# Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
## 3 Computer Vision-Based Control
Typically this can be a region of interest that includes multiple, obstructed or complex objects that can require specific grasping approaches. One of the most common robotic grasping problems in industry settings is bin-picking [45], accordingly this problem has been already addressed for several decades [46]. Bin-picking is a task where arbitrarily placed objects that are overlapping each other in a bin must be taken out and placed in a dedicated position and orientation. Historically this problem has been addressed as one of the greatest robotic challenges in manufacturing automation [47]. As many different combinations of conditions can be present in this problem, the proposed methods that tend to solve this task also cover most of the robotic grasping issues. The bin-picking problem in some cases can be eliminated in roots by avoiding situations where parts mix together as object placement in some part of the manufacturing process is structured. If object placements are structured at least along Z axes, relatively non-complex computer vision algorithms can be applied to pick and place them [48]. However, in many cases, it is not possible, as it requires modifying the manufacturing plant. Some factories lack manufacturing floor space, as complex and relatively big object dividers or shakers should be installed, and this excludes plant modification. The next aspect is high plant adaption expenses, which in some situations are not sustainable, and eventually, the plant is hardly adjustable if product design changes. Therefore, many factories end up with tasks where randomly placed objects that are located in the bin must be picked and placed by hand. This is where the vision-based grasping and automated bin-picking solution can be applied. Vision-based grasping approaches can be divided into analytic or data-driven methods where analytic methods focus on analyzing the shape of the target object, whereas data-driven methods are based on machine learning [49]. Learning-based approaches tend to be more generic and flexible in dealing with uncertainties of the environment and therefore also more promising in the smart manufacturing context. Model-based and model-free learning-based grasping approaches can be distinguished, respectively, working with known or unknown objects. Typically the first step in the model-based grasping approach is object detection, which is followed by pose estimation, grasp pose determination and path planning. For  
Figure 3: Some of the possible combinations of different conditions: (**a**) single object [35]; (**b**) multiple, similar and overlapping objects [36]; (**c**) complex, multiple, different and divided objects [37], (**d**) multiple, different and overlapping objects [38]. model-based solutions, the optimal gripping point usually can be defined beforehand by an expert whether it is a centre for simple shaped objects or a specific part of the object [50]. Therefore, the overall goal is to find an object that could be picked with the highest confidence and without collisions with the environment. Different approaches have been proposed throughout the years to estimate the 6D pose of the object in cluttered scenes [50; 51; 52]. Several multi-stage and hybrid approaches proceed with 2D CNN-based object detection algorithms such as SSD [53], YOLO [54], LINEMOD [51], etc., followed by an additional neural network for pose-estimation of the detected object. In addition, reverse methods are proposed by segmenting objects first and then finding the location [55; 56] and estimating the position. These methods are effective but also comparatively slow due to multi-stage architecture. Single-shot 6D pose-estimation using only RGB images [57] followed by PnP [58] shows a decrease in time and is accurate for grasping objects in the household environment; however, single-shot methods lack comprehensive industrial use-case studies. When dealing with unknown objects, the approach and respective methods change.