A Qualitative Analysis of a USB Camera for AGV Control

Abstract

The increasing use of Automated Guided Vehicles (AGV) in the industry points to a search for better techniques and technologies to adapt to market requirements. Proper position control and movement give an AGV greater movement accuracy and greater lateral oscillations stability and vibration. It leads to smaller corridors and leaner plants, to more relaxed shipment devices, and to greater safety in the transport of fragile loads, for instance. AGV control techniques are not new, but new sensors’ applications are possible, such as USB cameras. In this sense, it is necessary to ensure the sensor is adequate to control system requirements. This work addresses AGVs driven by passive floor demarcations. It presents a qualitative analysis of a USB camera as sensors for AGV control, not yet a common industrial application. We performed the experiments with a small AGV prototype on an eight-shaped lane, varying both camera parameters and AGV parameters, such as linear speed. The AGV uses a USB camera with different image processing settings—different morphological filters structuring elements shapes and sizes, and three different image resolutions—to analyze the factors that affect line detection and control processing. This paper’s main contribution is a qualitative and quantitative analysis for the different sensor configurations. In addition, it discusses the influence sources on camera image as a position sensor. Furthermore, the experiments confirm sensor pertinence for the proposed control system.

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

“…In addition, the AGV will include additional sensors, such as obstacle detection, to help it to navigate over the obstacle. The AGV successfully traveled in the required tape-type path (Figure 22a,b) with constant speed when tested in an indoor area using the D. Puppim de Oliveira et al [16] approach. When the AGV speed increased from 0.08 m/s to 0.29 m/s [10], the AGV breaks from the tape-type path (Figure 22c,d) as it approaches curvature.…”

“…When the AGV operates on agricultural land and is not aware of path curvature, it achieves a Mean Absolute Error (MAE) [14] of 0.1947 m error, indicating that the AGV is going away from the ground truth path [15], due to the AGV speed being steady if the AGV speed has increased. D. Puppim de Oliveira et al [16] tested the AGV in an indoor environment using landmark-based navigation and discovered a curvature path on a tape-type path. They increased the AGV's speed and stated that the experiment meets to identify the motion blur occurrence in the speed range of 0.08 m/s to 0.29 m/s, as well as its impact on measurements.…”

An extended analysis on tuning the parameters of Adaptive Monte Carlo Localization ROS package in an automated guided vehicle

“…Figure 8 shows the distance between the camera sensor and the AGV tags plane, and the camera sensor field of view angle, as well as the size of the generated image, in pixels. With this information and using the approach of [30], one can calculate the equivalent distance value, in meters, per image pixel. Thus, the vision-based external tracking system measures the AGV pose in the environment.…”

“…Given the matrix D, Equation 1, and the rotation matrix R, Equation 2, Equation 3 shows the linear transformations to obtain the pose values on the same Cartesian coordinate system of the map created using GMapping. The transformed pose coordinates are x , y , in meters, and θ , in degrees; d p = 0.0047 m/pixel is the equivalent distance per image pixel, which was found according to [30]; o x , o y , and o θ are the original coordinates of the mapping step; x p and y p are the AGV position in pixels, and θ p is the θ angle related to the frame coordinate axis.…”

Roadmap, routing and obstacle avoidance of AGV robot in the static environment of the flexible manufacturing system with matrix devices layout

“…Cheong and Lee based on the image to calculate the distance from the position of the AGV robot to the device to locate the robot's stop to create a low-cost AGV system suitable for SMEs (Small and Medium Enterprises) 9 ; Other scholars used laser scanners to create a roadmap and ability to avoid collisions in the active environment of AGV of 5th generation in the application of AGV robot servicing the Košice-šaca hospital in Slovakia 10 . In addition, many other studies address mapping for AGVs using cameras and different methods based on which local optimization algorithms based on maps to avoid obstacles for AMRs [11][12][13] . Regarding routing and finding the shortest path for AGV robots, there are studies [14][15][16][17] but only considering the length in terms of distance and experimenting with small robot models.…”