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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

647
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
647

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Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot.

Manuel Vega-Heredia1, Ilyas Muhammad1,2, Sriharsha Ghanta1

  • 1Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.

Sensors (Basel, Switzerland)
|March 19, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces the Mantis v2 glass-façade-cleaning robot. A vision-based system using an external camera achieved the best orientation estimation, with an error under 0.8 degrees for improved robot navigation.

Keywords:
glass-façade-cleaning robotsmulti-sensor integrationorientation trackingreconfigurable robotsrobot heading tracking

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Area of Science:

  • Robotics
  • Service Robots
  • Automation

Background:

  • Glass-façade-cleaning robots require precise orientation tracking for vertical surface navigation.
  • The complexity of shifting between window panels presents significant challenges for maintaining accurate robot positioning.

Purpose of the Study:

  • To propose and evaluate sensor-based methods for estimating the orientation of the Mantis v2 glass-façade-cleaning robot.
  • To compare the performance of various sensors and fusion techniques for angular position estimation in a real-world robot application.

Main Methods:

  • Utilized an Inertial Measurement Unit (IMU), wheel encoders, beacons, Time-of-Flight (ToF) sensors, and an external vision sensor on the Robot Operating System (ROS).
  • Implemented complementary and Kalman filters for sensor fusion to estimate heading angles.
  • Evaluated sensor performance using static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS).

Main Results:

  • Time-of-Flight (ToF) sensors were effective for orientation up to 30 degrees.
  • Beacon systems exhibited delays up to five seconds.
  • Odometry errors increased with navigation distance due to wheel slippage on glass surfaces.
  • The vision sensor system demonstrated superior performance with an orientation angle error below 0.8 degrees.

Conclusions:

  • The vision sensor-based approach is the most effective method for orientation estimation for the Mantis v2 robot.
  • The study validates the proposed sensor integration and orientation tracking techniques for façade-cleaning robots.