Medical material intelligent distribution robot system based on dynamic path planning
The intelligent medical supply delivery robot system based on dynamic path planning solves the problems of low flexibility and high maintenance costs of traditional rail-guided mechanical delivery methods, and achieves efficient and flexible delivery of medical supplies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YANTAI HAITAI ROBOT TECHNOLOGY CO LTD
- Filing Date
- 2025-12-13
- Publication Date
- 2026-05-01
AI Technical Summary
In existing hospital medical supply logistics systems, traditional rail-guided mechanical delivery methods are inflexible, rely on static routes for path planning, are prone to aging of mechanical parts, have high maintenance costs, and are difficult to handle emergency delivery tasks.
The system employs a medical supply intelligent delivery robot based on dynamic path planning, which includes a task management module, a delivery robot, a map management system, and a collision avoidance management module. It uses sensors to plan paths in real time, dynamically allocate tasks, and control a dedicated lifting machine to achieve efficient delivery of medical supplies.
It improved the flexibility and timeliness of medical supply distribution, enhanced the accuracy of new task allocation, and reduced the risk of mechanical failure and maintenance costs.
Smart Images
Figure CN121956989A_ABST
Abstract
Description
A medical supply intelligent delivery robot system based on dynamic path planning Technical Field
[0001] This invention relates to the field of delivery robot technology, specifically to an intelligent medical supply delivery robot system based on dynamic path planning. Background Technology
[0002] With the rapid development of 5G, artificial intelligence, autonomous driving, and drones in my country, digital logistics systems have become very mature in various industries. Unmanned express delivery, logistics warehousing robots, food delivery robots, triage robots, and medicine delivery robots are ubiquitous. Currently, the medical supply logistics systems used in hospitals mostly employ traditional rail-guided mechanical logistics products such as railcars and box-type logistics. These products use dedicated vertical lifts installed in nurse stations and specially laid tracks to deliver medical supplies placed in boxes directly to wards and other destinations without obstacles. However, this traditional railcar delivery method mostly relies on manual allocation or fixed robot matching for tasks. The number of railcars is limited and can only be used for point-to-point transportation. Path planning also relies heavily on pre-installed static routes. When faced with new and urgent delivery tasks, the flexibility is low, and the mechanical components on the tracks are prone to aging and failure, resulting in high maintenance costs.
[0003] To address these shortcomings, this invention proposes a medical supplies intelligent delivery robot system based on dynamic path planning. Summary of the Invention
[0004] The purpose of this invention is to provide a medical supplies intelligent delivery robot system based on dynamic path planning, in order to solve the problems mentioned in the background art.
[0005] The objective of this invention can be achieved through the following technical solution: a medical supply intelligent delivery robot system based on dynamic path planning, comprising the following modules: a task management module, used to acquire delivery tasks from medical technology departments, wards, operating rooms, and ICUs, and assign the delivery tasks to each operating delivery robot; a delivery robot, the delivery robot carrying multiple sensors for delivering corresponding medical supplies to designated destinations according to the delivery tasks; a map management system, the map management system including a path planning unit and an elevator control management unit; the path planning unit is used to plan delivery paths based on newly added delivery tasks and the current position of the delivery robots, and the elevator control management unit is used to control the dedicated lifting machines in the planned delivery paths after adding new delivery tasks; and a collision avoidance management module, used to determine whether there are any abnormalities in the planned path based on various sensor data on the delivery path, and to generate avoidance instructions when an abnormality is determined to be in the path.
[0006] Preferably, the task management module operates as follows: it acquires the delivery tasks assigned to each delivery robot currently delivering goods, and acquires the starting point and destination of a newly added delivery task; it calculates the task deviation between each delivery robot and the newly added delivery task based on the starting point and destination of the newly added delivery task; it arranges the acquired deviations between each delivery robot and the newly added delivery task to generate a delivery sequence, and selects the delivery robot with the smallest deviation as the target delivery robot for the newly added delivery task; and it assigns the newly added delivery task to the selected target delivery robot for the newly added delivery task.
