A management system for mobile nursing service robots

Abstract

The present invention relates to the technical field of drug delivery, and provides a management system for a mobile nursing service robot, including: a patient information management module determines the obstacle priority and speed priority of the robot in the process of performing drug delivery tasks according to the information of the target patient in the database; a local path planning model determines the corresponding obstacle weight based on the obstacle density response obstacle priority, determines the corresponding speed weight based on the obstacle density response speed priority, generates a corresponding evaluation function in response to the obstacle weight and the speed weight, and selects the best path and the best speed group for controlling the execution of the mobile nursing service robot according to the evaluation function. The adaptive weight of the present invention can match the stability and speed requirements of different emphasis in different situations of drug delivery tasks, and can plan shorter paths in areas with dense obstacles.

Description

Technical Field

[0001] The present invention relates to the technical field of drug distribution, and in particular to a management system of a mobile nursing service robot. Background Art

[0002] With the popularity of mobile nursing service robots in hospitals, in order to reduce the burden of medical staff on drug distribution tasks for hospitalized or hospitalized patients, the demand for mobile nursing service robots that can automatically plan routes to perform drug distribution tasks for hospitalized patients is increasing day by day. However, when performing drug dispensing tasks, the current mobile nursing service robots are difficult to adapt to the different requirements for the stability and speed of their movement process in different situations, resulting in that when patients are in critical conditions, rather than relying on mobile nursing service robots, they rely more on medical staff to run to get medicine. Therefore, mobile nursing service robots are in urgent need of improving their movement process.

[0003] At present, the path planning and motion parameter setting of mobile nursing service robots mostly use global path search algorithms to plan a safe and collision-free path from the current location of the mobile nursing service robot to the target location. However, in the actual hospital environment, there are unknown dynamic obstacles and areas that are not detected by the sensors carried by the mobile nursing service robot itself. Simply using global path planning is not enough to meet the requirements of path planning, which leads to the concept of local path planning.

[0004] Local path planning mainly involves collecting surrounding environment data based on sensors during the movement of the mobile nursing service robot, updating the environmental information around the current position of the mobile nursing service robot, and planning a local path to avoid obstacles in real time, so as to finally reach the target point safely. Most mobile nursing service robots use the dynamic window method as their local path planning algorithm.

[0005] For example, the patent with application number 202410660334.5 discloses a method for indoor path planning of a dual-arm transfer nursing robot, which specifically discloses the use of an improved dynamic window algorithm to plan a local path, and then optimize the global path to plan the local path, and guide the dual-arm transfer nursing robot to find the path, so as to achieve faster and safer transportation of the nursing person. However, the improved dynamic window algorithm does not involve the improvement of the weight of the evaluation function.

[0006] Therefore, how to make the mobile nursing service robot adapt to the different requirements of stability and speed in different situations of drug delivery tasks is a technical problem that needs to be solved. Summary of the invention

[0007] To this end, the present invention provides a management system for a mobile nursing service robot, which sets the obstacle weight and speed weight of the local path planning model according to the obstacle priority, speed priority and obstacle density information, so that the adaptive weight can match the stability and speed requirements of the drug delivery task in different situations with different emphases, and a shorter path can be planned in obstacle-dense areas, thereby improving the intelligence of the mobile nursing service robot's drug delivery task.

[0008] To achieve the above-mentioned object, the present invention proposes a management system for a mobile nursing service robot, comprising a patient information management module, a collector mounted on the mobile nursing service robot, and a local path planning model;

[0009] The patient information management module is provided with a database storing patient vital signs information and diagnosis information, and determines the obstacle priority and speed priority of the mobile nursing service robot in the process of performing the drug delivery task according to the information of the target patient in the database;

[0010] The collector is used to collect the density of obstacles within a corresponding range leading to any target patient;

[0011] The local path planning model includes an obstacle unit, a speed unit and an evaluation function unit, so as to enable the mobile nursing service robot to adapt to the obstacle and speed priority requirements of the drug delivery task during movement;

[0012] The obstacle unit is used to determine a corresponding obstacle weight in response to the obstacle priority based on the obstacle density;

[0013] The speed unit is used to determine a corresponding speed weight in response to the speed priority based on the obstacle density;

[0014] The evaluation function unit is used to generate a corresponding evaluation function in response to the obstacle weight and the speed weight, and select the optimal path and optimal speed group for controlling the execution of the mobile nursing service robot according to the evaluation function.

