In the rapidly evolving landscape of smart manufacturing, safety has emerged as a critical and non-negotiable core element. With the implementation of regulations such as the EU Machinery Regulation 2023/1230 and the advancement of China’s “Implementation Plan for Promoting Equipment Renewal in the Industrial Sector,” the demand for safe production is growing at an unprecedented pace. In application scenarios like human-machine interaction, various operating machine tools, storage and retrieval systems in automated warehouses, and logistics production lines, the deployment of safety protection devices is particularly vital. From complex human-robot collaboration scenarios to anti-collision systems for mobile robots, every detail impacts production efficiency and personnel safety. As technology advances at a high speed, AI robots have become indispensable components in modern manufacturing. They not only enhance production efficiency but also improve the precision and repeatability of processes, thereby significantly boosting product quality and consistency. The applications of AI robots span a wide range of industries, including automotive manufacturing, electronic assembly, and food processing, playing a crucial role in reducing labor intensity, lowering production costs, and enhancing operational safety. However, as AI robots and smart production lines become more widespread, safety concerns are increasingly coming to the forefront. Risks associated with AI robot operations, such as accidental collisions and equipment failures, can potentially cause harm to operators. Therefore, designing and implementing effective safety protection devices in smart production lines is essential. These devices not only protect personnel from injuries but also ensure the stable operation of equipment, creating a secure and reliable working environment. By integrating advanced safety technologies into smart production lines, potential hazards can be minimized, ensuring the smooth progression of production activities. In summary, the importance of AI robots in modern manufacturing is undeniable, and smart production lines play a key role in industrial talent development. Simultaneously, the design and application of safety protection devices are critical for the safe and efficient operation of intelligent training production lines.
The current state of safety protection devices in AI robot smart production lines reveals that many developed countries have taken the lead in related research. These nations have achieved significant results in the design of safety protection devices and have established comprehensive industrial robot safety standard systems. For instance, the international standard ISO 10218 provides guidelines for robot safety requirements within a certain scope, offering essential direction for the safe operation of industrial robots. On the technological front, some advanced enterprises and research institutions continue to explore the use of cutting-edge sensor technologies, such as vision sensors and force sensors, to enable real-time monitoring of robot work areas. This allows for the timely detection of potential safety hazards and the implementation of appropriate measures. For example, certain German industrial robot manufacturers have integrated high-precision laser scanning sensors into their high-end training production line products, which can accurately sense when personnel enter hazardous areas and promptly halt robot operations to ensure safety. However, this research has limitations, including high costs that make it difficult for small and medium-sized enterprises or educational institutions to widely adopt these advanced safety protection devices in their training production lines. Additionally, compatibility between different brands and models of robots and safety protection devices needs further improvement, as misjudgments or delayed responses may occur in complex industrial and training environments. In recent years, with the rapid development of China’s manufacturing sector and increased emphasis on training for AI robot applications, domestic research on safety protection devices for AI robot smart production lines has grown. In terms of standard setting, China has actively referenced international advanced standards and developed its own national and industry standards, such as GB/T 37395, providing a basic framework for safety protection device design. In technological research and development, some domestic universities and research institutions are dedicated to creating low-cost, high-reliability safety protection devices tailored to China’s context. For instance, a research team at one university developed a safety protection system based on a combination of infrared thermal imaging and ultrasonic sensors, which can monitor the environment around training production lines to some extent at a relatively low cost. However, compared to international counterparts, domestic research is still in a catch-up phase, with room for improvement in sensor accuracy, system integration, and intelligence levels. Particularly in addressing the diverse and complex tasks of AI robot training and compatibility issues with different robot brands, further in-depth research is needed to meet the growing safety protection demands of AI robot smart training production lines.