[0007] Preferably, the method for obtaining the task deviation is as follows: obtaining the delivery tasks assigned to each delivery robot currently delivering goods and their priorities; setting a priority coefficient for each delivery robot based on the priority of its assigned delivery tasks; calculating a distance factor based on the distance between the current location of each delivery robot and the nearest dedicated lift, the distance between the current location of each delivery robot and the starting point of a new delivery task, and the remaining path distance of the currently assigned delivery tasks; obtaining the overlap ratio between the current planned path of each delivery robot and the path of the newly assigned task; and calculating the task deviation by combining the priority coefficient, the distance factor, and the overlap ratio.
[0008] Preferably, the map management system operates as follows: the path planning unit replans the delivery path based on the currently planned delivery path of the target delivery robot for the new delivery task, the current real-time location of the target delivery robot for the new delivery task, and the starting and ending points of the new delivery task; the elevator control management unit obtains the dedicated hoist that the target delivery robot for the new delivery task needs to use after the replanned delivery path, and obtains the current usage status of the dedicated hoist to be used and adjusts the dedicated hoist to be used.
[0009] Preferably, the method for regulating the dedicated elevators to be used is as follows: The number and operating status of the dedicated elevators to be used are obtained; an operating status indicator value is set according to the operating status of the dedicated elevators to be used; the current location or operating direction of the dedicated elevators to be used is obtained; based on the floor interval between the location or operating direction and the target delivery robot of the new delivery task, the deviation value of the dedicated elevator is obtained; the occupancy value of the dedicated elevators to be used is obtained based on whether they are being used by other delivery robots; a comprehensive regulation coefficient for the dedicated elevators to be used is calculated by combining the operating status indicator value, elevator deviation value, and dedicated elevator occupancy value; the obtained comprehensive regulation coefficient is compared with a set regulation threshold; and based on the comparison result, the corresponding dedicated elevator is regulated or the delivery task of a delivery robot with a lower priority that is currently using a dedicated elevator is suspended.
[0010] Preferably, the process of comparing the acquired comprehensive control coefficient with the set threshold is as follows: when the comprehensive control coefficient is less than or equal to the control threshold, it is determined that the current dedicated hoist can be scheduled first. The elevator control management unit sends a reservation instruction to the dedicated hoist, locks the dedicated hoist's docking permission at the floor where the target delivery robot of the new delivery task arrives, and marks the scheduling status of the hoist as ready for use; when the comprehensive control coefficient is greater than the control threshold, it is determined that the current dedicated hoist cannot be scheduled temporarily. The elevator control management unit prioritizes searching for backup dedicated hoists in the hospital that match the target delivery path. If a backup hoist exists, the above comprehensive control coefficient calculation steps are repeated to select other dedicated hoists for scheduling; if the comprehensive control coefficient of all dedicated hoists is greater than the control threshold, a coordination instruction is sent to other delivery robots of the dedicated hoist with the smallest comprehensive control coefficient. According to the task priority of other delivery robots, the delivery tasks of delivery robots with lower task priority that are currently using dedicated hoists are suspended, and the docking floor order of the dedicated hoists currently using delivery robots with lower task priority is adjusted.
[0011] Preferably, the multiple sensors include lidar, image sensors, ultrasonic radar, and inertial navigation sensors. The collision avoidance management module operates as follows: it continuously acquires various data of the delivery robot within the dedicated horizontal channel through multiple sensors; it preprocesses the acquired data and constructs a real-time environmental obstacle coordinate system within the dedicated channel; it extracts features from the preprocessed data, distinguishes obstacle types within the dedicated channel, and marks obstacle dimensions and the planned movement trajectory of the delivery robot within the dedicated channel; it calculates the estimated contact time based on the relative distance, relative speed, and channel width between the obstacle and the delivery robot, and sets corresponding avoidance strategies based on the determined risk level; the avoidance strategies include: when the risk is determined to be low, the delivery robot fine-tunes its speed to avoid the obstacle; when the risk is determined to be medium, it decelerates and plans a local detour path within the channel; when the risk is determined to be high, it stops urgently and sends a warning signal to the map management module.