[0015] Further, the obstacle density includes the number of obstacles in the detection window of the collector, the detection window radius and the predicted minimum distance from the obstacles on the moving path of the mobile nursing service robot, so as to enable the mobile nursing service robot to pass through the obstacle-dense area during its movement;

[0016] The obstacle unit is used to obtain an obstacle weight range that ensures a safe distance, and determine an obstacle weight by combining the obstacle weight range with the number of obstacles and the obstacle priority;

[0017] The speed unit is used to obtain a speed weight range corresponding to a moving path that meets a set indicator in the path set, and determine a speed weight by combining the speed weight range with the number of obstacles, the detection window radius, the predicted minimum distance and the speed priority.

[0018] Furthermore, the obstacle unit is used to obtain a minimum obstacle value and an obstacle range value in the obstacle weight range, obtain a correction value of the number of obstacles with the obstacle priority as an index, and obtain the obstacle weight by adding the minimum obstacle value to a ratio of the obstacle range value to the correction value.

[0019] Furthermore, the speed unit is used to obtain an intermediate value using the speed priority as an exponent of the ratio of the predicted minimum distance to the detection window radius, obtain the minimum speed value and the speed range value of the speed weight range, multiply the intermediate value by the speed range value and the correction value of the number of obstacles, and then add the minimum speed value to obtain the speed weight.

[0020] In the above scheme, the setting of obstacle weight and speed weight of the local path planning model is further made more adaptive to the requirements of stability and speed, and the denser the obstacles are, the smaller the obstacle weight and speed weight are, and the direction weight is relatively larger, so that the mobile nursing service robot adopts a path toward the target point and through the obstacle-dense area. This path is shorter, thereby shortening the execution time of the drug delivery task.

[0021] Furthermore, the management system further comprises an orientation unit;

[0022] The obstacle density also includes the horizontal projection size of the largest obstacle in the detection window of the collector;

[0023] The orientation unit is used to determine an orientation weight according to the obstacle function and the obstacle radius, and the local path planning model substitutes the orientation weight into the evaluation function.

[0024] Furthermore, the management system further includes a path calculation unit;

[0025] The path calculation unit is used to collect speed samples of the mobile nursing service robot, and calculate a path set having multiple local moving paths of the mobile nursing service robots by combining the speed samples with a kinematic model;

[0026] The kinematic model is a model for kinematic modeling of a two-wheel differentially driven mobile nursing service robot or a model for kinematic modeling of a four-wheel mobile nursing service robot.

[0027] In the above scheme, local movement process control of the mobile nursing service robot with various movement modes is realized.

[0028] Furthermore, the patient information management module is mounted on a hospital management system cloud platform, including a diagnosis information unit and a vital sign information unit, and the management system cloud platform is interconnected with a central node of the robot operating system;

[0029] The diagnostic information unit is used to obtain the diagnostic information of the management system cloud platform, including the electronic prescription and the drug inventory management library, and combine the electronic prescription and the drug inventory management library to determine the obstacle pre-priority and speed pre-priority of the drug delivery task;

[0030] The vital sign information unit is used to generate the obstacle priority and the speed priority according to the vital sign information, the obstacle pre-priority and the speed pre-priority;

[0031] The local path planning model is mounted on a node of the robot operating system of the mobile nursing service robot, and the central node and the nodes form a centralized collaborative communication to enable the mobile nursing service robot to obtain the obstacle priority and the speed priority.

[0032] In the above scheme, the hospital's management system cloud platform is interconnected with the central node of the mobile nursing service robot operating system, which enables the system to automatically judge the different emphases of stability and speed.

