MSc Thesis Defense by:

Satya Shree Sankini

 

Date:  Wednesday, May 15th, 2019

Time:  2:00 pm – 4:00 pm

Location: 3105, Lambton Tower

 

Path planning is an essential task for the robot to navigate

and control its motion in any environment. The optimal path needs to be rerouted each time a new obstacle appears in front of the robot in the dynamic environment. This research focuses on the MAX-MIN Ant System Algorithm(MMAS) which is an Ant Colony Algorithm derived from Ant System and is different from it in terms of the pheromone deposition. The effectiveness of this algorithm to obtain a near optimal solution is illustrated by the means of experimental study. Using a greedier search than the Ant System algorithm is one of the specific characteristics of the MMAS, which will be studied in the research. The robot environment model is represented by a grid which has obstacles whose positions change in each map that is used. Local search routines and diversification mechanisms introduced by the previous researchers are used to enhance the performance of the MMAS algorithm. To implement the MMAS algorithm used in our research, the experiments are performed in MATLAB development environment where a simulation program is designed, and the algorithm is implemented in grid maps of sizes starting from the smallest grid 10x10 to the grid of size 400x400. In our experiments a new obstacle is added after every iteration of the algorithm which makes it a challenging task for the robots to find the near optimal path. The performance evaluation of the MMAS algorithm is studied and is also compared to that of the ACO algorithm when implemented in different grid maps.

 

 

Internal Reader:  Dr. Imran Ahmad

External Reader: Dr. Chunhong Chen

Advisor:  Dr. Dan Wu

Chair: Dr. Stephanos Mavromoustakos

 

 

5113 Lambton Tower, 401 Sunset Ave. Windsor ON, N9B 3P4, (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca