OBJECT DETECTION SYSTEM BASED ON YOLO AND PALLET POSE ESTIMATION WITH ARUCO MARKER FOR AUTONOMOUS FORKLIFT
Utilizing technology such as artificial intelligence and robotics potentially improves E-Commerce trends on the production and consumption side. Autonomous forklift is the innovation that can be implemented for picking and distributing goods in warehousing with pallets. Pallet is a horizontal rec...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/73660 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Utilizing technology such as artificial intelligence and robotics potentially
improves E-Commerce trends on the production and consumption side.
Autonomous forklift is the innovation that can be implemented for picking and
distributing goods in warehousing with pallets. Pallet is a horizontal rectangular
structure that has a cavity for placing objects on it. The picking system in the
warehouse using autonomous forklift requires a perception system which consists
of detecting and estimating poses on the pallet when in the picking mode and
detecting the dynamic object while in the navigation mode. This research will cover
how to estimate pose pallets with some different approaches as well as human and
another forklift detection. ArUco marker will be used on the custom pallet model to
estimate the position and orientation in real time using the coordinates
transformation system as well as information from roll pitch yaw angles
representation. The object detection system will be implemented using the You Only
Look Once (YOLO) method in estimating dynamic objects with various lightning
condition in a warehouse. Camera calibration, distance, position, orientation, and
the data communication protocol will be discussed in this study. Position estimation
system has the value of error 1.621 cm, 2.99 cm, and 2.7966 cm in the x, y, and z
planes respectively. Orientation estimation also has the value of error 0.3512
degrees in roll, 2.094 degrees in pitch, and 1.4992 degrees in yaw. The speed of
object detection can reach 24 frames per second (FPS) with the converted YOLOv5
to ONNX (Open Neural Network Exchange) model and the distance to marker with
regression method in calibration has an error rate of 3.99 cm on the first camera
and 2.26 cm on the second camera. The detection of the marker also reached 30
FPS on camera. Based on the experimental results, the object detection model and
pose estimation on pallet have provided the expected estimation results locally. |
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