WO2021051754A1 - 基于物联网和slam技术的智能医疗物资补给机器人 - Google Patents

基于物联网和slam技术的智能医疗物资补给机器人 Download PDF

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WO2021051754A1
WO2021051754A1 PCT/CN2020/078277 CN2020078277W WO2021051754A1 WO 2021051754 A1 WO2021051754 A1 WO 2021051754A1 CN 2020078277 W CN2020078277 W CN 2020078277W WO 2021051754 A1 WO2021051754 A1 WO 2021051754A1
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robot
things
internet
slam technology
intelligent medical
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PCT/CN2020/078277
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English (en)
French (fr)
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秦传波
林靖殷
曾军英
王璠
梁中文
宋子玉
何伟钊
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五邑大学
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0234—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons
    • G05D1/0236—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons in combination with a laser
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B25J9/1666—Avoiding collision or forbidden zones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G12/00—Accommodation for nursing, e.g. in hospitals, not covered by groups A61G1/00 - A61G11/00, e.g. trolleys for transport of medicaments or food; Prescription lists
    • A61G12/001—Trolleys for transport of medicaments, food, linen, nursing supplies
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J5/00—Manipulators mounted on wheels or on carriages
    • B25J5/007—Manipulators mounted on wheels or on carriages mounted on wheels
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
    • G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
    • G05D1/0253—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/30—General characteristics of devices characterised by sensor means

Definitions

  • the supply of medical supplies is an indispensable part of the daily operation of the hospital. Medical services such as seeing a doctor, checking up, and being hospitalized are accompanied by the consumption of medical supplies. However, the number of hospitals and hospitals is large every day, the consumption of various medical materials is huge, and the storage space of medical materials such as hospital pharmacies is limited. Therefore, frequent replenishment of medical supplies is required, and statistics on the use of various materials are also difficult. Through cloud data center monitoring, the material replenishment robot can replenish medical materials in time and make statistics on their usage.
  • the purpose of the present invention is to solve at least one of the technical problems existing in the prior art, and to provide an intelligent medical material supply robot based on the Internet of Things and SLAM technology, which can replace medical staff to autonomously carry medical materials to a designated location, save manpower and improve Transportation efficiency.
  • the environment perception module is provided with a binocular camera and a lidar, the binocular camera obtains image information by real-time shooting, and the lidar obtains map information by sensing spatial information;
  • the data processing module is used to analyze the image information captured by the binocular camera, and perform incremental calculation of the robot pose according to the inter-frame information in the image information, and complete by analyzing the map information sensed by the lidar Judgment of static obstacles and dynamic obstacles;
  • the control module is provided with a central processing unit for receiving and processing the data obtained by the environment perception module and a main control board for controlling the movement module;
  • the cloud data center composed of cloud servers, is used to analyze the material usage at the current time and the past time and transmit the data to the control module.
  • the lidar emits a laser beam, and the laser beam is reflected when it encounters an obstacle.
  • the distance between the robot and the obstacle is calculated by the lidar, and the calculation formula is as follows:
  • K is the proportional coefficient
  • T I is the integral time constant
  • T D is the derivative time constant
  • error (t) is the deviation signal
  • y d (t) is the given value
  • y (t) is the output value.
  • the omni-directional movement of the Mecanum wheel is realized by adopting a forward and inverse kinematics model.
  • FIG. 1 is a schematic block diagram of the structure of an embodiment of the present invention
  • FIG. 2 is a block diagram of the working principle of an embodiment of the present invention.
  • Figure 3 is a schematic diagram of the laser radar triangulation ranging
  • the intelligent medical material supply robot based on the Internet of Things and SLAM technology includes:
  • the data processing module is used to analyze the image information captured by the binocular camera, and perform incremental calculation of the robot pose based on the inter-frame information in the image information, and complete the detection of static obstacles by analyzing the map information sensed by the lidar With the judgment of dynamic obstacles, the robot can achieve the functions of map positioning, composition and automatic obstacle avoidance;
  • the control module is provided with a central processing unit for receiving and processing the data obtained by the environment perception module and a main control board for controlling the movement module, thereby controlling the rotation of the mecanum wheel, and using a control algorithm to control the robot to move stably;
  • the cloud data center composed of cloud servers, is used to analyze the material usage at the current time and the past time and transmit the data to the control module to realize the intelligent dispatch of the material supply robot to the designated location to complete the delivery of medical materials.
