Abstract:
There are many circumstances where involvement of a human driver to control
vehicles is not feasible. The best examples for the above mentioned scenarios are the
applications based on Astrology. As a solution to this problem, researchers are trying
to create unmanned autonomous vehicles. Most of these researches have been
conducted using the power of artificial intelligence. Nevertheless unmanned
autonomous vehicle navigation is one of the biggest problems in the current era of
artificial intelligence. It is more difficult when vehicles are navigating dynamically
changing environment. This thesis presents a swarm intelligent based solution for the
navigation of unmanned ground vehicles within dynamically changing environment.
The proposed solution uses only local information around the vehicle, as in reality
human driver also getting decisions based on the partially observable environment.
System then uses current positions of unmanned vehicles as inputs and provides
future positions of unmanned vehicles as the output. Also this proposed system
consists of three main modules called data acquisition module, data processing and
decision making module and decision execution module. Data acquisition module
collects data from other agents and from the environment. Data processing and
decision making module acts as the brain of the system and decision execution
module executes the output of the decision making module.
Evolutionary computing and machine learning are main techniques which were used
behind this proposed system. System initially uses evolutionary computing technique
to navigate in an unknown environment. But when system familiarize with the
environment, it tries to work with its prior knowledge by using machine learning
techniques. Ultimately vehicles will navigate as swarms of vehicles to the targets.
Evaluation illustrates swap mutation is more efficient than Gaussian mutation in
evolutionary computing approach. Neural network works 98% accurately when we
use 25000 training samples in the machine learning module. Final evaluation uses
both computer simulated environment and small real toy vehicles to demonstrate the
solution. Upon completion of the final system, we can observe a successful target
oriented navigation of vehicles in a partially observable environment using swarm
intelligence based approach
