CN111489023A - Multifunctional intelligent robot system for processing dynamic crew service demand information - Google Patents
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Abstract
The invention discloses a multifunctional intelligent robot system for processing dynamic crew service demand information, which is combined with a wireless communication module to carry out data communication with a cloud end, can receive ordering information transmitted from the cloud end, and carries out real-time path planning and obstacle avoidance through A and an artificial potential field algorithm. And face recognition and ticket feature extraction can be completed through machine vision, and efficient and non-contact ticket checking service is completed. The HMI interface and the voice recognition are carried, so that a good human-computer interaction effect can be provided for the crew and passengers. The system adopts A and an artificial potential field algorithm to plan the path, so that the optimal path can be found while passengers and obstacles are avoided; the intelligent power management is equipped, the power consumption can be compared with the predicted power consumption of the current path according to the electric energy allowance of the battery, whether charging operation is carried out or not is judged, various services can be accurately, safely and efficiently completed by the system, the real-time performance is high, and the application is convenient.
Description
Technical Field
The invention belongs to the technical field of intelligent robots, and particularly relates to a multifunctional intelligent robot system for processing dynamic crew service demand information.
Background
At present, the contents of the train service on most trains are too dispersed and are traditional, such as manual ticket checking, dining car meal supply and the like. The manual ticket checking has large workload and long time consumption, and the ticket escaping and leaking phenomena are easy to occur; the dining car has a small meal supply amount, a train passageway is narrow, crowding is easily caused, most passengers are more prone to ordering and delivering meal in advance by using ordering apps, management is difficult, and resources in stations cannot be effectively utilized. And the service content of the manual service is single.
The optimization algorithm of the multi-machine optimal path and the stochastic obstacle processing algorithm used by the intelligent robot system are fully applied to various route planning systems, are used for solving the optimal path planning problem and are used for dealing with the situation of random roadblocks. Meanwhile, the development of various sensing and detecting technologies provides technical support for the establishment and the perfection of the multifunctional intelligent robot system, and the condition of the host can be monitored in real time and relevant data can be returned. The continuous development of wireless communication of 5G communication provides many solutions for data information transmission at medium and short distances.
By adopting the traditional manual ordering mode, the train is crowded, the management is difficult, resources in the station cannot be effectively utilized, and the problems of omission and the like also exist.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides a multifunctional intelligent robot system for processing dynamic passenger service requirement information, so that the technical problems that the passenger service content on most trains is excessively dispersed and traditional, the train is crowded, the management is difficult, the resources in the trains cannot be effectively utilized, and the omission exists are solved.
To achieve the above object, the present invention provides a multifunctional intelligent robot system for processing dynamic crew service requirement information, comprising: the system comprises a control module, a path planning module, an interaction module and a detection module;
the control module is configured to identify a current target working mode, and acquire information corresponding to the target working mode, where the information corresponding to the target working mode includes: target location and target demand;
the path planning module is used for realizing path planning by adopting an optimization algorithm of a multi-machine optimal path and a stochastic obstacle processing algorithm so as to search an optimal path reaching the target position while avoiding obstacles, and then the control module controls the robot to reach the target position according to the optimal path and executes operation corresponding to the target requirement;
the detection module is used for executing detection operation corresponding to the target requirement and transmitting the acquired detection data to the control module so that the control module transmits the detection data to a cloud for verification;
and the interaction module is used for realizing human-computer interaction.
Preferably, when the target working mode is a food delivery mode, the control module is configured to receive food ordering information of the user and a location of the user sent from the cloud, feed back a food delivery state to the user through the cloud, and then, after the optimal path planned by the path planning module reaches the location of the user, prompt and confirm food delivery through the interaction module.
Preferably, when the target operating mode is the temperature measurement mode, the control module is configured to receive the temperature measurement target position input through the interaction module, measure the temperature of the target object by the detection module after the optimal path planned by the path planning module reaches the temperature measurement target position, and transmit the temperature measurement data to the control module, so that the control module transmits the temperature measurement data to the cloud for verification.