  • the lidar emits a laser beam, and the laser beam will be reflected when encountering an obstacle. According to the principle of similar triangles, the true distance between the robot and the obstacle can be calculated.
  • Figure 3 is a schematic diagram of the laser radar triangulation ranging, the calculation formula is as follows:
  • K is the proportional coefficient
  • T I is the integral time constant
  • T D is the derivative time constant
  • error (t) is the deviation signal
  • y d (t) is the given value
  • y (t) is the output value
  • y d ( The difference between t) and y(t) is the error value, that is, the deviation signal.
  • the mecanum wheel adopts an O-rectangular installation method, and the rotation of the wheel can generate a yaw axis rotational torque, and the torque arm of the rotational torque is relatively long.
  • the forward and inverse kinematics model is used to realize the omnidirectional movement of the Mecanum wheel.
  • the forward kinematics model can calculate the motion state of the chassis through the speed of the four wheels, while the inverse kinematics model can calculate the speed of the four wheels according to the motion state of the chassis.
  • the binoculars in the binocular camera are modulated by hardware to be absolutely synchronized and output image data at the same time. Therefore, the image depth information can be obtained, and the relative motion of the robot can be estimated according to the inter-frame matching algorithm, and constructed
  • the error function formed by the reprojection error, using a nonlinear optimization algorithm to adjust the robot pose, has achieved the purpose of outputting high-precision pose for a long time.
  • the data processing module uses the SLAM technology based on the ROS system to construct the map, by calling the current relatively complete map construction open source package Cartographer, using laser and visual odometer information to generate a two-dimensional map, Eliminate errors in the composition process through closed-loop detection.
  • the basic unit for closed-loop detection is submap.
  • a submap is composed of a certain number of laser scans. When inserting a laser scan into its corresponding submap, it will estimate its best position in the submap based on the existing laser scan and other sensor data of the submap. The accumulation of errors in the creation of submaps in a short period of time is considered to be sufficiently small.
  • the scan match strategy in Cartographer takes a window near the estimated pose of the laser scan newly added to the map, and then searches for a possible match of the laser scan in the window. If a good enough match is found, it will The closed-loop constraint of the matching is added to the pose optimization problem.
  • the focus of Cartographer is the creation of local submaps fused with multi-sensor data and the implementation of the scan match strategy for closed-loop detection.
  • a scheduling instruction from a cloud data center when a scheduling instruction from a cloud data center is received, it is first analyzed according to the received perception data and positioning data, a grid map and a cost map are created, and the Astar path planning algorithm is used to complete Search for the global optimal path, and automatically avoid obstacles algorithm Dynamic Window Approach (DWA) in the process of going to the target point.
  • DWA Dynamic Window Approach
  • Figure 4 is a flowchart.
  • the robot's motion trajectory is adjusted in real time to achieve dynamic obstacle avoidance.
  • the DWA algorithm first determines the speed Multiple sets of velocities in the space are sampled, and the trajectory of the robot at these speeds is simulated for a certain period of time. After multiple sets of trajectories are obtained, these trajectories are evaluated and the speed corresponding to the optimal trajectory is selected to drive the robot.
  • the staff of the material storage warehouse loads the required transportation materials for the robot according to the material list displayed on the interactive interface of the robot, and provides each material on the interactive interface.
  • the material marks its storage location on the robot.
  • instructions are given through the interactive interface to make the robot start autonomous transportation.
  • the robot will plan the transportation itinerary according to the distance of the target location of each transportation task and the priority of the task.
  • the interactive interface will display the information of the materials needed to be unloaded.
  • the medical staff will issue instructions through the exchange interface to make the supply robot perform the next task.
  • the medical staff takes away the materials not in the unloading list or not all of them, they also need to register through the interactive interface.
  • the registration information will be uploaded to the cloud data center, and the data center will deploy the supply robot according to the remaining material request and task information. Remaining transportation tasks or return to the warehouse to load and unload materials.
  • the binocular camera and lidar are used to realize map positioning and composition, and the cloud data center according to the material
  • the usage situation dispatches the medical material supply robot in real time.