[0033] Further, the diagnostic information unit is used to combine the electronic prescription and the drug inventory management library to determine the packaging level and the urgency level of the delivered drug;

[0034] The diagnostic information unit is used to set the obstacle pre-priority to be greater than the speed pre-priority to make the movement process of the mobile nursing service robot smoother when it is determined that the packaging level exceeds the safety packaging level for fast delivery, and / or the demand urgency level exceeds the safety urgency level for fast delivery. Otherwise, the obstacle pre-priority is set to be less than the speed pre-priority to make the movement process of the mobile nursing service robot faster.

[0035] Furthermore, the management system further comprises a device for monitoring the patient's vital signs information, which communicates with the central node, wherein the vital signs information comprises at least one of respiration, blood pressure, blood oxygen, body temperature, heart rate, electrocardiogram and blood sugar;

[0036] The vital sign information unit, if it is determined that the vital sign information exceeds the emergency value, increases the speed pre-priority and generates the obstacle priority and the speed priority; otherwise, it generates the obstacle priority and the speed priority that are consistent with the obstacle pre-priority and the speed pre-priority.

[0037] Furthermore, if the vital sign information unit determines that the vital sign information exceeds an emergency value, it obtains a difference between the vital sign information and the emergency value, and determines an amplitude for increasing the speed pre-priority according to the difference.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The obstacle weight and speed weight of the local path planning model are set according to the obstacle priority, speed priority and obstacle density information, so that the adaptive weight can match the stability and speed requirements of drug delivery tasks in different situations, and a shorter path can be planned in areas with dense obstacles, thereby improving the intelligence of the mobile nursing service robot's drug delivery task.

[0040] 2. The obstacle weight and speed weight of the local path planning model can be set to adapt to the requirements of stability and speed. The denser the obstacles are, the smaller the obstacle weight and speed weight are, and the direction weight is relatively larger. As a result, the mobile nursing service robot adopts a path toward the target point and through the obstacle-dense area. This path is shorter, thereby shortening the execution time of the drug delivery task.

[0041] 3. It ensures that when the obstacle is too large, the orientation weight is small, and the obstacle weight and speed weight are relatively larger, so that the mobile nursing service robot will not be unable to quickly bypass the obstacle because it is always heading towards the target point.

[0042] 4. By interconnecting the hospital's management system cloud platform with the central node of the robot operating system, the system can further automatically judge the different emphases of stability and speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the general structure of a management system according to an embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of experimental results of the management system of an embodiment of the present invention using a conventional evaluation function;

[0045] Figure 3 It is a schematic diagram of experimental results of the management system of an embodiment of the present invention using a conventional evaluation function that only improves obstacle weight and speed weight;

[0046] Figure 4It is a schematic diagram of experimental results of the management system of an embodiment of the present invention using an evaluation function with improved three weights;

[0047] Figure 5 Schematic diagram of the ROS navigation system framework of the management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0050] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0051] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] like Figures 1 to 5 As shown, the present invention provides a management system for a mobile nursing service robot, which sets the obstacle weight and speed weight of the local path planning model according to the obstacle priority, speed priority and obstacle density information, so that the adaptive weight can match the stability and speed requirements with different emphases in different situations of the drug delivery task, and a shorter path can be planned in the obstacle-dense area, thereby improving the intelligence of the mobile nursing service robot's drug delivery task.

[0053] in, Figure 1 A schematic diagram of the general structure of a management system according to an embodiment of the present invention; Figure 2 It is a schematic diagram of experimental results of the management system of an embodiment of the present invention using a conventional evaluation function; Figure 3It is a schematic diagram of experimental results of the management system of an embodiment of the present invention using a conventional evaluation function that only improves obstacle weight and speed weight; Figure 4 It is a schematic diagram of experimental results of the management system of an embodiment of the present invention using an evaluation function with improved three weights; Figure 5 Schematic diagram of the ROS navigation system framework of the management system according to an embodiment of the present invention. Embodiment 1