Preferably, when the target working mode is a ticket checking mode, the control module is configured to receive a ticket checking target position input through the interaction module, then shoot the ticket information of the target object and the face information of the target object by the detection module after reaching the ticket checking target position according to the optimal path planned by the path planning module, and transmit the ticket information and the face information to the control module, so that the control module transmits the ticket information and the face information to the cloud for verification.
Preferably, the control module is further configured to determine whether the current battery power headroom meets the current path predicted power consumption, and perform charging route planning by the path planning module when the current battery power headroom does not meet the current path predicted power consumption, so as to perform charging operation.
Preferably, the path planning module includes:
the system comprises a path point acquisition module, a path starting point acquisition module and a path terminal point acquisition module, wherein the path starting point and the path terminal point are acquired from the cloud end by the control module, the path starting point is the current position of the robot, and the path terminal point is a target position;
the system comprises a control module, a road block information acquisition module and a cloud terminal, wherein the control module is used for acquiring basic road block information in a working area from the control module;
the path planning submodule is used for planning paths according to the path starting point, the path terminal point and basic roadblock information in the working area by using an A-x algorithm to obtain an optimal path;
the obstacle capturing module is used for capturing a scene in front of a robot traveling route and positioning a sudden obstacle in the scene;
and the obstacle avoidance module is used for synchronizing the obstacle information obtained in the artificial potential field algorithm to the A-algorithm in real time to assist the robot to adjust the path in time when a new obstacle is introduced so as to realize the obstacle avoidance function.
Preferably, the obstacle avoidance module includes:
the parameter acquisition module is used for setting a target point every other distance of a carriage by taking the terminal point in the optimal path acquired by the path planning sub-module as a starting target point until the distance between the target point and the robot is less than the distance between the carriages, so as to obtain a plurality of target points;
the potential field calculation module is used for calculating an attraction potential field, a basic obstacle repulsion potential field, a corridor left and right boundary repulsion field and a speed repulsion potential field of a sudden obstacle of each target point;
the force magnitude calculation module is used for obtaining the attraction of each target point to the robot from the attraction potential field of each target point, obtaining the repulsion force of the basic obstacles from the repulsion potential field of the basic obstacles, obtaining the total road boundary constraint repulsion force from the repulsion fields of the left and right boundaries of the aisle, and obtaining the speed repulsion force from the speed repulsion potential field of the sudden obstacles;
the resultant force calculation module is used for acquiring the sum of the vectors of the attraction force of each target point to the robot, the repulsion force of the basic obstacle, the total road boundary constraint repulsion force and the velocity repulsion force to obtain resultant force;
and the obstacle avoidance sub-module is used for taking the angle of the resultant force as a target motion angle of the robot, and setting the magnitude of the resultant force as a target motion speed of the robot, so that the control module drives the robot to move according to the target motion angle and the target motion speed, and obstacle avoidance is realized.
Preferably, is prepared from
Determine the repulsive force field U of the left boundary of the aislerL(q) and corridor right boundary potential field UrR(q) wherein krLIs the proportionality coefficient of the repulsive field of the left boundary of the road, krRIs the proportionality coefficient of the road right boundary repulsive field, W is the robot width, rho (q, q)L) Is the vertical distance between the center of mass of the robot and the corresponding point of the left boundary of the passageway, rho (q, q)R) For the vertical distance between the center of mass of the robot and the corresponding point of the right boundary of the aisleQ represents the current position coordinates of the robot system, qLRepresenting the coordinates of the corresponding points of the left boundary of the aisle, qRAnd representing the position coordinates of the corresponding point on the right boundary of the aisle.
Preferably, is prepared from
Determining a fundamental barrier repulsive force potential field Ureq(q) wherein ρn(q,qobs) Is the n-th power of the distance between the robot and the target point, n is a positive coefficient, L is the distance from the center to the top point of the robot, η represents a repulsive force scale factor, qobsRepresenting the coordinates of the position of the obstacle point, p0Indicating the range of influence of the obstacle.
