Abstract:
Most animals, including humans, use vision to track objects and to avoid obstacles. An
artificial system with similar capabilities will have many practical applications. But most
computer based vision systems require high computational power, complex vision algorithms,
and sophisticated sensors. In this research, optical flow based novel approach was tested,
which was inspired by the vision system of honeybees. This method reduced the
computational load and used only a single low-resolution camera for obstacle avoidance and
tracking.
The mathematical model for the optical flow was based on the pinhole model. A custom 3-D
simulation framework was developed using Simulink and VRML, which was compared
against the mathematical model. For velocities less than 30 ms"1, the results of the simulations
agreed with the mathematical model.
After the verification of the accuracy of the simulation framework, two models were
simulated, a ground based robot with two degrees of freedom, and a flying robot with six
degrees of freedom. Initial controller algorithms were based on the optical flow balancing
models and were able to avoid obstacles. But they suffered from gap gravitating and
overshooting at comers. Controller algorithms were developed in an iterative manner, and
final fuzzy logic based algorithms prevent gap gravitating, was able to control the speed to
make tight turns and was more efficient in avoiding obstacles.
The ground-based robot was tested with seven pre identified obstacle patterns with different
orientations, colors, textures and light conditions and was able to avoid them with average
accuracy more than 96 %. The flying robot, quadcopter, was tested for hovering capabilities
and was able to reach stability in less than 4 seconds. Due to physical limitations, it was not
possible to make extensive research on the physically build flying robot. But experiments
were carried in the simulation. The robot was able to automatically reduce the speed with
altitude similar to an airplane. It was also able to hover and to cruise at set speeds up to 30 ms"
l . It was also able to measure its rotation with respect to the ground.
The tracking experiments were conducted for single objects and multiple objects. The optical
flow was used to detect the objects. When tracking the pendulum, the correlation between the
theoretical period and the calculated period was as high as 0.996. The proposed method was
able to evaluate the velocity of more than 10,000 points in a fluid flow by tracking smallsuspended
particles floating on it. In future, it may be possible to develop a mobile robot that
can track a moving target while avoiding obstacles using optical flow.