| Standard | Scope | Key Requirements | Applicability to AI Robots |
|---|---|---|---|
| ISO 10218 | Industrial robot safety | Risk assessment, protective measures | High, with guidelines for integration |
| EU Machinery Regulation 2023/1230 | General machinery safety | Essential health and safety requirements | Moderate, requires adaptation for AI specifics |
| GB/T 37395 | Chinese industrial robot safety | Basic safety principles and testing | High, but evolving with AI advancements |
Demand analysis for safety protection devices involves selecting representative AI robot smart production line sites for on-site inspections. These sites can cover enterprises of different scales, various industry sectors (such as automotive manufacturing and electronic processing), and production lines at different stages of development. During field research, we observe the operational states of AI robots in actual training scenarios, including their motion trajectories, speed variations, and operational precision, while meticulously recording potential safety hazards. For instance, we identify collision risk points between robot arms and peripheral equipment or personnel, as well as unexpected situations that may arise during material grasping and placement. Simultaneously, we engage in in-depth discussions with on-site operators, maintenance staff, and training instructors to understand the practical safety issues they encounter daily and their expectations and needs for safety protection devices. Through this field research, we gather the most authentic and direct firsthand data, enabling the design of safety protection devices that better align with real application scenarios and effectively address safety concerns in production and training. We conduct thorough investigations into the characteristics of different types of AI robot smart production lines, including robot models, functions, operation methods, specific production content, and the skill levels and safety awareness of personnel involved. By comprehensively analyzing these factors, we clarify the specific requirements for safety protection devices in various production contexts, such as the required monitoring area范围, response speed, and protection levels. Enhanced human-robot collaboration safety is a priority; the system must react swiftly when personnel enter hazardous zones, controlling equipment to stop. This minimizes the movement distance of industrial equipment, preventing collisions with personnel. High-precision video understanding algorithms and redundant mechanism designs enable the system to accurately detect personnel in hazardous areas, even when obscured by equipment, ensuring safety through efficient identification. Adopting a hardware-software integrated design tailored for safety detection systems allows for out-of-the-box products, reducing overall costs and enabling users to achieve industrial safety upgrades more economically and efficiently.
Safety pain points in AI robot smart production lines include several critical issues. First, the adaptability to complex scenarios is inadequate. AI robot smart production lines encompass a variety of production scenarios, such as robot programming and teaching, fault troubleshooting, and collaborative operations. In each scenario, the interaction patterns between personnel and robots, action postures, and risk points differ. Traditional non-intelligent methods struggle to accurately distinguish between normal interactions and potential collision risks in complex environments, and they often fail to respond quickly within extremely short time frames. Second, functional requirements are difficult to meet. Traditional non-intelligent methods have limitations in functionality and low accuracy. For example, safety sensors may not effectively distinguish between personnel and automated guided vehicles (AGVs) or other mobile automation devices, making it challenging to ensure personnel safety. Moreover, for increasingly popular collaborative robots, even though touch-stop safety solutions are employed, they can still cause some degree of injury to personnel. Third, vulnerabilities are hard to avoid. Traditional non-intelligent methods are prone to loopholes that can lead to significant personal safety hazards. For instance, when using safety sensors, if personnel are in a hazardous area and an external party intentionally or accidentally resets the safety sensor signal, restarting the automation system could injure those in the danger zone. Fourth, traceability functions are lacking. Traditional non-intelligent methods can only issue alarm signals when dangers occur, without clarifying the causes or providing visual images for tracing. This not only poses major challenges for improving factory management levels but also complicates the fair determination of responsibility in workplace injury incidents.
Design strategies for safety protection devices in AI robot smart production lines involve multiple approaches to address these challenges. First, sensor selection and integration are crucial. Sensors are key components of safety protection devices, and their selection directly affects the monitoring effectiveness and reliability of the system. Among various sensor types, we choose those that match the characteristics of AI robot smart training production lines and integrate them appropriately. For example, vision sensors provide intuitive image information, aiding in identifying the position and posture of personnel and objects, but they may be affected in low-light or complex environments. Force sensors can perceive the contact force between robots and external objects, which is vital for preventing collision accidents, but using them alone may not achieve comprehensive monitoring of the entire work area. Therefore, we consider the actual environment of the production line and the operational characteristics of the robots to determine the optimal sensor combination, such as integrating vision sensors with force sensors. By employing suitable signal processing and data fusion techniques, we achieve complementary advantages, enhancing monitoring accuracy and comprehensiveness. The data fusion can be represented by the formula: $$ z = f(x_v, x_f) $$ where \( x_v \) is vision sensor data, \( x_f \) is force sensor data, and \( f \) is the fusion function optimizing detection.