Preferably, is prepared from
Determining velocity repulsive potential field U of sudden obstaclerev(q) wherein kvConstant of velocity repulsion, vor＝|vobs-vrI is the speed of the robot relative to the obstacle and theta is the relative speed vorAngle between position vectors of robot relative to sudden obstacle, vobsRepresenting the speed of the obstacle, vrRepresenting the speed of the robotic system.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the path planning algorithm based on the dynamic roadblock information optimizes an A-algorithm suitable for a static traffic network in the aspect of multiple parallel routes and optimizes a traditional artificial potential field algorithm in the aspect of random roadblock strain capacity, and the two algorithms are combined and applied, so that the system can safely and efficiently move to a target according to a specified route. The integrated software platform design unifies all links of meal searching, ordering, delivering and the like which are not related to each other, reduces the management difficulty, and meanwhile, the software is internally provided with multiple functional services, so that the service of taking a ride is diversified.
Drawings
Fig. 1 is a block diagram of an overall structure of a system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method implementation provided by an embodiment of the present invention;
FIG. 3 is a flow chart of an interaction provided by an embodiment of the invention;
FIG. 4 is a HMI UI interactive interface design provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a velocity repulsion according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The multifunctional intelligent robot system used on the train and capable of completing ticket checking and meal delivery is designed, peripheral equipment can be flexibly added to meet various requirements, a passenger can select service content by using operation software, data are sent to the cloud, the cloud is connected with the control module, and after preparation is completed, the robot system can accurately serve the passenger. The management is more convenient, and the service of passengers is more rapid.
The method comprises the steps that a passenger orders at an ordering client, ordering data are transmitted to a cloud end and stored in a MySQ L database, the ordering data are transmitted to a control module in an intelligent robot system, and automatic opening of a corresponding cabin door in the robot is achieved.
Fig. 1 is a schematic structural diagram of a multifunctional intelligent robot system for processing dynamic crew service requirement information according to an embodiment of the present invention, including: the system comprises a control module, a path planning module, an interaction module and a detection module;
the control module is used for identifying a current target working mode and acquiring information corresponding to the target working mode, wherein the information corresponding to the target working mode comprises: target location and target demand;
the path planning module is used for realizing path planning by adopting an optimization algorithm of a multi-machine optimal path and a stochastic obstacle processing algorithm so as to search an optimal path reaching a target position while avoiding obstacles, and then the control module controls the robot to reach the target position according to the optimal path and then executes operation corresponding to target requirements;
the detection module is used for executing detection operation corresponding to the target requirement and transmitting the acquired detection data to the control module so that the control module transmits the detection data to the cloud for verification;
and the interaction module is used for realizing human-computer interaction.
In the embodiment of the invention, when the target working mode is the food delivery mode, the control module is used for receiving the food ordering information of the user and the position of the user sent from the cloud, feeding back the food delivery state to the user through the cloud, and realizing the food delivery prompt and confirmation through the interaction module after the optimal path planned by the path planning module reaches the position of the user.
In the embodiment of the invention, the detection module comprises a temperature detection unit, an electric energy detection unit and an image detection unit. The interaction module comprises a voice interaction design part and an HMI interaction interface design part.
In the embodiment of the invention, when the target working mode is the temperature measurement mode, the control module is used for receiving the temperature measurement target position input through the interaction module, measuring the temperature of the target object by the detection module after the optimal path planned by the path planning module reaches the temperature measurement target position, and transmitting the temperature measurement data to the control module, so that the control module transmits the temperature measurement data to the cloud for verification.
In the embodiment of the invention, when the target working mode is the ticket checking mode, the control module is used for receiving the ticket checking target position input by the interaction module, then shooting the ticket information of the target object and the face information of the target object by the detection module after the optimal path planned by the path planning module reaches the ticket checking target position, and transmitting the ticket information and the face information to the control module, so that the control module transmits the ticket information and the face information to the cloud for verification.
In the embodiment of the present invention, the control module is further configured to determine whether the current battery power headroom meets the current path predicted power consumption, and perform charging route planning by the path planning module when the current battery power headroom does not meet the current path predicted power consumption, so as to perform charging operation.
