Intelligent mobile robot navigation in unknown and complex environment using reinforcement learning technique.

The usage of mobile robots (MRs) has expanded dramatically in the last several years across a wide range of industries, including manufacturing, surveillance, healthcare, and warehouse automation. To ensure the efficient and safe operation of these MRs, it is crucial to design effective control strategies that can adapt to changing environments. In this paper, we propose a new technique for controlling MRs using reinforcement learning (RL).

An improved human activity recognition technique based on convolutional neural network.

A convolutional neural network (CNN) is an important and widely utilized part of the artificial neural network (ANN) for computer vision, mostly used in the pattern recognition system. The most important applications of CNN are medical image analysis, image classification, object recognition from videos, recommender systems, financial time series analysis, natural language processing, and human-computer interfaces. However, after the technological advancement in the power of computing ability and the emergence of huge quantities of labeled data provided through enhanced algorithms, nowadays, CNN is widely used in almost every area of study.

Obstacle Detection in Infrared Navigation for Blind People and Mobile Robots.

The paper is a continuation of the authors' work intended for infrared navigation for blind people and mobile robots. This concerns the detection of obstacles in the person's or mobile robot's trajectory, in particular, the detection of corners. The temperature distribution of a building's internal wall near a corner has been investigated.