  • the material supply robot receives the corresponding scheduling information, and uses the path planning algorithm to dynamically avoid obstacles and go to the designated floor for material delivery based on the robot's positioning and map information.
  • the robot can replace medical staff to autonomously carry medical supplies to a designated location, saving manpower and improving transportation efficiency.
  • the cloud data center monitors the situation, it comprehensively considers the urgency of the task, the target distance, the amount of materials and other information, and intelligently dispatches the idle material supply robot to autonomously navigate and transport multiple medical materials to a single or Multiple material points complete the transportation tasks assigned by the cloud data center.
  • the material supply robot can automatically load and unload medical materials, completely replacing traditional human transportation.

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  • Automation & Control Theory (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

本发明公开了一种基于物联网和SLAM技术的智能医疗物资补给机器人，通过双目摄像头和激光雷达来实现地图定位与构图，云端数据中心根据物资使用情况对医疗物资补给机器人进行实时地调度。物资补给机器人接收相应的调度信息，根据机器人定位和地图信息，通过路径规划算法实现动态避障前往指定楼层进行物资投放。

Description

基于物联网和SLAM技术的智能医疗物资补给机器人 技术领域
本发明涉及物资运输技术领域，特别涉及一种基于物联网和SLAM技术的智能医疗物资补给机器人。
背景技术
医疗物资的补给是医院日常运作中必不可少的组成部分。看病、检查、住院等医疗服务都伴随着医疗物资的消耗。而医院每日诊疗人数和住院人数众多，各类医疗物资消耗数量巨大，且医院药房等医疗物资点存储空间有限。因此，需要频繁对医疗物资进行补给，各类物资使用情况的统计也较为困难。通过云端数据中心监控，物资补给机器人便可及时补给医疗物资，并统计其使用情况。
当前国内医院医疗物资运输仍以人工运输为主，往往存在物资补给不及时、物资数量难以统计的问题，而且现在的人工运输方式运输效率不高，消耗医院部分人力资源。
发明内容
本发明的目的在于至少解决现有技术中存在的技术问题之一，提供一种基于物联网和SLAM技术的智能医疗物资补给机器人，可以代替医护人员自主运载医疗物资到指定位置，节省人力并提高运输效率。
根据本发明实施例的基于物联网和SLAM技术的智能医疗物资补 给机器人，包括：
环境感知模块，设有双目摄像头和激光雷达，所述双目摄像头通过实时拍摄获取图像信息，所述激光雷达通过感知空间信息获取地图信息；
数据处理模块，用于分析所述双目摄像头所捕获的图像信息，并根据图像信息中的帧间信息对机器人位姿进行增量式计算，通过分析所述激光雷达所感知的地图信息以完成对静态障碍物和动态障碍物的判断；
运动模块，设有麦克纳姆轮和电机，所述麦克纳姆轮由所述电机驱动；
控制模块，设有用于接收并处理所述环境感知模块获取数据的中央处理器和用于控制运动模块的主控板；
云端数据中心，由云服务器组成，用于对当前时刻和以往时刻的物资使用情况进行分析并将数据传送给所述控制模块。
根据本发明实施例的基于物联网和SLAM技术的智能医疗物资补给机器人，至少具有如下技术效果：通过双目摄像头和激光雷达来实现地图定位与构图，云端数据中心根据物资使用情况对医疗物资补给机器人进行实时地调度。物资补给机器人接收相应的调度信息，根据机器人定位和地图信息，通过路径规划算法实现动态避障前往指定楼层进行物资投放。