Specifically, as shown in fig. 2, a work flow within a control cycle of the system is that a working state of the system at present is judged, if no other work mode is selected, the system defaults to enter a meal delivery work flow, the system receives meal delivery position information transmitted from a cloud, then a meal delivery person confirms through an interactive interface, then the system reaches a destination through path planning and obstacle recognition, a customer receives the meal delivery position information and confirms through the interactive interface, the system performs mode confirmation and electric quantity confirmation after completing a single task, if the work flow is other work modes, a ticket checking mode and a temperature measuring mode are taken as examples in fig. 2, the system firstly confirms through an interactive page, then reaches a specified position through path planning, detects temperature through machine vision or infrared, and finally performs mode rechecking and electric quantity detection after all people are detected.
Wherein, can carry out the transmission of data through 5G wireless communication module between control module and the high in the clouds.
In the embodiment of the invention, the detection module sends the information acquired by the sensor to the control module at a specific frequency to realize real-time feedback of the information, the control module carries out robot path planning according to the information obtained by feedback, and simultaneously the information obtained by feedback is transmitted to the HMI (human machine interface) by a serial port protocol to carry out human-machine interaction display, the human-machine interaction module realizes human-machine interaction with the HMI (human-machine interface) through the voice recognition module, in the cloud end, a passenger transmits required information to the server end through a client, a crew member transmits the information to the server end through the human-machine interaction, and the MySQ L database effectively stores the user and food information and communicates with the server end.
In the embodiment of the invention, as shown in fig. 3, a special user issued to a restaurant waiter can start to log in a page and jump to a dining car management system, order information of a customer is obtained, a cabin door of the dining car can be controlled to be opened, and the dining car is informed to finish loading. The initial login page can register a common account number and enter a food ordering system to order food.
In the embodiment of the invention, after the client finishes ordering, the client feeds the ordering data back to the server and stores the ordering data as historical data into the database. And then the data is transmitted to a control module of the intelligent robot to realize the automatic opening of the corresponding cabin door. After the crew finishes loading the dining car, the robot system automatically detects the loading condition and feeds back the catering real-time information to the client in time through the server.
In the embodiment of the invention, various functions of the HMI (human machine interface) are shown as shown in FIG. 4, wherein the HMI (human machine interface) has three functions of shopping, service and administrator system. The shopping can realize basic functions of man-machine interaction of passengers, such as mobile phone ordering, manual switching and the like. The administrator system can check the system data operation condition and perform cabin door management in real time. Meanwhile, the UI can realize timely transmission and feedback of information, and a user can know system feedback information in time by adopting the UI design.
The HMI (human machine interface) data display part shown in fig. 4 can display the system data information received by the current system sensor, that is, the master control temperature, the electric quantity remaining, the network connection condition, and the module connection condition of the system, and simultaneously shows the on-off state of the door of the current robot food delivery system.
In an embodiment of the present invention, the path planning module includes:
the system comprises a path point acquisition module, a path starting point acquisition module and a path terminal point acquisition module, wherein the path starting point and the path terminal point are acquired from a cloud end by the control module;
the system comprises a road block information acquisition module, a cloud terminal and a control module, wherein the road block information acquisition module is used for acquiring basic road block information in a working area from the control module, and the basic road block information in the working area is acquired from the cloud terminal by the control module;
the roadblock information refers to both the roadblock information of the foundation in the working area, such as two sides of a walkway on a train, the opening state of a vehicle door inside the train, a passage in a train station and the like, and also refers to the dynamic roadblock information captured by a camera in the actual movement process of each intelligent robot, such as the appearance and disappearance of pedestrians and the like. The specific form of these pieces of roadblock information is the polar coordinates of the obstacle particles relative to the robot, i.e. the relative distance and relative angle of the target obstacle to the robot, and for a dynamic roadblock captured by a camera, the speed of the target obstacle relative to the robot.
Generally, the acquisition range of the basic roadblock information in the working area is the whole working area, and the roadblock information of the part does not change greatly in the working process; in the actual movement process of the intelligent robot, the acquisition range of the dynamic roadblock information captured by the camera is small, and the acquisition range is usually the area of a circle with the length of one carriage as the radius and the center of the circle of the robot.
The path planning submodule is used for planning paths by using an A-x algorithm according to a path starting point, a path terminal point and basic roadblock information in a working area to obtain an optimal path;
under the condition that the starting points, the end points and the obstacles are known, the A-x algorithm is mainly used for continuously calculating sum values of surrounding units capable of advancing, selecting the optimal next path through comparing the sum values, and the like to find the optimal path.
