Development Of Visual Sensors Research Articles

Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots.

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human-machine-environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions.

Development of Visual Sensors and Augmented Reality Based Smart Glasses for Enhancement of Business Sustainability and Wellbeing of the Aging Workforce

Development of Visual Sensors and Augmented Reality Based Smart Glasses for Enhancement of Business Sustainability and Wellbeing of the Aging Workforce

Retracted] Research Based on Visual Sensors and VR in the Field of Visual Culture

Visual sensors provide us with a wealth of multimedia data; one of the core parts of VR technology is to present people with a real and immersive simulation environment; the application of this technology makes the human narrative to achieve visual transformation and further strengthens the central position of visual culture. This paper studies the field of visual culture from two aspects of visual sensor and VR. In this paper, the HSV color model models task processing problems in visual sensor networks using separable load theory and studies ways to seek optimal task scheduling strategies to minimize the completion time of the visual task. This paper mainly uses literature research method, questionnaire survey method, and statistical analysis method to design a model image output process to explore the application of visual culture under the development of visual sensor and VR and analyzes the development status of film, game, and cultural tourism industry VR in the field of visual culture. Questionnaire data show that compared with traditional video games, more people chose games under the application of VR technology for entertainment; in VR game experience, more than 70% are satisfied with game color and style, operation form, scene design, and experience of VR game itself, which shows to some extent the role of visual sensor and VR in visual culture.

Development of groove recognition algorithm with visual sensor. Practical development of visual sensor for welding robot (1st Report)

Summary To improve the automation or artificial intelligence of welding operations, it is necessary to develop high-performance sensors. Laser visual sensors are designed to recognise the configuration or detect the position of a weld by irradiating a slit light on to the welded joint, receiving the light with a CCD camera, and converting the two-dimensional information into three-dimensional information by image processing. The visual sensors for welding robots must be free from the influences of welding process disturbances, such as arc light, heat, spatter and fume. This paper describes optical component arrangements and image processing algorithms. The optical component of a sensor head helps avoid optical window contamination caused by spatter or fume. The image processor used can process image data quickly and accurately even if welding arc light illuminates the workpiece surface. The accuracy of weld position detection is within ±0.2 mm.