根据本发明的一些实施例，所述激光雷达发射激光束，激光束遇 到障碍物会进行反射，通过激光雷达来计算机器人与障碍物的距离，其计算公式如下：
Figure PCTCN2020078277-appb-000001
Figure PCTCN2020078277-appb-000002
Figure PCTCN2020078277-appb-000003
其中，发射角度β为已知量，q为实测距离，s为激光头与镜头的距离，f为镜头的焦距，成像仪中x与s对应。
根据本发明的一些实施例，所述控制模块采用PID调节算法，其控制规律计算公式如下：
Figure PCTCN2020078277-appb-000004
error(t)＝y d(t)-y(t)
其中，K为比例系数，T I为积分时间常数，T D为微分时间常数，error(t)为偏差信号，y d(t)为给定值，y(t)为输出值。
根据本发明的一些实施例，所述运动模块由四个麦克纳姆轮和四个与麦克纳姆轮逐一对应的电机组成，所述麦克纳姆轮在所述主控板控制下由所述电机驱动在水平面上的任意方位移动。
根据本发明的一些实施例，所述麦克纳姆轮的全方位移动采用正逆运动学模型来实现。
根据本发明的一些实施例，所述数据处理模块基于ROS系统的 SLAM技术来构建地图。
根据本发明的一些实施例，所述云端数据中心给定目标点后，先确定机器人当前所处的位置和姿态，通过激光雷达来计算机器人与障碍物的距离，根据障碍物信息转化为能够用于路径规划的栅格地图，使用全局路径规划算法计算机器人当前可移动的最优路径，并在运动过程中不断感知环境信息的改变，使用局部路径规划算法躲避动态障碍物。
本发明的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本发明的实践了解到。
附图说明
本发明的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解，其中：
图1是本发明实施例的结构示意框图；
图2是本发明实施例的工作原理框图；
图3是激光雷达三角测距的原理图；
图4是自动避障算法Dynamic Window Approach(DWA)流程图。
具体实施方式
本部分将详细描述本发明的具体实施例，本发明之较佳实施例在附图中示出，附图的作用在于用图形补充说明书文字部分的描述，使人能够直观地、形象地理解本发明的每个技术特征和整体技术方案，但其不能理解为对本发明保护范围的限制。
在本发明的描述中，需要理解的是，涉及到方位描述，例如上、下、前、后、左、右等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。
在本发明的描述中，若干的含义是一个或者多个，多个的含义是两个以上，大于、小于、超过等理解为不包括本数，以上、以下、以内等理解为包括本数。如果有描述到第一、第二只是用于区分技术特征为目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量或者隐含指明所指示的技术特征的先后关系。
本发明的描述中，除非另有明确的限定，设置、安装、连接等词语应做广义理解，所属技术领域技术人员可以结合技术方案的具体内容合理确定上述词语在本发明中的具体含义。
下面结合附图，对本发明实施例作进一步阐述。
如图1-2所示，根据本发明第一方面实施例的基于物联网和SLAM技术的智能医疗物资补给机器人，包括：
环境感知模块，设有双目摄像头和激光雷达，双目摄像头通过实时拍摄获取图像信息，激光雷达通过感知空间信息获取地图信息；
数据处理模块，用于分析双目摄像头所捕获的图像信息，并根据图像信息中的帧间信息对机器人位姿进行增量式计算，通过分析激光雷达所感知的地图信息以完成对静态障碍物和动态障碍物的判断，使 得机器人达到地图定位、构图和自动避障的功能；
运动模块，设有麦克纳姆轮和电机，麦克纳姆轮由电机驱动，通过编程可实现水平面上的任意方位移动，使得物资补给机器人能够在狭小空间运动；
控制模块，设有用于接收并处理所述环境感知模块获取数据的中央处理器和用于控制运动模块的主控板，进而控制麦克纳姆轮的转动，并使用控制算法控制机器人稳定地运动；
云端数据中心，由云服务器组成，用于对当前时刻和以往时刻的物资使用情况进行分析并将数据传送给控制模块，以实现智能调度物资补给机器人前往指定地点完成医疗物资投放。
利用本机器人可以代替医护人员自主运载医疗物资到指定位置，节省人力并提高运输效率。一旦有物资点任意类型物资余量不足，云端数据中心监测到情况后，综合考量任务的紧急程度、目标距离、物资数量等信息，智能调度空闲物资补给机器人自主导航运输多种医疗物资到单个或多个物资点，完成云端数据中心所下达运输任务。物资补给机器人可实现自主装卸医疗物资，完全代替传统人力运输。
在本发明的一些具体实施例中，激光雷达发射激光束，激光束遇到障碍物会进行反射，根据相似三角形原理即可计算出机器人与障碍物间的真实距离。图3为激光雷达三角测距的原理图，其计算公式如下：