[0054] like Figures 1 to 5 As shown, this embodiment proposes a management system for a mobile nursing service robot, including a patient information management module, a collector mounted on the mobile nursing service robot, and a local path planning model; the patient information management module is provided with a database for storing patient vital signs information and diagnostic information, and the obstacle priority and speed priority of the mobile nursing service robot in the process of performing the drug delivery task are determined according to the information of the target patient in the database; the collector is used to collect the obstacle density within the corresponding range leading to any target patient; the local path planning model includes an obstacle unit, a speed unit and an evaluation function unit, which are used to enable the mobile nursing service robot to adapt to the obstacle and speed priority requirements of the drug delivery task during movement; the obstacle unit is used to determine the corresponding obstacle weight based on the obstacle density in response to the obstacle priority; the speed unit is used to determine the corresponding speed weight based on the obstacle density in response to the speed priority; the evaluation function unit is used to generate a corresponding evaluation function in response to the obstacle weight and the speed weight, and select the best path and the best speed group for controlling the execution of the mobile nursing service robot according to the evaluation function.

[0055] In this embodiment, the collector is a laser radar or a visual sensor.

[0056] Furthermore, the obstacle density includes the number of obstacles in the detection window of the collector, the detection window radius and the predicted minimum distance between the obstacles and the moving path of the mobile nursing service robot, so as to enable the mobile nursing service robot to pass through the obstacle-dense area during its movement; the obstacle unit is used to obtain the obstacle weight range for ensuring a safe distance, and the obstacle weight is determined by combining the obstacle weight range with the number of obstacles and the obstacle priority; the speed unit is used to obtain the speed weight range corresponding to the moving path that meets the set indicators in the path set, and the speed weight range is determined by combining the speed weight range with the number of obstacles, the detection window radius, the predicted minimum distance and the speed priority.

[0057] Furthermore, the obstacle unit is used to obtain a minimum obstacle value and an obstacle range value in the obstacle weight range, obtain a correction value of the number of obstacles with the obstacle priority as an index, and obtain the obstacle weight by adding the minimum obstacle value to a ratio of the obstacle range value to the correction value.

[0058] Furthermore, the speed unit is used to obtain an intermediate value using the speed priority as an exponent of the ratio of the predicted minimum distance to the detection window radius, obtain the minimum speed value and the speed range value of the speed weight range, multiply the intermediate value by the speed range value and the correction value of the number of obstacles, and then add the minimum speed value to obtain the speed weight.

[0059] In the above scheme, the setting of obstacle weight and speed weight of the local path planning model is further made more adaptive to the requirements of stability and speed, and the denser the obstacles are, the smaller the obstacle weight and speed weight are, and the direction weight is relatively larger, so that the mobile nursing service robot adopts a path toward the target point and through the obstacle-dense area. This path is shorter, thereby shortening the execution time of the drug delivery task.

[0060] Furthermore, the management system also includes a direction unit; the obstacle density also includes the horizontal projection size of the largest obstacle in the detection window of the collector; the direction unit is used to determine the direction weight according to the obstacle function and the obstacle radius, and the local path planning model substitutes the direction weight into the evaluation function.

[0061] In the above scheme, it is ensured that when the obstacle is too large, the orientation weight is smaller, and the obstacle weight and speed weight are relatively larger, so that the mobile nursing service robot will not be unable to quickly bypass the obstacle because it is always heading towards the target point.

[0062] Furthermore, the management system also includes a path calculation unit; the path calculation unit is used to collect speed samples of the mobile nursing service robot, and combine the speed samples with a kinematic model to calculate a path set having local moving paths of multiple mobile nursing service robots; the kinematic model is a model for kinematic modeling of a two-wheel differentially driven mobile nursing service robot or a model for kinematic modeling of a four-wheel mobile nursing service robot.

[0063] In the above scheme, local movement process control of the mobile nursing service robot with various movement modes is realized.

[0064] It should be noted that the local path planning model in this embodiment uses the speed model in the kinematic model of the mobile nursing service robot as the calculation model, and the feasible speed (speed group) is set according to the conditions of the mobile nursing service robot itself and the limitations of the environment. There is a range, and then sampling is performed within the speed range, the motion path of each speed is deduced through the kinematic model, and finally the simulated path is scored using the evaluation function to select the optimal path and speed group to control the movement of the mobile nursing service robot. The advantage of the local path planning model of this embodiment is that the reaction speed is fast. As long as the combination of angular velocity and linear velocity is obtained, the motion trajectory at the speed can be deduced.

