For decades several papers have been written on control and navigation of robots using artificial intelligent techniques such as fuzzy logic, neural network, genetic technique and other artificial intelligence techniques. Monitel & Adriansyah et al.1,2 have discussed about use of fuzzy logic techniques for control and navigation of mobile robot. Jin & Zuo et al.3,4 have analysed neural network techniques for control of mobile robot. Sheng and Li5 have used genetic algorithm for trajectory control of robot. Mohamed et al.6 have used potential field method for optimisation of path followed by an unmanned vehicle from source position to target position. Abdalla et al.7 have used particle swarm optimisation technique to find a safer path for mobile robot during navigation. Various researchers have used several techniques for finding out collision free path for robots.

Advancement in navigational path planning of robots using various artificial and computing techniques

Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review

Today, Automated Guided Vehicle (AGV) robots are integral to many factories. One of the basic problems of these robots is accurate navigation, which, in addition to creating security in performing tasks, it helps the robot to manage the battery power and energy and move on a predetermined path. Over the various method, the visions are based on good performance, recently been widely used. In this research, a simple but effective fusion method as the combination of vision(camera) and infrared (IR) sensors with the minimum number of sensors is proposed and implemented. The proposed method has been simulated and evaluated using the Vrep simulator with real dimensions of our previous design AGV system named Hongma and the Python API. In the simulation, the proposed method was carried out. In the experiment, five paths named Circle, Elliptical, Spiral,8 shapes, and Special path, different paths with different complexity were tested, and the experiment aimed to find the maximum speed at which the proposed algorithm and the vision sensor (camera sensors) can track the path with a 100% success rate. The results obtained in the experiment show the fusion method’s effectiveness over the five mentioned scenarios in tracking the planned path compared to the routine vision method.

Fusion BASED AGV Robot Navigation Solution Comparative Analysis and Vrep Simulation

Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, we present a comprehensive review of the state of the art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling. A system framework of CV in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, CV algorithms, a decision-making module, and an actuator. Applications of CV to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data preprocessing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for nonannotated data processing, developing effective data preprocessing mechanisms, customizing CV models, and opportunities aroused by 5G. 

https://figshare.com/articles/preprint/Computer_Vision_Techniques_in_Manufacturing/17125652/2/files/35020939.pdf

Computer Vision Techniques in Manufacturing

In recent years, the study of emotion has increased due to the interaction of human with machine as it is helpful to interpret human actions and to improve the relationship among humans and machines for developing the software that can understand the human states and can take action accordingly. This paper focuses on a preliminary study on emotion recognition using various psychological signals. Different researchers investigated various parameters which include facial expression, eye gaze, pupil size variation, eye movements using EEG, and deep learning techniques to extract the emotional features of humans. Diverse researchers have proposed a method for detecting emotions by using different psychological signals and achieved reliable accuracy. After a thorough analysis, it has been observed that the best accuracy achieved on the individual emotion detection was 90%. However, this experiment does not help to classify the specific emotion. To classify the specific emotion, the best accuracy achieved was 79.63%, which is a comparable accuracy.

Multimodal Emotion Recognition System Using Machine Learning and Psychological Signals: A Review

The main aim of this paper is to develop a smart material handling system using an AGV (automated guided vehicle). The task is to transport a container of a fixed size from a defined start point to a defined end point. There is an overhead camera located at the boundary of the arena, in such a way that complete arena can be seen in a single frame. The camera will be capturing real-time images of the vehicle to determine its position and orientation using OpenCV library. The computer will also perform the task of path planning by using various artificial intelligence algorithms like RRT (rapidly random exploring tree) and A* (A Star). The outcome of this process will be the shortest path from beginning point to finish point while avoiding the obstacles. The commands should be enough for the robot to understand where it should go next, i.e., the next pose for the robot. This process continues until the goal is reached. To achieve this, few algorithms are developed for shape detection and edge detection. They help in determining the obstacles and the free area/ path where robot can traverse. The image from overhead camera is used to make the shortest global path from start to end using image processing. The computer will do this using various packages in ROS (robot operating system). This global path will generate waypoints for robot to traverse and the image will also provide current pose for the robot. Though the orientation of the obstacles varies the path of AGV and will always follow the shortest path. Thus, AGV shows the artificial intelligent.

Chintan Kathrecha

Papers

Design and Development of Intelligent AGV Using Computer Vision and Artificial Intelligence