Figure PCTCN2020078277-appb-000005
Figure PCTCN2020078277-appb-000006
Figure PCTCN2020078277-appb-000007
其中，发射角度β为已知量，q为实测距离，s为激光头与镜头的距离，f为镜头的焦距，成像仪中x与s对应。
在本发明的一些具体实施例中，控制模块设有的主控板可以处理中央处理器下达的控制指令，进而驱动麦克纳姆轮的速度以及方位等信息。控制模块采用PID调节算法，其控制规律计算公式如下：
Figure PCTCN2020078277-appb-000008
error(t)＝y d(t)-y(t)
其中，K为比例系数，T I为积分时间常数，T D为微分时间常数，error(t)为偏差信号，y d(t)为给定值，y(t)为输出值，y d(t)与y(t)两者作差为误差值，即偏差信号。
在本发明的一些具体实施例中，运动模块由四个麦克纳姆轮和四个电机组成，在主控板的控制下，四个麦克纳姆轮的速度由四个独立的电机驱动，通过编程可实现水平面上的任意方位移动，使得物资补给机器人能够在狭小空间运动。
在本发明的一些具体实施例中，麦克纳姆轮采用O-长方形安装方式，轮子转动可以产生yaw轴转动力矩，而且转动力矩的力臂也比较长。
在本发明的一些具体实施例中，采用正逆运动学模型来实现麦克 纳姆轮的全方位移动。正运动学模型可以通过四个轮子的速度，计算出底盘的运动状态，而逆运动学模型则是可以根据底盘的运动状态解算出四个轮子的速度。
在本发明的一些具体实施例中，双目摄像头中的双目被硬件调制为绝对同步、同时输出图像数据，因此可获取图像深度的信息，根据帧间匹配算法估计机器人的相对运动，并构建由重投影误差构成的误差函数，使用非线性优化算法对机器人位姿进行调整，已达到长时间输出高精度位姿的目的。
在本发明的一些具体实施例中，数据处理模块采用基于ROS系统的SLAM技术来构建地图，通过调用目前比较完善的地图构建开源包Cartographer，使用激光和视觉里程计的信息来生成二维地图，通过闭环检测来消除构图过程中产生的误差。用于闭环检测的基本单元是submap。一个submap是由一定数量的laser scan构成。将一个laser scan插入其对应的submap时，会基于submap已有的laser scan及其它传感器数据估计其在该submap中的最佳位置。submap的创建在短时间内的误差累积被认为是足够小的。然而随着时间推移，越来越多的submap被创建后，submap间的误差累积则会越来越大。因此需要通过闭环检测适当的优化这些submap的位姿进而消除这些累积误差，这就将问题转化成一个位姿优化问题。当一个submap的构建完成时，也就是不会再有新的laser scan插入到该submap时，该submap就会加入到闭环检测中。闭环检测会考虑所有的已完成创建的 submap。当一个新的laser scan加入到地图中时，如果该laser scan的估计位姿与地图中某个submap的某个laser scan的位姿比较接近的话，那么通过某种scan match策略就会找到该闭环。Cartographer中的scan match策略通过在新加入地图的laser scan的估计位姿附近取一个窗口，进而在该窗口内寻找该laser scan的一个可能的匹配，如果找到了一个足够好的匹配，则会将该匹配的闭环约束加入到位姿优化问题中。Cartographer的重点内容就是融合多传感器数据的局部submap创建以及用于闭环检测的scan match策略的实现。
在本发明的一些具体实施例中，当接收到云端数据中心的调度指令，首先根据接收到的感知数据和定位数据对其进行分析，创建栅格地图和代价地图，使用Astar路径规划算法，完成对全局最优路径的搜索，并在前往目标点的过程中自动避障算法Dynamic Window Approach(DWA)，图4为其流程图，实时调整机器人的运动轨迹实现动态避障，DWA算法首先对速度空间的多组速度进行采样，并模拟机器人在这些速度下一定时间内的轨迹，在得到多组轨迹以后，对这些轨迹进行评价，选取最优轨迹所对应的速度来驱动机器人运动。
在本发明的一些具体实施例中，补给机器人到达相应物资装载区域后，物资存储仓库工作人员根据机器人交互界面所显示的物资清单，为机器人装载所需运送物资，并在交互界面上为每一物资标记其在机器人身上的存放位置。装载完成后，通过交互界面下达指令使机器人开始自主运输。机器人将根据各项运输任务目标位置的远近与任 务的优先级别，规划运输行程。到达指定目标点后，交互界面将显示出所需卸载物资信息，医护人员卸载完成后，通过交换界面下达指令，使补给机器人执行下一项任务。若医护人员取走卸载清单外的物资或未全部取走，也需通过交互界面进行登记，登记信息将上传至云端数据中心，数据中心将根据剩余物资请况和任务信息，调配补给机器人继续完成剩余运输任务或是返回仓库装卸物资。