[0065] When simulating the motion trajectory of the mobile nursing service robot at the next moment, it is necessary to know the motion model of the mobile nursing service robot. The mobile nursing service robot is limited by its own dynamic characteristics and the motion environment, and the sampling speed can be limited to a certain range: the linear speed and angular velocity of the mobile nursing service robot are subject to the maximum and minimum speed limits; the speed of the mobile nursing service robot is also affected by the performance of the motor. Since the torque of the motor has a certain range, the maximum acceleration and deceleration of the mobile nursing service robot is limited. Therefore, in each simulation cycle of the trajectory, the mobile nursing service robot can actually reach a speed range; in order to make the mobile nursing service robot stop before encountering an obstacle, combined with the maximum deceleration condition, the speed of the mobile nursing service robot has a range. The constraint range of the sampled speed sample is the intersection of the above three ranges.

[0066] Specifically, the kinematic model of the two-wheel differentially driven mobile nursing service robot is:

[0067] ,

[0068] In the formula, the next moment is recorded as t+1, and the current moment is recorded as t. is the angle between the center line and the x-axis.

[0069] Specifically, the kinematic model of the four-wheel mobile nursing service robot is:

[0070] ,

[0071] In the formula, point P is the center of mass of the wheeled mobile nursing service robot, d is the distance from point O to point P, b is half the distance between the two rear-drive wheels, 2r is the diameter of the rotating wheel, and θ is the angle between the driving direction of the wheeled mobile nursing service robot and the horizontal direction in the rectangular coordinate system. , Respectively represent the deflection angles of the driving wheels on both sides.

[0072] Specifically, the improved evaluation function in this embodiment is:

[0073] ,

[0074] In the formula, is the normalization coefficient, is the orientation weight, is the setting coefficient, R is the horizontal projection size of the largest obstacle in the detection window, is the orientation function, is the obstacle weight, is the obstacle function, is the speed weight, is the speed function, For the speed group.

[0075] Preferably, in the formula, the coefficient is set When the detection window of the sensor of the mobile nursing service robot is small, the coefficient is set It can be 0.2 to ensure that the orientation weight does not account for too large a proportion.

[0076] It should be noted that the conventional evaluation function is:

[0077] ,

[0078] Among them, the orientation weight , obstacle weight and speed weight are all set values, which leads to poor environmental adaptability of the algorithm. Specifically, since it cannot be adjusted according to the density of obstacles, especially the obstacle weight must be set to a larger value to ensure that it will not hit obstacles, which leads to the robot being unable to directly cross the dense obstacle area and the robot's moving path being longer. The simulation results are as follows Figure 2 shown.

[0079] The normalization coefficient ensures that the three scoring functions are normalized to the interval [0,1], and its expansion is specifically:

[0080] ,

[0081] In the formula, n represents the number of sampled trajectories in one cycle, and i represents the current simulated motion trajectory to be evaluated. It should be noted that by discretizing the velocity sampling and obtaining multiple groups of velocity groups, multiple groups of paths are simulated using the kinematic model to generate a path set. Therefore, the path set and the velocity group are discrete and one-to-one corresponding.

[0082] It is understandable that the scoring function The main function of is to continuously guide the mobile nursing service robot to move towards the target point during the movement of the mobile nursing service robot. Its value can be calculated by the following formula:

[0083] ,

[0084] in, Represents the angle difference between the end of the path simulated by the kinematic model and the target point. Because the change of the speed group of the mobile nursing service robot directly affects the posture state of the mobile nursing service robot, the orientation score function also changes with the change of the speed group, and decreases with the increase of the speed value of the mobile nursing service robot.

[0085] Obstacle scoring function Represents the distance from the path simulated by the kinematic model to the nearest obstacle. The smaller the distance value, the easier it is for the mobile nursing service robot to collide with obstacles; conversely, the larger the value, the farther away from obstacles the path simulated by the current speed group is, and the safer the mobile nursing service robot moves.

