtering and calibration algorithms specifically to reject vibration noise while preserving the true motion signal.
  • Validate the Compensated State Estimate: Rigorously test the system to prove that the filtered output is a more accurate and reliable representation of the robot's motion than the raw sensor data.
  • Real-Time Implementation: Integrate the entire pipeline into a real-time ROS package that can be used for future research.

Methodology

  1. Sensor Characterization and Calibration:
  • Test, compare, and choose the IMU that is suitable for a construction robot.
  • Physically mount, wire, configure, and calibrate the IMU on the dumper.
  • Collect and analyze IMU data under various conditions (stationary with engine on/off, moving at different speeds) to create a detailed vibration profile using frequency analysis (e.g., FFT).
  1. Algorithm Development and Implementation:
  • Research and compare various signal processing techniques for vibration rejection for a low-speed, high-vibration construction robot.
  • Implement and test different approaches, such as: Frequency-domain filtering (e.g., notch or band-stop filters), adaptive filters (e.g., LMS, RLS) that can adjust to changing vibration patterns, or advanced Kalman Filter (EKF/UKF) tuning, explicitly modeling vibration as a noise source.
  1. Experimental Validation and Performance Analysis:
  • Design experiments to quantify the effectiveness of the compensation algorithms.
  • Model and visualize the robot's motion and the characteristics of each sensor.
  1. Validation and Performance Analysis:
  • Define test trajectories for the dumper (e.g., straight lines, circles).
  • Validate performance by comparing the filtered IMU output against a ground-truth source (e.g., GNSS in open-sky conditions).
  • Quantify noise rejection by analyzing sensor output while the robot is stationary with the engine running and at various velocities.

Expected Outcomes

  • A Comparative Study of Filtering Techniques: A thesis that details the implementation and compares the performance of different algorithms for vibration mitigation.
  • An IMU-instrumented construction robot, providing a reliable foundation for future work on sensor fusion and control.
  • A validated ROS package that provides a vibration-compensated state estimate of the robot's orientation and velocity from an IMU.
Requirements

Requirements and Qualifications

  • Background in Mechatronics, Robotics, Electrical Engineering, or a related field.
  • Practical experience with the Robot Operating System (ROS/ROS2).
  • Hands-on experience with hardware integration and sensors (e.g., IMUs, GNSS).
  • Good understanding of digital signal processing, control theory, and filtering techniques (e.g., Kalman filters, frequency analysis) would be beneficial.
  • A proactive and independent approach to problem-solving.
  • Good communication skills in English.

Application

We look forward to your application. If you're interested in writing your thesis with us, please email your CV, proof of grades, and a brief introduction of your interest and experience. Feel free to contact us with any questions.

Possible start
sofort
Contact
Yuan-Jen Huang, M.Sc.
Room: MW 0501
Phone: +49 (89) 289 - 15931
yuan-jen.huangtum.de