Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …

An evolutionary algorithm based framework, a combination of modified breeder genetic
algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an …

This paper formulates the vehicle navigation task as a constrained optimal control problem
with constraints bounding a traversable region of the environment. A model predictive …

In this paper, a new approach is developed for solving the problem of mobile robot path
planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms …

Autonomous vehicles (AVs) provide a new mobility option and have been becoming
widespread around the world. They can be used not only as automobiles but also as public …

How to drive safely in complex real-world traffic settings has long been a question and
challenge for autonomous vehicles (AVs). Decision-making systems (DecSs) are the core of …

The proposed approach in this paper involves a new grid-based map model called “memory
grid” and a new behavior-based navigation method called “minimum risk method”. The …

A new fuzzy-based potential field method is presented in this paper for autonomous mobile
robot motion planning with dynamic environments including static or moving target and …

In this article, a risk-aware motion control scheme is considered for mobile robots to avoid
randomly moving obstacles when the true probability distribution of uncertainty is unknown …

Most conventional motion planning algorithms that are based on the model of the
environment cannot perform well when dealing with the navigation problem for real-world …