[0086] Speed ​​scoring function Represents the moving speed of the mobile nursing service robot.

[0087] Specifically, the calculation formula of the obstacle weight is:

[0088] ,

[0089] In the formula, is the obstacle weight, Represents the minimum value of the obstacle distance scoring function weight when ensuring the minimum safety distance, represents the maximum value of the obstacle distance scoring function weight when ensuring the maximum safety distance, N represents the number of obstacles in the detection window, Represents the obstacle priority. Among them, the minimum and maximum value Obtained from the obstacle weight range.

[0090] Specifically, the speed weight calculation formula is:

[0091] ,

[0092] In the formula, The speed weight is set by taking the maximum safety margin and the fastest speed of the robot in the environment as indicators, and obtaining the values ​​of reaching the target point to set the speed weight range. , and the maximum value of the speed weight range is obtained and minimum value , N represents the number of obstacles in the detection window, represents the speed priority, Represents the minimum distance from the mobile nursing service robot to the nearest obstacle, Represents the radius of the detection window. Therefore, it can be avoided that the speed weight is too large or too small.

[0093] Specifically, the detection window is a detection window of a laser radar sensor, and the obstacle density information can be obtained through scanning of the laser radar.

[0094] Specifically, see Figure 5 The robot ROS (Robot Operating System) navigation system framework, the core part of this navigation framework is the middle move_base function package; the static map is the node on the cloud platform; sensor coordinate conversion, odometer information, sensor information, chassis control are the nodes related to the robot platform. The move_base function package is the core node in ROS navigation. The entire robot navigation system is organized around move_base. It organizes the code in the form of plug-ins, and can replace the improved parts. These include global and local path planners, global and local cost maps, and recovery strategies. In this embodiment, the global path planner uses the AStar algorithm.

[0095] See also Figure 2 , 3 It is obvious from the experimental results of Figure 4 that the trajectory generated by the conventional evaluation function cannot pass through the obstacle-dense area, nor can it bypass larger obstacles. The trajectory generated by the evaluation function with only improved speed weight and obstacle weight can pass through the obstacle-dense area, but cannot bypass larger obstacles. The trajectory generated by the evaluation function with improved speed weight, obstacle weight and orientation weight can pass through the obstacle-dense area, and can bypass larger obstacles, making its planned path shorter, smoother and safer.

[0096] Furthermore, the patient information management module is mounted on a hospital management system cloud platform, including a diagnosis information unit and a vital sign information unit, and the management system cloud platform is interconnected with a central node of a robot operating system (ROS); the diagnosis information unit is used to obtain the diagnosis information of the management system cloud platform, including an electronic prescription and a drug inventory management library, and combine the electronic prescription and the drug inventory management library to determine the obstacle pre-priority and speed pre-priority of the drug delivery task; the vital sign information unit is used to generate the obstacle priority and the speed priority according to the vital sign information, the obstacle pre-priority and the speed pre-priority; the local path planning model is mounted on a node of the robot operating system (ROS) of the mobile nursing service robot, and the central node and the node constitute a centralized collaborative communication to enable the mobile nursing service robot to obtain the obstacle priority and the speed priority.

[0097] In the above solution, the hospital's management system cloud platform is interconnected with the central node of the robot operating system, which enables the system to automatically judge the different emphases of stability and speed.

[0098] Furthermore, the diagnostic information unit is used to combine the electronic prescription and the drug inventory management library to determine the packaging level and the demand urgency level of the delivered drugs; the diagnostic information unit is used to set the obstacle pre-priority level to be greater than the speed pre-priority level to make the movement process of the mobile nursing service robot smoother when it is judged that the packaging level exceeds the safe packaging level for fast delivery, and / or the demand urgency level exceeds the safety urgency level for fast delivery, so as to make the movement process of the mobile nursing service robot smoother; otherwise, the obstacle pre-priority level is set to be less than the speed pre-priority level to make the movement process of the mobile nursing service robot faster.