[0012] Preferably, the path planning unit operates as follows: acquiring the real-time location of each delivery robot, the start and end points of new tasks, obstacles appearing on the map, and the location information of dedicated lifts, and constructing a basic dataset for path planning; based on the constructed basic dataset, planning the initial path from the current location of each delivery robot to the end point, including dedicated lift transfer nodes; adjusting the initial path to avoid congested or conflicting road sections based on the real-time location of other robots and temporary obstacle information; and decomposing the optimized path into multiple segmented instructions such as turning, speed, and dedicated lift stopping, and synchronously sending them to the control systems of each delivery robot.
[0013] The beneficial effects of the present invention are as follows: 1. The present invention obtains the medical supply delivery tasks currently assigned to each delivery robot, rationally allocates new delivery tasks to each robot, and dynamically plans delivery paths and controls corresponding dedicated lifting machines in real time based on the new delivery tasks. Furthermore, it adjusts the delivery paths in real time based on the driving data of the delivery robots in the dedicated channels, thereby achieving dynamic and efficient delivery of medical supplies and improving the accuracy of new task allocation, path flexibility, and delivery timeliness.
[0014] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description
[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 is a system module diagram of a medical supplies intelligent delivery robot system based on dynamic path planning according to the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Please refer to Figure 1. This invention is a medical supply intelligent delivery robot system based on dynamic path planning, including a task management module, delivery robots, a map management system, and a collision avoidance management module. The task management module is mainly used to acquire delivery tasks from medical technology departments, wards, operating rooms, and ICUs, and to allocate these tasks to the various operating delivery robots. Medical technology departments mainly include places such as sterilization centers, pharmacies, and supply rooms used to store medicines and medical equipment. By using delivery robots to transport medical supplies from medical technology departments to ICUs, wards, and operating rooms through dedicated lifts and dedicated channels, the system can safely and efficiently meet the needs of timely delivery of large quantities of medical supplies within and across hospital buildings. Both the dedicated lifts and dedicated channels can accommodate multiple delivery robots with standard 30-50L turnover boxes.
[0019] The task management module operates as follows: It obtains the assigned delivery tasks for each delivery robot currently delivering goods, the start and destination points of any newly added delivery tasks, calculates the task deviation between each delivery robot and the newly added task based on these points, arranges the obtained deviations to generate a delivery sequence, selects the robot with the smallest deviation as the target robot for the new task, and assigns the new task to the selected target robot. The task deviation is obtained by: acquiring the assigned delivery tasks and their priorities for each delivery robot currently delivering goods; setting a priority coefficient for each robot based on its assigned priority; calculating a distance factor based on the distance between each robot's current location and the nearest dedicated lift, the distance between each robot's current location and the start point of the new task, and the remaining path distance of each robot's currently assigned delivery tasks; and obtaining the overlap ratio between each robot's current planned path and the path of the newly assigned task. Finally, it calculates the task deviation by combining the priority coefficient, distance factor, and overlap ratio, using the formula: in, For task deviation, This represents the priority coefficient for each delivery robot. A higher priority coefficient indicates a higher priority for the robot's current task and a less likely robot will be assigned new tasks. This is the distance factor. The larger the distance factor value, the farther the delivery robot is from the starting point of the new delivery task, and the longer it will take to reach the destination of the new delivery task. This represents the overlap ratio between the current planned path and the newly assigned task path for each delivery robot. A higher overlap ratio indicates that the existing delivery tasks and the new delivery tasks are more conveniently matched, resulting in a higher degree of compatibility. For example, given two delivery robots, one of which already has a high-priority delivery task with a priority coefficient of [value missing]. Distance factor Overlap ratio ,but: The other delivery machine currently has a low-priority delivery task, with a priority coefficient of [missing value]. Distance factor Overlap ratio ,but: After sorting the task deviation of each delivery robot, the delivery robot with a deviation of 0.067 is suitable for the current new delivery task. Therefore, the delivery robot with a deviation of 0.067 is selected as the target delivery robot for the new delivery task.
