Open AccessPath Planning for Autonomous Driving of Mobile Robots using Deep Neural Network based Model Predictive Control
Author(s) -
Kiwon Yeom
Publication year - 2021
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae1121_04
Subject(s) - model predictive control , artificial neural network , mobile robot , computer science , motion planning , controller (irrigation) , control theory (sociology) , robot , acceleration , path (computing) , control engineering , artificial intelligence , control (management) , engineering , physics , classical mechanics , agronomy , biology , programming language
A car-like mobile robot is a nonlinear affine system, and the mobile robot has physical constraints such as velocity and acceleration. Thus, no satisfactory solution may not be provided during self-driving under unknown environments. Although Model Predictive Control (MPC) has provided good performance in terms of control strategy, it is difficult to optimize the control parameters due to the uncertainty and non-linearity of a process. In this paper, the Deep Neural Networks (DNN) based Model Predictive Controller (MPC) is derived for tracking the given path during self-driving. The proposed DNN MPC produces the global optimal solution which has better performance than traditional MPC in terms of the errors of position and orientation. This paper verifies that the proposed DNN MPC based controller can track the desired path with high precision for the car-like mobile robot. Keywords—Path planning, autonomous driving, mobile robot, deep neural network, model predictive control.