[0099] Furthermore, the management system also includes a device for communicating with the central node for monitoring the patient's vital signs information, wherein the vital signs information includes at least one of respiration, blood pressure, blood oxygen, body temperature, heart rate, electrocardiogram and blood sugar; the vital signs information unit, if it is determined that the vital signs information exceeds the emergency value, increases the speed pre-priority and generates the obstacle priority and the speed priority; otherwise, it generates the obstacle priority and the speed priority that are consistent with the obstacle pre-priority and the speed pre-priority.

[0100] Furthermore, if the vital sign information unit determines that the vital sign information exceeds an emergency value, it obtains a difference between the vital sign information and the emergency value, and determines an amplitude for increasing the speed pre-priority according to the difference.

[0101] Specifically, the possible value sets of obstacle pre-priority and speed pre-priority are: The diagnostic information unit determines based on the electronic prescription that the patient suffers from an acute and critical injury, such as shock, chest pain, and difficulty breathing, and adopts the last set of pre-priority levels. The diagnostic information unit determines based on the electronic prescription that the patient suffers from a less urgent acute disease, such as a fracture, and adopts the fourth set of pre-priority levels.

[0102] Specifically, the calculation formula of the speed pre-priority is:

[0103] ,

[0104] In the formula, For speed priority, is the difference between the vital sign information and the emergency value, The speed pre-priority is a speed pre-priority. Different types of the vital sign information correspond to different emergency values.

[0105] For example, when the diagnostic information unit determines that the patient has difficulty breathing based on the electronic prescription, the normal heart rate of 60 beats / minute is used as the emergency value, and the difference between the heart rate and the emergency value is obtained. The difference is the absolute value of the heart rate minus the emergency value. The larger the difference, the closer the patient is to stopping breathing, and the higher the speed priority is needed.

[0106] In this embodiment, the obstacle weight and speed weight of the local path planning model are set according to the obstacle priority, speed priority and obstacle density information, so that the adaptive weight can match the stability and speed requirements of the drug delivery task under different circumstances, and a shorter path can be planned in the obstacle-dense area, thereby improving the intelligence and humanization of the mobile nursing service robot drug delivery task. Further, the setting of the obstacle weight and speed weight of the local path planning model based on the dynamic window method can be more adaptive to the stability and speed requirements, and the denser the obstacles, the smaller the obstacle weight and speed weight, and the larger the direction weight, so that the mobile nursing service robot adopts a path toward the target point and through the obstacle-dense area, and this path is shorter, thereby shortening the execution time of the drug delivery task. It is ensured that when the obstacle is too large, the direction weight is small, and the obstacle weight and speed weight are relatively large, so that the mobile nursing service robot will not be unable to quickly bypass the obstacle because it is always facing the target point. Through the interconnection of the hospital's management system cloud platform and the central node of the mobile nursing service robot operating system, the system further realizes the automatic judgment of the different emphases on stability and speed.

[0107] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A management system for a mobile nursing service robot, characterized in that: It includes a patient information management module, a collector mounted on a mobile nursing service robot, and a local path planning model; The patient information management module is provided with a database storing patient vital signs information and diagnosis information, and determines the obstacle priority and speed priority of the mobile nursing service robot in the process of performing the drug delivery task according to the information of the target patient in the database; The collector is used to collect the density of obstacles within a corresponding range leading to any target patient; The local path planning model includes an obstacle unit, a speed unit and an evaluation function unit, so as to enable the mobile nursing service robot to adapt to the obstacle and speed priority requirements of the drug delivery task during movement; The obstacle unit is used to determine a corresponding obstacle weight in response to the obstacle priority based on the obstacle density; The speed unit is used to determine a corresponding speed weight in response to the speed priority based on the obstacle density; The evaluation function unit is used to generate a corresponding evaluation function in response to the obstacle weight and the speed weight, and select an optimal path and an optimal speed group for controlling the execution of the mobile nursing service robot according to the evaluation function; The obstacle density includes the number of obstacles in the detection window of the collector, the detection window radius and the predicted minimum distance from obstacles on the moving path of the mobile nursing service robot, so as to enable the mobile nursing service robot to pass through the obstacle-dense area during its movement; The obstacle unit is used to obtain an obstacle weight range that ensures a safe distance, and determine an obstacle weight by combining the obstacle weight range with the number of obstacles and the obstacle priority; The speed unit is used to obtain a speed weight range corresponding to a moving path that meets a set indicator in the path set, and determine a speed weight by combining the speed weight range with the number of obstacles, the detection window radius, the predicted minimum distance and the speed priority; The obstacle unit is used to obtain the minimum obstacle value and the obstacle range value of the obstacle weight range, obtain the correction value of the number of obstacles with the obstacle priority as an index, and obtain the obstacle weight by adding the minimum obstacle value to the ratio of the obstacle range value to the correction value; The speed unit is used to obtain an intermediate value using the speed priority as an exponent of the ratio of the predicted minimum distance to the detection window radius, obtain the minimum speed value and the speed range value of the speed weight range, multiply the intermediate value by the speed range value and the correction value of the number of obstacles, and then add the minimum speed value to obtain the speed weight.

