Abstract:
ntelligent mobile robotic agents demand optimal motion planners with minimum query time.
Most contemporary algorithms lack one of these two required aspects. We propose a cellular
automata (CA) based efficient path planning scheme that generates optimal paths in minimum
time. A Cellular automata is evolved over the entire environment and subsequently used for
shortest path determination. This approach generates a parent-child relationship for each cell in
order to minimize the search time. Analysis and simulation results have proven it to be a robust
and a complete path planning scheme is robust and time efficient both in static and dynamic
environments.
In the second part of the thesis, we discuss an estimation problem of players in a Robocup
Small Size League based environment. RoboCup Small Size League provides with an interesting
platform for research on Multi-agent Intelligent Systems in an adversarial environment, where
the problems range from motion planning of robots to optimum decision making. An important
aspect in robot soccer is to define the strategies that a team should follow in order to successfully
execute a game of soccer. One approach to do this is to use the existing games to infer the
behaviors shown by the robots of a certain team. Specifically, the behaviors shown by a certain
robot during a game can be inferred and analyzed and may be even learnt to execute the game
play during a game. We used a regression based approach to create models for certain robots
based on the locations of the players in the field, using the data from the games of Robocup
2013.
