Leader-follower System for Unmanned Ground Vehicle
Name
Kristjan Jansons
Abstract
Unmanned ground vehicles can be utilized effectively, if they have autonomous capabilities. One of those capabilities is leader-follower functionality, which means that the robot has to follow a predefined object. Usually the predefined object is a person, which is still more effective than controlling the robot with teleoperation using a remote control. It is worthwhile, because teleoperation requires the full attention of the operator while leader-follower system allows the person to keep their situational awareness, which is essential in a military environment. Leader-follower systems often exploit multiple
technologies: LIDARs, GPS, infrared markers, radar transponder tags and cameras. The aim of this thesis is to develop and test the proof of concept leader-follower system, which relies on camera vision only and is able to follow any object which means that the class of the object does not have to be predefined. Specifically, the approach uses behavioral cloning to train deep siamese network with convolutional layers to determine the velocity and turning commands of the vehicle based on input from camera. The results show that the proof of concept system works, but requires further development in order to make it robust and safe.
technologies: LIDARs, GPS, infrared markers, radar transponder tags and cameras. The aim of this thesis is to develop and test the proof of concept leader-follower system, which relies on camera vision only and is able to follow any object which means that the class of the object does not have to be predefined. Specifically, the approach uses behavioral cloning to train deep siamese network with convolutional layers to determine the velocity and turning commands of the vehicle based on input from camera. The results show that the proof of concept system works, but requires further development in order to make it robust and safe.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Tambet Matiisen
Defence year
2017