Two lists are needed for recording all the blocks considered to find the best path (called open list) and for recording the blocks not considered (called close list). The value G is the moving amount of the starting point from the block, in order to calculate the value G, the system needs to obtain the value G from the front of the system, then add 1, the value G of each block represents the total moving amount of the path formed from the initial point to the block, the value H is the moving amount from the current block to the end point, therefore, the value F of each block, namely G + H, can be obtained, starting from the initial point, the next feasible block is searched, the minimum F block is screened out through F value comparison, then the block is taken as the object, the next traveling route is further screened, and the final traveling route is the optimal solution.
The obstacle capturing module is used for capturing a scene in front of the robot traveling route and positioning a sudden obstacle in the scene;
after the current optimal path is obtained, the system needs to ensure that the system is not influenced by suddenly appearing obstacles in the process of running along the path, so the system starts the machine vision function of the system, a binocular camera which can be composed of two ov7725 cameras returns a scene in front of the rapid scanning robot to the control module, the control module uses a matching cost algorithm to position the obstacles, calculates the relative polar coordinates of the obstacles by using a parallax principle, and calculates the relative speed of the obstacles relative to the robot by means of the variation of the relative polar coordinates of the obstacles within a period of time. And meanwhile, the control module uploads the updated information of the barrier to the cloud.
And the obstacle avoidance module is used for synchronizing the obstacle information obtained in the artificial potential field algorithm to the A-algorithm in real time to assist the robot to adjust the path in time when a new obstacle is introduced so as to realize the obstacle avoidance function.
Wherein, keep away barrier module includes:
the parameter acquisition module is used for setting a target point every other distance of a carriage by taking the terminal point in the optimal path acquired by the path planning sub-module as a starting target point until the distance between the target point and the robot is less than the distance between the carriage, so as to obtain a plurality of target points;
the potential field calculation module is used for calculating an attraction potential field, a basic obstacle repulsion potential field, a corridor left and right boundary repulsion field and a speed repulsion potential field of a sudden obstacle of each target point;
wherein, is composed of Uatt(q)＝0.5ζ/ρ2(q,qgoal) Determining the gravitational potential field of the target point, ζ being the coefficient of attraction, ρ2(q,qgoal) Is the square of the distance between the system and the target. Due to the long and narrow passage inside the train, basically the target points are all in one direction, and the system will move to the nearest target point with the trend of running to the end point. In this dynamic process, each time the robot moves through a target point, the nearest target point is updated to the next target point until the end point is reached.
By
Determining a fundamental barrier repulsive force potential field Ureq(q) wherein ρn(q,qobs) Is the n power of the distance between the robot and the target point, n is a positive coefficient, preferably 2, L is the distance from the center to the top of the robot, η represents a repulsive force scale factor, q isobsRepresenting the coordinates of the position of the obstacle point, p0Representing the range of influence of an obstacle, wherein the distance p between the system and the obstacle is introducedn(q,qobs) And the problem of target unreachability is prevented.
By
Determine the repulsive force field U of the left boundary of the aislerL(q) and corridor right boundary potential field UrR(q) wherein krLIs the proportionality coefficient of the repulsive field of the left boundary of the road, krRIs the proportionality coefficient of the road right boundary repulsive field, W is the robot width, rho (q, q)L) Is the vertical distance between the center of mass of the robot and the corresponding point of the left boundary of the passageway, rho (q, q)R) For the vertical between the center of mass of the robot and the corresponding point of the right boundary of the aisleStraight distance, ρ (q, q)L)＝|q-qL|，ρ(q，qL)＝|q-qR| q denotes the position coordinates of the robot system, qLRepresenting the coordinates of the corresponding points on the left side of the aisle, qRRepresenting the coordinates of the corresponding point on the right side of the aisle
Because the train walkway has the characteristic of narrowness, considering that the system can only run in the boundary line of the aisle, a boundary repulsive force field is introduced, and simultaneously k is changedrLAnd krRThe proportional relationship of (a) can control the relative position (such as left or right) of the system in the train walkway.
