Obstacle Avoidance and Target Tracking by Two Wheeled Differential Drive Mobile Robot using ANFIS in Static and Dynamic Environment

EasyChair Preprint 3865

Abstract

This paper proposes an idea for navigation and target acquiring for an autonomous robot in static as well dynamic environment using a tuned and trained Sugeno-type fuzzy inference system. This term for such system is Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS incorporates benefits from both fuzzy logic controller and neural networks and therefore is a robust tool to solve the stated problem. The input/output training data used to tune the system parameters is intuitively chosen and can be considered expert knowledge. The architecture of the entire system consists of four ANFIS controllers. Two of them are used to control the wheel velocities of left and right wheel of the robot in order to get to the target defined by the user. The remaining two ANFIS controllers are utilized to successfully avoid obstacles encountered on the way to target.

Keyphrases: ANFIS, Fuzzy Logic Controller, obstacle avoidance, robot navigation

@booklet{EasyChair:3865,
  author    = {Dhruv Patel and Kelly Cohen},
  title     = {Obstacle Avoidance and Target Tracking by Two Wheeled Differential Drive Mobile Robot using ANFIS in Static and Dynamic Environment},
  howpublished = {EasyChair Preprint 3865},
  year      = {EasyChair, 2020}}