[0020] Delivery robots, equipped with multiple sensors, deliver medical supplies to designated destinations based on delivery tasks. Utilizing private cloud technology, these robots achieve full signal coverage during transport, preventing data loss and ensuring complete controllability throughout the process. Furthermore, they are all equipped with solid-state batteries providing a 200km range and employ contactless wireless charging to completely eliminate the risk of electrical sparks from contact oxidation. Currently, all medical robots use ternary lithium batteries, but traditional ternary lithium batteries pose fire safety hazards such as spontaneous combustion and thermal runaway. Regulations prohibit the use of traditional ternary lithium batteries in public places. Solid-state batteries are used for parking and charging, which completely solves the safety risks and battery life issues of using robots in hospital environments. Hospitals can use the robots for delivery with complete safety and worry-free battery life. Each delivery robot is equipped with LiDAR, image sensors, ultrasonic radar, and inertial navigation sensors. LiDAR is mainly used for long-distance detection, environmental modeling, localization, and obstacle detection. The vision sensor is mainly used for obstacle recognition, enhanced localization, and video surveillance. The ultrasonic radar is mainly used for short-distance detection and emergency obstacle avoidance. The inertial navigation is mainly used for accurate perception of vehicle position and attitude, and high-precision combined navigation.
[0021] The map management system includes a path planning unit and an elevator control unit. The path planning unit acquires the real-time location of each delivery robot, the start and end points of new tasks, obstacles appearing on the map, and the location information of dedicated elevators to construct a basic path planning dataset. Based on this dataset, it plans the initial path for each delivery robot from its current location to the end point, including dedicated elevator transfer nodes. According to the real-time location of other robots and temporary obstacle information, it adjusts the initial path to avoid congested or conflicting sections. The optimized path is broken down into multiple segmented commands for turning, speed, and dedicated elevator stopping, which are simultaneously sent to the control systems of each delivery robot. The elevator control unit obtains the dedicated elevators required for the target delivery robots in the newly added delivery tasks after the replanning of the delivery path. The system obtains the current usage status of the dedicated elevators to be used and adjusts the dedicated elevators accordingly. The method for adjusting the dedicated elevators is as follows: Obtain the number and operating status of the dedicated elevators to be used; set the operating status indicator value for the dedicated elevators to be used based on their operating status; obtain the current location or direction of the dedicated elevators to be used; obtain the elevator deviation value based on the floor interval between the location or direction and the target delivery robot for the new delivery task; obtain the elevator occupancy value based on whether the dedicated elevators to be used are being used by other delivery robots; and calculate the comprehensive adjustment coefficient for the dedicated elevators to be used by combining the operating status indicator value, elevator deviation value, and dedicated elevator occupancy value using the formula: in, For comprehensive control coefficient, The operating status indicator value is used to reflect the operating status of the dedicated hoist. The better the status, the lower the operating status indicator value. This represents the deviation value of the dedicated lifting machine. The larger the deviation value, the farther the dedicated lifting machine is from the target delivery robot for the new delivery task. This represents the occupancy value of a dedicated elevator. A higher occupancy value indicates that the dedicated elevator is being used by other delivery robots, which has a greater impact on the scheduling of delivery robots for new delivery tasks. For example, the current data for a certain dedicated elevator are as follows: operating status indicator value. The elevator is operating normally and in good condition; the deviation value is [not specified]. The floor spacing between the delivery robots and the newly added delivery task targets is small, reducing elevator occupancy. This means that the current dedicated hoist is about to be idle and has low occupancy; therefore The current data for the other dedicated hoist are as follows: Operating status indicator value The current dedicated hoist is in poor condition, and the elevator deviates from its rated value. The floor spacing between the delivery robots and the newly added delivery task targets is large, resulting in high elevator occupancy. It is currently being used by other delivery robots and is currently occupied; then The system compares the combined control coefficient of two dedicated elevators with a control threshold, which is preset to 8.0. When the combined control coefficient is less than or equal to 8.0, the dedicated elevator is determined to be available for priority scheduling. The elevator control management unit sends a reservation instruction to the dedicated elevator, locking its docking permission at the floor where the target delivery robot for the new delivery task arrives, and marking the elevator's scheduling status as "ready for use." When the combined control coefficient is greater than 8.0, the dedicated elevator is determined to be temporarily unavailable for scheduling. The elevator control management unit prioritizes searching for backup dedicated elevators within the hospital that match the target delivery route. If a backup elevator exists, the above steps for calculating the combined control coefficient are repeated to select other dedicated elevators for scheduling. If the combined control coefficient of all dedicated elevators is greater than the control threshold, a coordination instruction is sent to the other delivery robots of the dedicated elevator with the lowest combined control coefficient. Based on the task priority of other delivery robots, the delivery tasks of delivery robots with lower task priority currently using dedicated elevators are suspended, and the docking floor order of the dedicated elevators currently using delivery robots with lower task priority is adjusted.
