Robot Vision system is lately an important part of the robot system and it becomes main
source of the data used for robot environment perception in order to adequate interaction
between robot and surrounding objects and humans. There are numerous advantages of the
vision system, but there are also disadvantages like unrobustness of the vision system
considering external influences, very complex analysis of cluttered scenes or object recognition
when object shape changes. Since current state of the art shows growing potential of
development, improvement and implementation of robot vision system based on artificial
intelligence and machine learning, for object recognition and human tracking, this field of
scientific research is used as a basis for research presented in this doctoral dissertation.
During the research it was mandatory to develop vision system that collects data and
enables robot to reliably and autonomously function in dynamical environment. Vision system
needs to enable f...eature extraction and classification of different objects from the scene without
large databases commonly used in modern robot systems. Therefore, several new algorithms
based on neural networks, fuzzy logic and genetic algorithms (as well as some other artificial
intelligence algorithms) were developed and used in intelligent mobile robot platform for object
and human detection, tracking and interaction.
Presented research is guided towards development of mobile robot platform and its
control system based on intelligent robot vision algorithms for object and human recognition
(classification) and tracking in lab environment. Initial research were done with robot platform
where vision system is used as main sensor for manipulator control, and developed algorithms
were basis for development of algorithms for human detection and tracking by mobile robot.
Initial research was done in order to enable object recognition for DaNI mobile robot
platform with monocular camera and FRIEND robot system with manipulator equipped with
stereo camera. For adequate interaction of robot with surrounding objects segmentation
algorithms and intelligent classification algorithms were developed.
Further research was done for human tracking, so experimental mobile robot platforms
with stereo camera, thermal camera and RGBD sensor were developed. In this dissertation
improvement of robot vision module by neural network classificatory is presented as well as
concept of GA-optimal neural network input selection. Implementation of thermal camera for
human tracking into mobile robot system is not very common, so development of new fuzzysegmentation
algorithm and new genetic algorithm for optimal segmentation parameter tuning
of thermal image was done. Also, software based on Support Vector Machne (SVM) was
developed for classification of objects in thermal image (human detection in thermal image).
As an improvement of human tracking algorithm, in this dissertation nonlinear
autoregressive neural network is suggested for prediction and estimation of human position
filmed by stereo camera. Finally, some solutions for intelligent control of mobile robot platform
equipped with RGBD sensor for human tracking were suggested and with adequate adaptation
this platform can be used for gesture recognition.
Results presented in this doctoral dissertation confirm that implementation of modern
methods and algorithms from the palate of artificial intelligence and machine learning tools
allows development of vision based control system that enables mobile robot to detect,
recognize and locate objects and humans, that further enables intelligent interaction of robot
system with surrounding environment. Implementation of more than one artificial intelligence
technique (like artificial neural networks, fuzzy logic and genetic algorithms) into intelligent
control system development enables autonomous and reliable object and human recognition
and tracking that is superior compared to current solutions.
