S. Parasuraman

Objectives: This paper presents an amended Ant Colony Optimization (ACO) algorithm for a mobile robot navigation to find the most optimal path. Methods: A modified design and development of an improved Ant Colony Optimization algorithm based upon a prior research work done is proposed in this paper. The algorithm put forth is enhanced by simplifying the equations already proposed and enlarging the area of the simulation framework, extending the task capabilities of the robot, as well as testing the algorithm in real time on an autonomous mobile robot. Findings: The proposed algorithm has to calculate optimal trajectory for the mobile robot to traverse to perform the following tasks: target-searching, boundaryfollowing and obstacle avoidance. The total length of the path traversed determines the efficiency of path traced. This proposed method also enhances the utility of the ACO algorithm by designing and creating a feasible ACO graphical user interface. Further we carried out the research on the working of the ACO algorithm by performing systematic testing, simulations and real-time implementation. Improvements: Future work could involve the implementation of a positioning system that allows the robot to determine its actual real world position and then provide feedback to the ACO algorithm so that adjustments could be made. The basic ACO algorithm could be modified to model ants to move in eight directions. All simulations and real time implementations could be done in pre-known environments with static well defined obstacles. By including dynamic obstacle avoidance capabilities, the range of real life applications in which the algorithm could be implemented on would be greatly expanded.

Authors: S.Parasuraman, Bijan Shirinzadeh and Velappa Ganapathy

A key issue in the research of an autonomous robot is the design and development of the navigation technique that enables the robot to navigate in a real world environment. In this research, the issues investigated and methodologies established include (a) Designing of the individual behavior and behavior rule selection using Alpha level fuzzy logic system (b) Designing of the controller, which maps the sensors input to the motor output through model based Fuzzy Logic Inference System and (c) Formulation of the decision-making process by using Alpha-level fuzzy logic system. The proposed method is applied to Active Media Pioneer Robot and the results are discussed and compared with most accepted methods. This approach provides a formal methodology for representing and implementing the human expert heuristic knowledge and perception-based action in mobile robot navigation. In this approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of a set of simple conditional statements composed of linguistic variables.Keywards: Mobile robot, behavior based control, fuzzy logic, alpha level fuzzy logic, obstacle avoidance behavior and goal seek behavior