Second, safety control system design is essential. The safety control system is the core component that ensures timely and effective measures are taken in dangerous situations. Designing an efficient and reliable safety control system requires consideration from multiple aspects. First, we define the system architecture, clarifying the relationships and functional divisions between modules, such as sensor interface modules, data processing modules, decision-making modules, and execution modules. Second, we ensure the system’s response speed; upon detecting a hazard signal, it must make accurate judgments and execute corresponding actions within an extremely short time (typically required to be at the millisecond level), such as stopping robot movement or sounding an alarm. Additionally, we focus on the system’s reliability and stability, avoiding false positives or missed detections. This necessitates rigorous testing and validation of the system, adopting appropriate redundant designs and fault-tolerant mechanisms to ensure normal operation under various complex conditions. The response time can be modeled as: $$ t_{\text{response}} = t_{\text{sensing}} + t_{\text{processing}} + t_{\text{actuation}} $$ where each component must be minimized to achieve \( t_{\text{response}} < 100 \, \text{ms} \) for effective safety.
Third, multi-sensor fusion and intelligent algorithm optimization are employed. By utilizing multi-sensor fusion technology, we organically integrate various types of sensors, such as vision sensors, force sensors, and infrared sensors, and process and analyze the collected data through advanced intelligent algorithms. For example, using deep learning algorithms for target recognition and behavior analysis of images captured by vision sensors, combined with contact force information perceived by force sensors, allows for more accurate judgment of potential hazardous situations between personnel and AI robots. This approach of multi-sensor fusion and intelligent algorithm optimization significantly improves the monitoring precision and response speed of safety protection devices, offering innovation in the field of safety protection for AI robot smart training production lines. The optimization can be expressed as: $$ \min_{\theta} L(\theta) = \sum_{i} \| y_i – \hat{y}_i \|^2 $$ where \( L \) is the loss function for AI robot safety predictions, \( y_i \) is the actual safety status, and \( \hat{y}_i \) is the predicted status based on sensor inputs.
Fourth, adaptive safety protection mechanisms are designed. We create an adaptive safety protection mechanism that can automatically adjust the monitoring parameters and response strategies of safety protection devices based on different scenarios, the operational states of AI robots, and the activities of personnel. For instance, in robot programming training scenarios, when personnel are primarily operating near the control console, the safety protection device can appropriately reduce the monitoring frequency of the robot work area to save energy and reduce data processing load. However, when personnel enter the robot work area for debugging or intervention, the device automatically increases the monitoring frequency, strengthening the monitoring of human-robot interactions to ensure safety. This adaptive safety protection mechanism better accommodates the complex and changing environments of AI robot smart training production lines. The adaptation can be described by: $$ \lambda = g(\text{scenario}, \text{robot state}, \text{human activity}) $$ where \( \lambda \) is the adaptive parameter controlling monitoring intensity.
| Sensor Type | Advantages | Disadvantages | Best Use Cases |
|---|---|---|---|
| Vision Sensors | High-resolution imaging, object recognition | Sensitive to lighting conditions | Human detection in well-lit areas |
| Force Sensors | Direct contact measurement, precise force feedback | Limited to point of contact | Collision avoidance in collaborative tasks |
| Laser Scanners | Long-range, high accuracy in distance measurement | High cost, complex integration | Perimeter monitoring of hazardous zones |
| Infrared Sensors | Low cost, works in various light conditions | Lower resolution, affected by heat sources | Basic presence detection in training lines |
Fifth, autonomous planning for obstacle avoidance is implemented. To ensure the safety of operators’ hands, fingers, and arms, modern manufacturing equipment for AI robots is often enclosed in protective cages. If access is required, protective doors open to allow technicians to enter for maintenance. During machine operation, locking mechanisms prevent the doors from being opened. If a protective door opens when it should not, integrated switches or sensors detect this state. In such cases, the system ensures that the machine only resumes operation after the door is fully closed or locked. Autonomous planning for obstacle avoidance enables AI robots operating in collaborative spaces to automatically plan paths to avoid collisions before impacting operators, allowing direct interaction between operators and AI robots in cooperative work environments to complete tasks. If no operators are present in the collaborative workspace, the AI robot operates in a non-collaborative mode. The path planning can be formulated as: $$ \text{minimize} \int_{0}^{T} \| u(t) \|^2 \, dt \quad \text{subject to} \quad \dot{x} = f(x, u), \quad x(t) \notin \mathcal{O} $$ where \( x \) is the robot state, \( u \) is the control input, and \( \mathcal{O} \) represents obstacles including humans.
