In intelligent manufacturing and smart logistics scenarios, multi-robot collaborative operation of fork-picking robots has become a core mode for improving efficiency. However, the risk of collisions in intensive working environments also increases significantly. To address this issue, a comprehensive protection system integrating "perception-decision-execution-management" across multiple dimensions is needed to ensure the safe and efficient operation of robots in dynamic environments.
Environmental perception is the foundation of collaborative operation. Fork-picking robots need to be equipped with devices such as LiDAR, ultrasonic sensors, and 3D vision cameras to form a triple protection system of "vision + touch + spatial perception." For example, the 3D vision system can identify complex dynamic obstacles, such as temporarily stacked goods or moving pedestrians, and predict their trajectories through deep learning algorithms; LiDAR SLAM navigation technology achieves centimeter-level positioning accuracy by constructing a 3D point cloud map in real time, ensuring the robot's accurate perception of its surroundings. Furthermore, devices such as UWB positioning tags can assist robots in achieving autonomous navigation in dynamic indoor and outdoor environments, avoiding collisions caused by positioning deviations.
Task allocation and path planning are the core of collaborative operation. The central dispatch system dynamically allocates tasks based on robot type, battery level, load, location, and task priority using a bidding algorithm. For example, urgent orders are prioritized for robots with sufficient battery power and the shortest path, while low-priority tasks are handled by idle robots. For path planning, the system dynamically adjusts routes based on real-time traffic data. When multiple robots need to pass through narrow passages, a "virtual traffic light" mechanism allocates right-of-way: mainline vehicles have priority, while branchline vehicles pass sequentially at preset time intervals, and a "resource reservation" algorithm prevents deadlocks. This hybrid architecture of "centralized planning and distributed execution" ensures both a globally optimal solution and endows robots with local obstacle avoidance capabilities.
Dynamic obstacle avoidance and real-time response are crucial for safe operation. During task execution, robots must possess autonomous obstacle avoidance capabilities. By monitoring the surrounding environment in real time, when an obstacle enters the warning zone, an audible warning is issued. If the obstacle continues to approach, the system automatically reduces power output; when a collision is unavoidable, power is instantly cut off and auxiliary braking is activated. For example, some robots are equipped with anti-collision touch edges and emergency stop buttons, which can immediately stop the robot upon collision or manual triggering, ensuring safety. Furthermore, robots must possess path fine-tuning capabilities, adjusting their paths in real time based on local perception information while executing central commands to respond to unexpected situations in dynamic environments.
Traffic control and area protection are the rule-based safeguards for collaborative operations. At bottleneck areas such as intersections and narrow passages, virtual traffic rules, such as traffic lights or priority right-of-way allocation, need to be established to prevent congestion. For example, digital twin technology can be used to simulate freight yard layouts and traffic flow, optimizing path planning algorithms in advance to reduce congestion risks. Speed bumps and warning lights should be installed at intersections such as loading and unloading areas, with audible and visual alarms alerting approaching vehicles. Simultaneously, independent paths should be planned for robots to avoid intersections with pedestrian areas, further reducing collision risks.
Energy management and global optimization support continuous operation. The scheduling system needs deep integration with the battery management system to achieve accurate prediction and optimization of energy consumption. For example, when a robot's battery level falls below a threshold, the system automatically plans the shortest path back to the charging station; in long-distance transport tasks, the task is split and completed by multiple robots in relay, preventing a single vehicle from stopping midway due to insufficient battery power. Furthermore, robots that support charging while running will be prioritized for low-load tasks, extending continuous operation time.
System integration and data interoperability are the key to collaborative operations. Fork-picking robots need to seamlessly integrate with systems such as WMS and MES to achieve automatic task assignment, real-time data upload, and operational status visualization. For example, using low-latency communication technologies such as 5G/WiFi 6, each robot uploads location, speed, and task progress data every second, allowing the scheduling system to dynamically adjust its path. When a robot malfunctions and stops, the system immediately broadcasts a "danger zone" message to surrounding vehicles, which automatically detour to ensure operational continuity.
Safety management and emergency response are the last line of defense. The system must have self-diagnostic and protection functions, monitor the status of critical components in real time, and immediately stop and issue an alarm in case of a malfunction. Simultaneously, it should regularly simulate scenarios such as equipment failure and network outages to verify emergency response capabilities. For example, through stress testing and drills, bottlenecks in production line logistics can be identified in advance, and task allocation strategies can be dynamically adjusted to ensure efficient operation even in emergencies.