2. The management system of the mobile nursing service robot according to claim 1, characterized in that: It also includes an orientation unit; The obstacle density also includes the horizontal projection size of the largest obstacle in the detection window of the collector; The orientation unit is used to determine an orientation weight according to the obstacle function and the obstacle radius, and the local path planning model substitutes the orientation weight into the evaluation function.

3. The management system of the mobile nursing service robot according to claim 2, characterized in that: Also includes a path estimation unit; The path calculation unit is used to collect speed samples of the mobile nursing service robot, and calculate a path set having multiple local moving paths of the mobile nursing service robots by combining the speed samples with a kinematic model; The kinematic model is a model for kinematic modeling of a two-wheel differentially driven mobile nursing service robot or a model for kinematic modeling of a four-wheel mobile nursing service robot.

4. The management system of the mobile nursing service robot according to any one of claims 1 to 3, characterized in that: The patient information management module is installed on the hospital's management system cloud platform, including a diagnosis information unit and a vital sign information unit. The management system cloud platform is interconnected with the central node of the robot operating system; The diagnostic information unit is used to obtain the diagnostic information of the management system cloud platform, including the electronic prescription and the drug inventory management library, and combine the electronic prescription and the drug inventory management library to determine the obstacle pre-priority and speed pre-priority of the drug delivery task; The vital sign information unit is used to generate the obstacle priority and the speed priority according to the vital sign information, the obstacle pre-priority and the speed pre-priority; The local path planning model is mounted on a node of the robot operating system of the mobile nursing service robot, and the central node and the nodes form a centralized collaborative communication to enable the mobile nursing service robot to obtain the obstacle priority and the speed priority.

5. The management system of the mobile nursing service robot according to claim 4, characterized in that: The diagnostic information unit is used to combine the electronic prescription with the drug inventory management library to determine the packaging level and the urgency level of the drug to be delivered; The diagnostic information unit is used to set the obstacle pre-priority to be greater than the speed pre-priority to make the movement process of the mobile nursing service robot smoother when it is determined that the packaging level exceeds the safety packaging level for fast delivery, and / or the demand urgency level exceeds the safety urgency level for fast delivery. Otherwise, the obstacle pre-priority is set to be less than the speed pre-priority to make the movement process of the mobile nursing service robot faster.

6. The management system of the mobile nursing service robot according to claim 4, characterized in that: Also included is a device for monitoring the patient's vital signs information, communicating with the central node, wherein the vital signs information includes at least one of respiration, blood pressure, blood oxygen, body temperature, heart rate, electrocardiogram and blood sugar; The vital sign information unit, if it is determined that the vital sign information exceeds the emergency value, increases the speed pre-priority and generates the obstacle priority and the speed priority; otherwise, it generates the obstacle priority and the speed priority that are consistent with the obstacle pre-priority and the speed pre-priority.

7. The management system of the mobile nursing service robot according to claim 5, characterized in that: The vital sign information unit obtains the difference between the vital sign information and the emergency value if it is determined that the vital sign information exceeds the emergency value, and determines the amplitude of increasing the speed pre-priority according to the difference.

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