Sixth, different safety zone modules are划分. Modern safety protection devices feature intelligent detection capabilities, such as light curtain sensors and laser scanners. Light curtain sensors emit and receive infrared beams to form an invisible curtain; when an object interrupts this curtain, the system immediately detects it and triggers the AI robot to stop, preventing limbs from entering hazardous areas. Laser scanners monitor the space around the AI robot in real-time; when they detect personnel approaching dangerous zones, they issue early warnings and implement measures like减速 or stopping, providing comprehensive protection for operators. In stable production states of AI robots, through the coordination of functional safety controllers and external sensors, different safety zone modules can be effectively划分. When external devices or personnel are in different zones, the AI robot responds according to preset measures. For example, in safe zones, the AI robot operates at normal speed to maintain production efficiency; when entering warning zones, it automatically减速 to ensure equipment and personal safety; and upon entering danger zones, it immediately stops working, effectively guaranteeing production safety. Relying on the override control of safety controllers, regardless of the status of teach pendants, controllers, and drives, the safety controller can cut off power at the first sign of anomalous trajectories, halting the machine to protect personnel and property. Simple and reliable recovery mechanisms also allow quick resumption of production from a stopped state. The zone-based safety can be modeled with a piecewise function: $$ v_{\text{robot}} = \begin{cases} v_{\text{normal}} & \text{if } d \geq d_{\text{safe}} \\ v_{\text{slow}} & \text{if } d_{\text{warning}} \leq d < d_{\text{safe}} \\ 0 & \text{if } d < d_{\text{warning}} \end{cases} $$ where \( d \) is the distance to the hazard, and \( v \) is the robot velocity.
Seventh, integration with automation equipment endpoints is achieved. The system can be directly connected to automation equipment endpoints, enabling seamless integration. When hazardous situations arise in actual scenarios, the equipment endpoint possesses strong identification capabilities, allowing it to quickly and accurately recognize danger. Moreover, this endpoint has intelligent differentiation functions, responding only to humans—that is, it can accurately distinguish between people and objects, avoiding unnecessary reactions to non-hazardous items. Additionally, it features a more refined alarm mechanism that can perform secondary alarms based on filtering strategies for position and size. Through this secondary alarm approach, it provides precise guidance for collaborative AI robots, guiding them to perform multi-level减速 operations, ensuring the safety and stability of the entire work process. The integration logic can be encapsulated as: $$ A = \begin{cases} \text{alarm}_1 & \text{if } \text{object} = \text{human} \wedge \text{risk} = \text{high} \\ \text{alarm}_2 & \text{if } \text{object} = \text{human} \wedge \text{risk} = \text{medium} \\ \text{no action} & \text{otherwise} \end{cases} $$ where \( A \) is the alarm action triggered by the AI robot system.
| Strategy | Key Components | Expected Outcome | Challenges |
|---|---|---|---|
| Sensor Selection and Integration | Vision, force, laser sensors | Enhanced monitoring accuracy | Cost and compatibility issues |
| Safety Control System Design | Modular architecture, fast response | Reliable hazard mitigation | Achieving millisecond-level response |
| Multi-Sensor Fusion and AI Algorithms | Data fusion, deep learning | Improved detection and response | Algorithm complexity and training data |
| Adaptive Safety Mechanisms | Scenario-based parameter adjustment | Flexibility in dynamic environments | Real-time adaptation logic |
| Autonomous Obstacle Avoidance | Path planning, collision detection | Safe human-robot collaboration | Computational load and accuracy |
| Safety Zone划分 | Zone-based speed control | Graded safety responses | Defining accurate zone boundaries |
| Automation Endpoint Integration | Seamless connectivity, smart alarms | Efficient and safe operations | Interfacing with diverse equipment |
In conclusion, through this investigation into the practical application of existing safety protection devices in AI robot smart production lines, we have gained a comprehensive understanding of their current status, effectiveness, and existing issues. Safety protection devices play a vital role in ensuring the safe operation of production lines, but they still face numerous challenges. To further enhance the safety of AI robot smart production lines, it is necessary to continuously improve the safety protection system through sensor selection and integration, safety control system design, multi-sensor fusion and intelligent algorithm optimization, adaptive safety protection mechanism design, autonomous obstacle avoidance planning, division of different safety zone modules, and integration with automation equipment endpoints. Only in this way can we better adapt to the rapidly developing demands of modern manufacturing, ensuring the safe and efficient operation of AI robot smart production lines. The overall safety performance can be quantified as: $$ S = \sum_{i=1}^{n} w_i s_i $$ where \( S \) is the total safety score, \( w_i \) are weights for each strategy, and \( s_i \) are individual strategy scores, emphasizing the need for a holistic approach in AI robot environments.