Development of Efficient Obstacle Avoidance for a Mobile Robot Using Fuzzy Petri Nets

Abstract

In the field of autonomously operated robots it is quite common for a form of environment mapping to be implemented. The process of environment mapping requires standby time and processing time. The robot remains stationary and surveys the surroundings, then constructs an environmental representation and determines the appropriate path of travel. This process diminishes the fluidity of robot locomotion, resulting in limited applications. Through the application of fuzzy Petri nets (FPNs) with embedded memory module, an enhanced mapping method was proposed to overcome such a limitation. With the storage and reuse of key information, such as past calculations and decisions made by the robot, time spent in survey and analysis is reduced. Since this approach requires less processing time, the proposed method can be executed while the robot is in motion, continuously looping at frequent intervals. The proposed method brings the benefits of greater movement fluidity while also introducing the ability to navigate in a dynamic environment with less erratic decision making. The modeling and simulation results demonstrated an 8% increase in efficiency.