With the rapid advancement of technology, industries such as manufacturing, warehousing, and logistics increasingly apply mobile robots. Unmanned forklifts, as a type of mobile robot, significantly improve work efficiency and reduce labor costs through automation and intelligent operations. However, these high-tech devices also bring potential safety risks. This article will analyze the main risks mobile robots face and explain how unmanned forklifts address these risks using advanced technology and system design
1.Main Risks
1.Collision Risk
During operation, mobile robots may collide with personnel, other robots, or fixed facilities (such as shelves, walls, etc.). Such collisions can result in equipment damage, cargo loss, and even personal injury. To enhance efficiency in warehouses or factories, managers have introduced unmanned forklifts for transporting and stacking goods. These unmanned forklifts navigate through the warehouse along predetermined paths, moving goods to designated locations.
Scenario 1: Sudden Obstacles
A warehouse worker accidentally drops a box onto the path of an unmanned forklift while transporting goods. The unmanned forklift relies on sensors for navigation, but if the sensors fail to detect this sudden obstacle in time, it might collide directly with the box, causing damage to the goods. More seriously, if the worker cannot avoid the collision in time, it could result in personal injury.
Scenario 2: Equipment Malfunction
experiences a malfunction in its sensors or navigation system during operation, it may fail to accurately assess its surroundings. For example, a malfunction in the LiDAR system could prevent the forklift from recognizing an upcoming shelf. As a result, the unmanned forklift could collide with the shelf, causing goods to fall and potentially leading to cargo loss or endangering the safety of workers.
Cause 1: Navigation system malfunction, sensor failure, improper path planning, sudden appearance of personnel or obstacles.
Cause 2: Sensor malfunction
Description: Mobile robots, such as unmanned forklifts, rely on sensors for environmental perception and navigation. When these sensors malfunction, the robot fails to accurately perceive its surroundings, potentially leading to operational errors or safety incidents. During the transport process, if the forklift’s LiDAR sensor is suddenly physically damaged (for example, struck by falling cargo or collision leading to casing rupture), the sensor malfunctions. As a result of the LiDAR malfunction, the forklift may fail to detect surrounding obstacles or other forklifts, potentially colliding with shelves, walls, or other equipment, causing equipment damage. Malfunctioning sensors also fail to recognize nearby personnel, increasing the risk of serious personal injury. Operational errors due to sensor malfunctions can result in incorrect handling, dropping, or crushing of goods, leading to cargo damage.
2.Causes: Sensor damage, external interference (such as strong light, dust, smoke, etc.), electromagnetic interference, etc.
3.Environmental Changes
1.Cause: High-power electromagnetic devices or strong light interference, etc.
High-power electromagnetic devices (such as welding machines, cutting machines, etc.) in the workshop generate strong electromagnetic interference, which can cause vehicle misoperations and sensor malfunctions. Electromagnetic interference may lead to sensor misreporting and missed reporting, causing the robot to inaccurately detect obstacles or perceive non-existent obstacles, thereby increasing operational uncertainty. Affected by electromagnetic interference, robots may lose accurate positioning and navigation capabilities, resulting in route deviation or repeated routes, affecting work efficiency. Robots under electromagnetic interference may operate erroneously, such as sudden acceleration or stopping, increasing the risk of collisions with other equipment and causing equipment damage. Workers operating near high-power devices, if not accurately recognized by the robot, may face the risk of being struck or squeezed, leading to safety incidents.
二、Safety Measures for Unmanned Forklifts
1.Advanced Sensor Technology
- LiDAR (Light Detection and Ranging): Used for distance measurement and obstacle detection, LiDAR can generate high-precision environmental maps, aiding unmanned forklifts in precise positioning and obstacle avoidance. It utilizes laser pulses to measure distances between objects, achieving centimeter-level accuracy to ensure the forklift accurately perceives distance information in its surroundings. High-resolution LiDAR captures subtle environmental changes and details, enhancing positioning and navigation accuracy. With 360-degree omnidirectional scanning, it monitors the environment without blind spots, enabling the forklift to comprehensively perceive obstacles and environmental changes, thereby enhancing safety. Moreover, LiDAR features a high refresh rate, providing real-time environmental awareness data on distance and obstacle information. LiDAR assists unmanned forklifts in making rapid obstacle avoidance decisions in complex and dynamic environments, ensuring vehicle safety. It also exhibits strong resistance to light variations (such as strong light or shadows) and industrial electromagnetic interference, ensuring stable operation in diverse environmental conditions.