[0022] The collision avoidance management module is used to determine whether there are any anomalies in the planned path based on various sensor data along the delivery route. When an anomaly is detected, an avoidance command is generated. The working method is as follows: Multiple sensors continuously acquire data on the delivery robot within a dedicated horizontal channel. This data is preprocessed to construct a real-time environmental obstacle coordinate system within the dedicated channel. Feature extraction is performed on the preprocessed data to distinguish obstacle types within the dedicated channel and to mark obstacle dimensions and the planned movement trajectory of the delivery robot within the channel. Based on the relative distance, relative speed, and channel width between the obstacle and the delivery robot, the estimated contact time is calculated. A corresponding avoidance strategy is then implemented based on the determined risk level. Avoidance strategies include: for low-risk situations, the delivery robot slightly adjusts its speed to avoid the obstacle; for medium-risk situations, it decelerates and plans a local detour within the channel; for high-risk situations, it stops immediately and sends a warning signal to the map management module. The estimated contact time is calculated using the formula: in, To estimate the contact time, This represents the real-time straight-line distance between the robot and the obstacle in front of it. For safety redundancy distance, The robot's current forward speed, This represents the moving speed of the obstacle ahead. If the obstacle is stationary, then... For example, when a delivery robot is transporting goods in a dedicated channel, there is a barrier 5 meters in front of it. The parameters are as follows: The robot's forward speed , The safety redundancy distance is set to The estimated contact time is: The estimated contact time range for risk assessment is set to 2 to 5 seconds. When the estimated contact time is less than 2 seconds, it is judged as high risk; when the estimated contact time is greater than 2 seconds but less than 5 seconds, it is judged as medium risk; and when the estimated contact time is greater than 5 seconds, it is judged as low risk.
[0023] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.
Claims
1. A medical supplies intelligent delivery robot system based on dynamic path planning, characterized in that, The system includes the following modules: a task management module, used to acquire delivery tasks from medical technology departments, wards, operating rooms, and ICUs, and assign these tasks to various operating delivery robots; delivery robots, equipped with multiple sensors to deliver corresponding medical supplies to designated destinations based on the delivery tasks; and a map management system, including a path planning unit and an elevator control management unit. The path planning unit plans delivery routes based on new delivery tasks and the current locations of the delivery robots, while the elevator control management unit controls the dedicated lifts in the planned delivery routes after new delivery tasks are added. The collision avoidance management module is used to determine whether there are any abnormalities in the planned path based on various sensor data along the delivery route. When an abnormality is detected, an avoidance instruction is generated.
2. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 1, characterized in that, The task management module operates as follows: it obtains the delivery tasks assigned to each delivery robot currently delivering goods, and obtains the starting point and destination of the newly added delivery task; it calculates the task deviation between each delivery robot and the newly added delivery task based on the starting point and destination of the newly added delivery task; it arranges the obtained deviations between each delivery robot and the newly added delivery task to generate a delivery sequence, and selects the delivery robot with the smallest deviation as the target delivery robot for the newly added delivery task; and it assigns the newly added delivery task to the selected target delivery robot for the newly added delivery task.
3. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 2, characterized in that, The method for obtaining the task deviation is as follows: Obtain the assigned delivery tasks and priorities of each delivery robot currently delivering goods; set a priority coefficient for each delivery robot based on the priority of its assigned delivery tasks; calculate a distance factor based on the distance between each delivery robot's current location and the nearest dedicated lift, the distance between each delivery robot's current location and the starting point of a new delivery task, and the remaining path distance of each delivery robot's currently assigned delivery tasks; obtain the overlap ratio between each delivery robot's current planned path and the path of a newly assigned task; and calculate the task deviation by combining the priority coefficient, distance factor, and overlap ratio.
4. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 1, characterized in that, The map management system operates as follows: the path planning unit replans the delivery path based on the currently planned delivery path of the target delivery robot for the new delivery task, the current real-time location of the target delivery robot for the new delivery task, and the starting and ending points of the new delivery task; the elevator control management unit obtains the dedicated hoist that the target delivery robot for the new delivery task needs to use after the replanned delivery path, and obtains the current usage status of the dedicated hoist to adjust the dedicated hoist to be used.
5. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 4, characterized in that, The method for regulating the dedicated lifting machines required is as follows: obtain the number and operating status of the dedicated lifting machines required; set the operating status indicator value according to the operating status of the dedicated lifting machines required; obtain the current location or operating direction of the dedicated lifting machines required; obtain the deviation value of the dedicated lifting machines based on the floor interval between the location or operating direction and the target delivery robot of the newly added delivery task; obtain the occupancy value of the dedicated lifting machines required based on the usage of other delivery robots. The comprehensive control coefficient of the dedicated hoist to be used is calculated by combining the integrated operation status indicator value, elevator deviation value, and dedicated hoist occupancy value. The obtained comprehensive control coefficient is compared with the set control threshold. Based on the comparison result, the corresponding dedicated hoist is controlled or the delivery task of the delivery robot with lower priority that is currently using the dedicated hoist is suspended.
6. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 5, characterized in that, The process of comparing the acquired comprehensive control coefficient with the set threshold is as follows: When the comprehensive control coefficient is less than or equal to the control threshold, it is determined that the current dedicated hoist can be scheduled first. The elevator control management unit sends a reservation instruction to the dedicated hoist, locks the dedicated hoist's docking permission at the floor where the target delivery robot of the new delivery task arrives, and marks the hoist's scheduling status as ready for use. When the comprehensive control coefficient is greater than the control threshold, it is determined that the current dedicated hoist cannot be scheduled temporarily. The elevator control management unit prioritizes searching for backup dedicated hoists in the hospital that match the target delivery path. If a backup hoist exists, the above comprehensive control coefficient calculation steps are repeated to select other dedicated hoists for scheduling. If the comprehensive control coefficient of all dedicated hoists is greater than the control threshold, a coordination instruction is sent to other delivery robots of the dedicated hoist with the smallest comprehensive control coefficient. According to the task priority of other delivery robots, the delivery tasks of delivery robots with lower task priority that are currently using dedicated hoists are suspended, and the docking floor order of the dedicated hoists currently using delivery robots with lower task priority is adjusted.
7. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 1, characterized in that, The various sensors include lidar, image sensors, ultrasonic radar, and inertial navigation sensors. The collision avoidance management module operates as follows: it continuously acquires various data of the delivery robot within the dedicated horizontal channel through multiple sensors; it preprocesses the acquired data and constructs a real-time environmental obstacle coordinate system within the dedicated channel; it extracts features from the preprocessed data, distinguishes obstacle types within the dedicated channel, and marks obstacle dimensions and the planned movement trajectory of the delivery robot within the dedicated channel; it calculates the estimated contact time based on the relative distance, relative speed, and channel width between the obstacle and the delivery robot, and sets corresponding avoidance strategies based on the determined risk level; the avoidance strategies include: when the risk is determined to be low, the delivery robot fine-tunes its speed to avoid the obstacle; when the risk is determined to be medium, it decelerates and plans a local detour path within the channel; when the risk is determined to be high, it stops urgently and sends a warning signal to the map management module.
8. The intelligent medical supply delivery robot system based on dynamic path planning according to claim 1, characterized in that, The path planning unit operates as follows: it acquires the real-time location of each delivery robot, the start and end points of new tasks, and the location information of obstacles and dedicated lifts on the map to construct a basic dataset for path planning; based on the constructed basic dataset, it plans the initial path from the current location of each delivery robot to the end point, including the dedicated lift transfer node; based on the real-time location of other robots and temporary obstacle information, it adjusts the initial path to avoid congested or conflicting road sections; and it decomposes the optimized path into multiple segmented instructions, such as turning, speed, and dedicated lift stopping, and sends them synchronously to the control systems of each delivery robot.