Abstract
In modern intelligent manufacturing environments, the autonomy and adaptability of collaborative robots (Cobots) heavily rely on the environmental perception capabilities of their vision systems. Traditional fixed-position industrial cameras face limitations in field of view, deployment flexibility, and close-range precision observation, making it challenging to meet the diverse requirements for close-range interactive tasks in flexible production lines. To enhance collaborative robots' analysis capabilities in unstructured work scenarios, this study explores integrating an endoscope camera module—featuring an ultra-wide field of view and high resolution—into AI collaborative robot vision systems. This integration aims to leverage the module's unique wide-angle and compact structure to strengthen the robot's overall perception and detailed capture of multi-object, dynamic environments within the workspace, providing richer visual information for complex decision-making.
I. Visual Bottlenecks and Requirements for Collaborative Robots in Flexible Manufacturing
In flexible manufacturing scenarios such as electronics assembly and precision machining, collaborative robots must perform tasks including part picking, fine assembly, initial quality inspection, and safe human-robot collaboration. These tasks demand that the vision system achieve multi-scale perception—from macro-level workspace layout to micro-level component details—within the robot's compact working radius. Traditional vision solutions often face a dilemma: fixed wide-angle cameras offer broad fields of view but lack flexibility and struggle with close-range target observation; cameras deployed at the end of robotic arms (Eye-in-Hand) frequently require posture adjustments to search for targets due to limited field of view, compromising operational fluidity and efficiency. Therefore, a vision module that balances wide field of view, high resolution, and compact size is key to optimizing collaborative robots' close-range operational capabilities.
II. Technical Characteristics of the Imaging Module and Its Perceptual Advantages in Robotic Systems
The imaging module at the center of this research features an optical design that overcomes conventional field-of-view limitations. Employing a fixed-focus design with a focal length of 2.2mm ± 5%, it achieves an ultra-wide field of view (FOV) spanning 190° horizontally, vertically, and diagonally. In collaborative robot work scenarios, this feature means that when deployed at the end of a robotic arm or fixed at a critical position within a work cell, a single imaging pass can cover the vast majority of the robot's operational range. This significantly reduces the scanning movements required to locate or position target objects, thereby enhancing task execution efficiency.
The sensor employs a high-resolution design with an effective pixel count of 3552 (horizontal) x 3576 (vertical). The high pixel density combined with an F2.4±5% aperture ensures images retain rich texture and edge details under typical industrial lighting conditions. This is critical for high-precision pick-and-place operations (e.g., retrieving minute electronic components from trays) and enables preliminary visual inspection of component surface conditions (e.g., scratches, assembly accuracy), enhancing the robot's “hand-eye coordination” capabilities and quality control functions.
The module features a compact physical structure, with key installation dimensions like length, width, and thickness maintained within millimeter-level tolerances (e.g., 30.00±0.2mm, 13.05±0.3mm). This miniaturized design facilitates integration near the end-of-arm tooling (EOAT) of collaborative robots or installation in confined spaces like robot bases or workbench edges without significantly increasing payload or impeding movement. Its interface utilizes a standard 40-pin 0.5mm pitch board-to-board connector (0.5S-2X-26-WB02), facilitating reliable high-speed data connections with robot controllers or dedicated vision processing units.
III. Systematic Enhancement of AI Collaborative Robot System Capabilities Through Module Integration
System-level integration of this ultra-wide-angle imaging module with AI collaborative robots fundamentally reconfigures the robot's environmental perception and task execution logic.
When deployed in an “Eye-to-Hand” configuration (fixed within the workspace), its 190° ultra-wide field of view functions as a global monitoring “sky eye,” continuously providing the robot control system with real-time panoramic imagery of the entire workcell. Based on this, AI algorithms can simultaneously track multiple targets within the workspace (such as moving material carts, human operator positions, and task statuses at different workstations), enabling more efficient task scheduling, more precise monitoring of safe human-robot distances, and more flexible dynamic path planning.
When deployed in an “Eye-in-Hand” configuration (mounted at the end of the robotic arm), the module's wide-field-of-view characteristics prove particularly advantageous during specific task execution. For instance, during part assembly operations, the robot can simultaneously identify the spatial relationship between the assembly base and the component to be installed using a single frame image as it approaches the target workpiece. This eliminates the scanning or perspective adjustment actions required in traditional solutions to obtain complete information. Combined with robotic kinematic models and image distortion correction algorithms, precise hand-eye calibration and 3D spatial position estimation can be achieved based on wide-angle imagery.
Regardless of deployment method, the module's high-resolution, wide-angle video stream provides superior data input for AI vision algorithms (e.g., object detection, semantic segmentation, pose estimation) deployed on the robot. This enables collaborative robots to handle more complex unstructured tasks, such as identifying and grasping specific parts from disorganized bins, inspecting assembly integrity of irregular products, or collaborating safely and efficiently with humans in confined spaces.
IV. Conclusion: Expanding Perceptual Boundaries to Enhance Collaborative Robot Operational Flexibility
By deeply integrating ultra-wide-angle, high-resolution imaging modules into AI collaborative robot systems, this research demonstrates a technical pathway to systematically enhance a robot's environmental adaptability and operational efficiency by expanding the perception range of a single visual sensor. This solution effectively resolves the trade-off between “wide coverage” and “detailed observation” in vision systems for flexible production lines, providing a more robust perceptual foundation for collaborative robots to execute diverse intelligent tasks in complex, dynamic industrial environments.
This integration strategy not only enhances performance in existing tasks like picking, assembly, and inspection but also opens new possibilities for collaborative robots in broader application domains such as precision maintenance, logistics sorting, and laboratory automation. Its core insight lies in the shift of visual component innovation for intelligent equipment—moving away from pursuing extreme performance in a single parameter toward comprehensive optimization across multiple constraints including spatial coverage, detail resolution, deployment flexibility, and cost efficiency.
