The Vision Guided Robotic systems (VGR) is an essential aspect of modern intelligent robotics. The VGR is rapidly transforming manufacturing processes by enabling robots to be highly adaptable and intelligent reducing the cost and complexity. For any sensor-based intelligent robots, vision-based planning is considered as one of the most prominent steps followed by controlled actions using visual feedback. To develop robust vision-based autonomous systems in robotic applications, path-planning and localization can be implemented along with Visual Servoing (VS) for robust feedback control. In the available literature, most of the reviews are focused on a particular module of autonomous systems like path planning, motion planning strategies, or Visual Servoing techniques. In this paper overall review of different modules in vision-guided robotic systems is presented. So, this review provides researchers with broader in-depth knowledge about different modules that exist in the vision-guided autonomous system. The review also includes different vision sensors that are commonly used in industries covering their characteristics and applications. In this work, overall, 227 research papers in path planning and vision-based control algorithms are reviewed with recent intelligent techniques based on optimization and learning-based approaches. The graphical analysis illustrating the advancements of research in the field of vision-based robotics using Artificial Intelligence (AI) is also discussed. Lastly, this paper concludes by discussing some of the research gaps, challenges, and future directions existing in vision-based planning and control.

Weld seam identification with industrial robots is a difficult task since it requires manual edge recognition and traditional image processing approaches, which take time. Furthermore, noises such as arc light, weld fumes, and different backgrounds have a significant impact on traditional weld seam identification. To solve these issues, deep learning-based object detection is used to distinguish distinct weld seam shapes in the presence of weld fumes, simulating real-world industrial welding settings. Genetic algorithm-based state-of-the-art object detection models such as Scaled YOLOv4 (You Only Look Once), YOLO DarkNet, and YOLOv5 are used in this work. To support actual welding, the aforementioned architecture is trained with 2286 real weld pieces made of mild steel and aluminum plates. To improve weld detection, the welding fumes are denoised using the generative adversarial network (GAN) and compared with dark channel prior (DCP) approach. Then, to discover the distinct weld seams, a contour detection method was applied, and an artificial neural network (ANN) was used to convert the pixel values into robot coordinates. Finally, distinct weld shape coordinates are provided to the TAL BRABO manipulator for tracing the shapes recognized using an eye-to-hand robotic camera setup. Peak signal-to-noise ratio, the structural similarity index, mean square error, and the naturalness image quality evaluator score are the dehazing metrics utilized for evaluation. For each test scenario, detection parameters such as precision, recall, mean average precision (mAP), loss, and inference speed values are compared. Weld shapes are recognized with 95% accuracy using YOLOv5 in both normal and post-fume removal settings. It was observed that the robot is able to trace the weld seam more precisely.

Weld seam detection and tracking using intelligent techniques is a common task in robotic welding. In this study, vision-based robotic welding is implemented using a 2D Camera to reduce the experimental cost in a planar environment. Firstly, different multi-class object recognition algorithms namely Faster Region-Based Convolutional Neural Network (Faster R-CNN), Single Shot Detector (SSD) and You Look Only Once (YOLOv3) are trained with 1530 manually labeled images. To recognize the weld seam under different brightness levels addressing arc light and splash, Contrast-Limited Adaptive Histogram Equalization (CLAHE) method has been used. Further, an extensive experimental study is conducted to find the optimal hyperparameters among three deep learning models. The performance metrics like Accuracy, Root Mean Square Error, Precision, Recall, Mean Average Precision and F1-score are calculated for different test cases. Finally, homogeneous coordinate transformation is implemented to obtain robot coordinates from weld seam pixel coordinates. The analysis is also extended to detect the bad welding conditions for improper shapes and track the weld seam for good welding shapes using TAL BRABO manipulator. It was found that the weld shapes were accurately detected and tracked precisely with 99.9% accuracy using YOLOv3 than Faster R-CNN and SSD.

Path planning for robotic manipulators has proven to be a challenging issue in industrial applications. Despite providing precise waypoints, the traditional path planning algorithm requires a predefined map and is ineffective in complex, unknown environments. Reinforcement learning techniques can be used in cases where there is a no environmental map. For vision-based path planning and obstacle avoidance in assembly line operations, this study introduces various Reinforcement Learning (RL) algorithms based on discrete state-action space, such as Q-Learning, Deep Q Network (DQN), State-Action-Reward- State-Action (SARSA), and Double Deep Q Network (DDQN). By positioning the camera in an eye-to-hand position, this work used color-based segmentation to identify the locations of obstacles, start, and goal points. The homogeneous transformation technique was used to further convert the pixel values into robot coordinates. Furthermore, by adjusting the number of episodes, steps per episode, learning rate, and discount factor, a performance study of several RL algorithms was carried out. To further tune the training hyperparameters, genetic algorithms (GA) and particle swarm optimization (PSO) were employed. The length of the path travelled, the average reward, the average number of steps, and the time required to reach the objective point were all measured and compared for each of the test cases. Finally, the suggested methodology was evaluated using a live camera that recorded the robot workspace in real-time. The ideal path was then drawn using a TAL BRABO 5 DOF manipulator. It was concluded that waypoints obtained via Double DQN showed an improved performance and were able to avoid the obstacles and reach the goal point smoothly and efficiently.