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Hybrid navigation of an autonomous mobile robot to depress an elevator button

Wu Pan-Long, Zhang Zhe-Ming, Liew Chuin Jiat, Shaw Jin-Siang

Journal of Automation Mobile Robotics and Intelligent Systems

2022

Vol. 16, No. 4

25--35

The development of an autonomous mobile robot (AMR) with an eye-in-hand robot arm atop for depressing elevator button is proposed. The AMR can construct maps and perform localization using the ORB-SLAM algorithm (the Oriented FAST [Features from Accelerated Segment Test] and Rotated BRIEF [Binary Robust Independent Elementary Features] feature detector-Simultaneous Localization and Mapping). It is also capable of real-time obstacle avoidance using information from 2D-LiDAR sensors. The AMR, robot manipulator, cameras, and sensors are all integrated under a robot operating system (ROS). In experimental investigation to dispatch the AMR to depress an elevator button, AMR navigation initiating from the laboratory is divided into three parts. First, the AMR initiated navigation using ORB-SLAM for most of the journey to a waypoint nearby the elevator. The resulting mean absolute error (MAE) is 8.5 cm on the x-axis, 10.8 cm on the y-axis, 9.2-degree rotation angle about the z-axis, and the linear displacement from the reference point is 15.1 cm. Next, the ORB-SLAM is replaced by an odometry-based 2D-SLAM method for further navigating the AMR from waypoint to a point facing the elevator between 1.5 to 3 meter distance, where the ORB-SLAM is ineffective due to sparse feature points for localization and where the elevator can be clearly detected by an eye-in-hand machine vision onboard the AMR. Finally, the machine vision identifies the position in space of the elevator and again the odometry-based 2D-SLAM method is employed for navigating the AMR to the front of the elevator between 0.3 to 0.5 meter distance. Only at this stage can the small elevator button be detected and reached by the robot arm on the AMR. An average 60% successful rate of button depressing by the AMR starting at the laboratory is obtained in the experiments. Improvements for successful elevator button depressing rate are also pointed out.

Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints

Mishra Akshansh

Fatigue of Aircraft Structures

2020

Iss. 12

27--35

Friction Stir Welding joint quality depends on input parameters such as tool rotational speed, tool traverse speed, tool tilt angle and an axial force. Surface defects formation occurs when these input parameters are not selected properly. The main objective of the recent paper is to develop Discrete Wavelet Transform algorithm by using Python programming and further subject it to the Friction Stir Welded samples for the identification of various external surface defects present.