By
Determining velocity repulsive potential field U of sudden obstaclerev(q) wherein kvConstant of velocity repulsion, vor＝|vobs-vrI is the speed of the robot relative to the obstacle and theta is the relative speed vorThe angle between the position vector of the robot relative to the sudden obstacle is
When the robot moves towards the direction far away from the obstacle, the acting force of the velocity repulsion potential field is reduced to zero. v. ofobsRepresenting the speed of the obstacle, vrRepresenting the speed of the robotic system.
The force magnitude calculation module is used for obtaining the attraction of each target point to the robot according to the attraction potential field of each target point, obtaining the repulsion force of the basic obstacles according to the repulsion potential field of the basic obstacles, obtaining the total road boundary constraint repulsion force according to the repulsion force fields of the left and right boundaries of the aisle, and obtaining the speed repulsion force according to the speed repulsion potential field of the sudden obstacles;
in the embodiment of the invention, the
And determining the attraction of the target point to the robot, namely calculating a derivative of the potential field formula, and then calculating a vector sum.
By
Determining the repulsive force of the obstacle, namely obtaining the vector sum after derivation of a potential field formula, wherein Freq1In a direction from the obstacle to the robot, Freq2Is directed to the target point by the robot.
From Fr＝ΔUrL(q)+ΔUrR(q) determining the total aisle boundary constraint repulsive force, namely deriving a potential field formula and then solving a vector sum.
By
The resultant force calculation module is used for acquiring the vector sum of the attraction force of each target point to the robot, the repulsion force of the basic obstacle, the total road boundary constraint repulsion force and the velocity repulsion force to obtain resultant force;
in the embodiment of the invention, the
And determining the resultant force applied to the system.
And the obstacle avoidance sub-module is used for taking the angle of the resultant force as the target motion angle of the robot, and setting the magnitude of the resultant force as the target motion speed of the robot, so that the control module drives the robot to move according to the target motion angle and the target motion speed, and obstacle avoidance is realized.
During the continuous operation of the robot system, the position information of the robot system is uploaded at any time and compared with the terminal, and if the terminal is reached, the path planning is finished.
It should be noted that, according to the implementation requirement, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can be combined into new steps/components to achieve the purpose of the present invention.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A multi-functional intelligent robotic system for processing dynamic crew service demand information, comprising: the system comprises a control module, a path planning module, an interaction module and a detection module;
the control module is configured to identify a current target working mode, and acquire information corresponding to the target working mode, where the information corresponding to the target working mode includes: target location and target demand;
the path planning module is used for realizing path planning by adopting an optimization algorithm of a multi-machine optimal path and a stochastic obstacle processing algorithm so as to search an optimal path reaching the target position while avoiding obstacles, and then the control module controls the robot to reach the target position according to the optimal path and executes operation corresponding to the target requirement;
the detection module is used for executing detection operation corresponding to the target requirement and transmitting the acquired detection data to the control module so that the control module transmits the detection data to a cloud for verification;
and the interaction module is used for realizing human-computer interaction.
2. The robot system of claim 1, wherein when the target operation mode is a meal delivery mode, the control module is configured to receive user ordering information and a location of a user from a cloud, feed a meal delivery state back to the user through the cloud, and then, when an optimal path planned by the path planning module reaches the location of the user, prompt and confirm delivery of a meal through the interaction module.
3. The robot system of claim 1, wherein when the target operation mode is a temperature measurement mode, the control module is configured to receive a temperature measurement target position input through the interaction module, measure temperature of the target object by the detection module after the optimal path planned by the path planning module reaches the temperature measurement target position, and transmit the temperature measurement data to the control module, so that the control module transmits the temperature measurement data to the cloud for verification.
4. The robot system of claim 1, wherein when the target operation mode is a ticket checking mode, the control module is configured to receive a ticket checking target location input through the interaction module, capture, by the detection module, ticket information of a target object and face information of the target object after the optimal path planned by the path planning module reaches the ticket checking target location, and transmit the ticket information and the face information to the control module, so that the control module transmits the ticket information and the face information to the cloud for verification.
5. The robotic system of claim 1, wherein the control module is further configured to determine whether the current battery power headroom meets the current path predicted power consumption, and perform a charging route planning by the path planning module to perform a charging operation when the current battery power headroom does not meet the current path predicted power consumption.