- Pallet Recognition Camera: A pallet recognition camera is a device integrating visual recognition technology used to identify and locate pallets and their contents. This technology finds wide application on forklifts, significantly enhancing work efficiency, safety, and operational precision. The pallet recognition camera swiftly identifies the position and orientation of pallets, aiding forklifts in quickly and accurately positioning pallets, thereby reducing operation time and improving efficiency. It can identify key pallet features in the environment and mark them, facilitating precise alignment of the forklift amidst cluttered conditions with personnel and other moving objects. This assists in navigation and precise control unit (MCU) operations for detailed picking and placing to prevent vehicle deviation and collisions. By continuously monitoring pallets and the surrounding environment in real time, the pallet recognition camera helps forklifts avoid collisions, reducing equipment damage and the risk of personnel injury. Real-time image data from the camera ensures stable pallet extraction and placement, preventing goods from falling due to improper handling. By automatically recognizing pallets, forklifts can achieve automated alignment and handling, reducing manual operations, enhancing automation levels, and optimizing logistics and warehouse processes. The camera can recognize pallets of different types and sizes, suitable for various warehouse and logistics applications, thereby enhancing the versatility of forklifts. The application of pallet recognition cameras reduces reliance on operator skills, enabling even novices to perform accurate operations using image data from the camera, thereby simplifying the training process. These cameras can record operational data for each task, providing valuable support for optimizing warehouse management.Flexible Mechanical Collision Prevention: Flexible mechanical collision prevention technology is an advanced safety feature widely used in automated equipment such as mobile robots and unmanned forklifts. This technology employs flexible materials and structural designs to absorb impact forces during collisions, thereby reducing the risk of equipment damage and personnel injury.
2.Efficient Mobile Robot Control System
In unmanned forklifts and mobile robot systems, advanced control technologies are crucial to ensuring efficient and safe operation. Key technologies include SLAM (Simultaneous Localization and Mapping), path planning algorithms, and advanced dynamic recognition calibration algorithms used in the Jiuxing forklift, among others.
1.SLAM (Simultaneous Localization and Mapping): SLAM technology integrates data from laser radar and cameras to construct high-precision environmental maps in real-time. This enables unmanned forklifts to achieve precise localization and navigation in unknown and dynamic environments, ensuring safe operations in complex work scenarios. By fusing data from multiple sensors, SLAM systems achieve centimeter-level positioning accuracy, allowing unmanned forklifts to accurately determine their current position in environments such as warehouses and factories, thereby enhancing operational precision and efficiency. SLAM adapts to environmental changes, such as the appearance of new obstacles or the movement of goods, dynamically adjusting maps and paths to avoid downtime and the time loss associated with replanning.
2.Path Planning Algorithms: By employing dynamic path planning algorithms, unmanned forklifts can automatically adjust their travel routes based on real-time environmental data, avoiding obstacles and hazardous areas. This adaptive capability enables unmanned forklifts to operate efficiently in busy and constantly changing environments. Path planning algorithms not only focus on obstacle avoidance but also optimize travel routes to reduce path length and time, thereby enhancing handling efficiency and overall operational efficiency. Dynamic path planning can proactively identify potential hazardous areas, such as crowded areas or narrow passages, to select the safest travel routes and reduce the risk of accidents.
3.Real-time Monitoring and Remote Control
- Real-time Monitoring System: Using 4G or wireless WiFi, the forklift sensor data is transmitted in real-time over the network to backend servers to monitor the operational status of unmanned forklifts. It promptly alerts in case of abnormal vehicle conditions, facilitating quick troubleshooting to enhance efficiency.
- Remote Control Function: Operators can intervene and operate unmanned forklifts in abnormal states through a remote control system, thereby preventing accidents in a timely manner.
4.Safety Protocols and Training
- Safety Operation Procedures: Establish detailed operating procedures for unmanned forklifts, including startup, operation, parking, and emergency handling steps, to ensure compliance by operators.
- Training and Drills: Provide systematic training for operators and conduct regular safety drills to enhance their ability to respond to emergencies effectively.
- Area Segmentation and Warning Signs: Install clear warning signs and divide the working area of unmanned forklifts into safety zones to remind personnel to be cautious and avoid entering hazardous areas.
Summary
Mobile robots, especially unmanned forklifts, offer significant advantages in improving operational efficiency and reducing costs. However, ensuring their safety is equally crucial. By employing advanced sensor technologies, efficient navigation systems, redundant designs, real-time monitoring and remote control capabilities, network security measures, as well as establishing safety protocols and conducting training, unmanned forklifts can effectively mitigate various safety risks. This ensures safe and efficient operations in industrial, warehousing, and logistics sectors. Looking ahead, continuous technological advancements will further enhance the safety of mobile robots, providing robust support for the development of intelligent and automated systems.