根据本发明实施例的基于物联网和SLAM技术的智能医疗物资补给机器人，通过上述设置，可以达成至少如下的一些效果：通过双目摄像头和激光雷达来实现地图定位与构图，云端数据中心根据物资使用情况对医疗物资补给机器人进行实时地调度。物资补给机器人接收相应的调度信息，根据机器人定位和地图信息，通过路径规划算法实现动态避障前往指定楼层进行物资投放。利用本机器人可以代替医护人员自主运载医疗物资到指定位置，节省人力并提高运输效率。一旦有物资点任意类型物资余量不足，云端数据中心监测到情况后，综合考量任务的紧急程度、目标距离、物资数量等信息，智能调度空闲物资补给机器人自主导航运输多种医疗物资到单个或多个物资点，完成云端数据中心所下达运输任务。物资补给机器人可实现自主装卸医疗物资，完全代替传统人力运输。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示意性实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含 于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
尽管已经示出和描述了本发明的实施例，本领域的普通技术人员可以理解：在不脱离本发明的原理和宗旨的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由权利要求及其等同物限定。

Claims (7)

  1. 一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于，包括：
    环境感知模块，设有双目摄像头和激光雷达，所述双目摄像头通过实时拍摄获取图像信息，所述激光雷达通过感知空间信息获取地图信息；
    数据处理模块，用于分析所述双目摄像头所捕获的图像信息，并根据图像信息中的帧间信息对机器人位姿进行增量式计算，通过分析所述激光雷达所感知的地图信息以完成对静态障碍物和动态障碍物的判断；
    运动模块，设有麦克纳姆轮和电机，所述麦克纳姆轮由所述电机驱动；
    控制模块，设有用于接收并处理所述环境感知模块获取数据的中央处理器和用于控制运动模块的主控板；
    云端数据中心，由云服务器组成，用于对当前时刻和以往时刻的物资使用情况进行分析并将数据传送给所述控制模块。
  2. 根据权利要求1所述的一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于，所述激光雷达发射激光束，激光束遇到障碍物会进行反射，通过激光雷达来计算机器人与障碍物的距离，其计算公式如下：
    Figure PCTCN2020078277-appb-100001
    Figure PCTCN2020078277-appb-100002
    Figure PCTCN2020078277-appb-100003
    其中，发射角度β为已知量，q为实测距离，s为激光头与镜头的距离，f为镜头的焦距，成像仪中x与s对应。
  3. 根据权利要求1所述的一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于，所述控制模块采用PID调节算法，其控制规律计算公式如下：
    Figure PCTCN2020078277-appb-100004
    error(t)＝y d(t)-y(t)
    其中，K为比例系数，T I为积分时间常数，T D为微分时间常数，
    error(t)为偏差信号，y d(t)为给定值，y(t)为输出值。
  4. 根据权利要求1所述的一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于：所述运动模块由四个麦克纳姆轮和四个与麦克纳姆轮逐一对应的电机组成，所述麦克纳姆轮在所述主控板控制下由所述电机驱动在水平面上的任意方位移动。
  5. 根据权利要求1或4所述的一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于：所述麦克纳姆轮的全方位移动采用正逆运动学模型来实现。
  6. 根据权利要求1所述的一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于：所述数据处理模块基于ROS系统的SLAM 技术来构建地图。
  7. 根据权利要求1所述的一种基于物联网和SLAM技术的智能医疗物资补给机器人，其特征在于：所述云端数据中心给定目标点后，先确定机器人当前所处的位置和姿态，通过激光雷达来计算机器人与障碍物的距离，根据障碍物信息转化为能够用于路径规划的栅格地图，使用全局路径规划算法计算机器人当前可移动的最优路径，并在运动过程中不断感知环境信息的改变，使用局部路径规划算法躲避动态障碍物。
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