6. The robotic system as claimed in any one of claims 1 to 5, wherein the path planning module comprises:
the system comprises a path point acquisition module, a path starting point acquisition module and a path terminal point acquisition module, wherein the path starting point and the path terminal point are acquired from the cloud end by the control module, the path starting point is the current position of the robot, and the path terminal point is a target position;
the system comprises a control module, a road block information acquisition module and a cloud terminal, wherein the control module is used for acquiring basic road block information in a working area from the control module;
the path planning submodule is used for planning paths according to the path starting point, the path terminal point and basic roadblock information in the working area by using an A-x algorithm to obtain an optimal path;
the obstacle capturing module is used for capturing a scene in front of a robot traveling route and positioning a sudden obstacle in the scene;
and the obstacle avoidance module is used for synchronizing the obstacle information obtained in the artificial potential field algorithm to the A-algorithm in real time to assist the robot to adjust the path in time when a new obstacle is introduced so as to realize the obstacle avoidance function.
7. The robotic system as claimed in claim 6, wherein the obstacle avoidance module comprises:
the parameter acquisition module is used for setting a target point every other distance of a carriage by taking the terminal point in the optimal path acquired by the path planning sub-module as a starting target point until the distance between the target point and the robot is less than the distance between the carriages, so as to obtain a plurality of target points;
the potential field calculation module is used for calculating an attraction potential field, a basic obstacle repulsion potential field, a corridor left and right boundary repulsion field and a speed repulsion potential field of a sudden obstacle of each target point;
the force magnitude calculation module is used for obtaining the attraction of each target point to the robot from the attraction potential field of each target point, obtaining the repulsion force of the basic obstacles from the repulsion potential field of the basic obstacles, obtaining the total road boundary constraint repulsion force from the repulsion fields of the left and right boundaries of the aisle, and obtaining the speed repulsion force from the speed repulsion potential field of the sudden obstacles;
the resultant force calculation module is used for acquiring the sum of the vectors of the attraction force of each target point to the robot, the repulsion force of the basic obstacle, the total road boundary constraint repulsion force and the velocity repulsion force to obtain resultant force;
and the obstacle avoidance sub-module is used for taking the angle of the resultant force as a target motion angle of the robot, and setting the magnitude of the resultant force as a target motion speed of the robot, so that the control module drives the robot to move according to the target motion angle and the target motion speed, and obstacle avoidance is realized.
8. The robotic system as claimed in claim 7, wherein the robotic system is powered by
Determine the repulsive force field U of the left boundary of the aislerL(q) and corridor right boundary potential field UrR(q) wherein krLIs the proportionality coefficient of the repulsive field of the left boundary of the road, krRIs the proportionality coefficient of the road right boundary repulsive field, W is the robot width, rho (q, q)L) Is the vertical distance between the center of mass of the robot and the corresponding point of the left boundary of the passageway, rho (q, q)R) The vertical distance between the center of mass of the robot and a corresponding point of the right boundary of the aisle is represented as q, the current position coordinate of the robot system is represented as qLRepresenting the coordinates of the corresponding points of the left boundary of the aisle, qRAnd representing the position coordinates of the corresponding point on the right boundary of the aisle.
9. The robotic system as claimed in claim 8, wherein the robotic system is configured to be operated by
Determining a fundamental barrier repulsive force potential field Ureq(q) wherein ρn(q,qobs) Is the n-th power of the distance between the robot and the target point, n is a positive coefficient, L is the distance from the center to the top point of the robot, η represents a repulsive force scale factor, qobsIndicating a disorderPosition coordinates of material points, p0The radius of influence of each obstacle is indicated.
10. The robotic system as claimed in claim 9, wherein the robotic system is powered by
Determining velocity repulsive potential field U of sudden obstaclerev(q) wherein kvConstant of velocity repulsion, vor＝|vobs-vrI is the speed of the robot relative to the obstacle and theta is the relative speed vorAngle between position vectors of robot relative to sudden obstacle, vobsRepresenting the speed of the obstacle, vrRepresenting the speed of the robotic system.
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