CN110647089A - Intelligent warehouse logistics robot control system and control method - Google Patents
Intelligent warehouse logistics robot control system and control method Download PDFInfo
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- CN110647089A CN110647089A CN201911028660.XA CN201911028660A CN110647089A CN 110647089 A CN110647089 A CN 110647089A CN 201911028660 A CN201911028660 A CN 201911028660A CN 110647089 A CN110647089 A CN 110647089A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
Abstract
The invention relates to an intelligent warehouse logistics robot control system and a control method, comprising a robot subsystem, a control terminal and a UWB positioning base station, wherein the robot subsystem comprises a main control panel, a slave control panel, a mileometer, an ultrasonic sensor, a power supply commutation module, an IMU, a UWB tag, a laser radar, a photoelectric isolation module, a brushless direct current motor driver, a brushless direct current motor, a remote control receiver and a plurality of push rods; the UWB positioning system comprises a UWB tag, a main control panel, an IIC (inter integrated circuit) and an IMU (inertial measurement Unit), wherein the UWB tag is in wireless communication with a UWB positioning base station, the main control panel is in real-time communication with the UWB tag and is in real-time communication with the IMU through the IIC, and the main control panel is in real-time communication with a laser radar; meanwhile, the main control board controls the brushless direct current motor to work; the slave control board is in two-way communication with the main control board, and meanwhile, the slave control board is connected with the remote control receiver, the odometer and the ultrasonic sensor; and the push rods are connected with the slave control panel through the power supply reversing module. On the premise of improving the precision, the reliability of the system operation is enhanced.
Description
Technical Field
The invention relates to the field of autonomous positioning and navigation of mobile robots, in particular to a control system and a control method of an intelligent warehouse logistics robot.
Background
The intelligent storage logistics machine is being applied to links such as sorting, packing, transport in industry more and more, replaces artifical transport goods, has improved work efficiency. The intelligent storage logistics robot automatically avoids obstacles through ground navigation and finishes the set working process. The positioning and navigation of the robot are key technologies of the intelligent warehouse logistics robot, and the positioning technology of the robot can be divided into two categories of absolute positioning and relative positioning. The sensors for relative positioning measurement mainly comprise an IMU (inertial measurement unit), an odometer and the like, and the disadvantage of the relative positioning measurement is that errors are gradually accumulated along with the passage of time; the absolute positioning mainly adopts visual positioning, instant positioning and map building (SLAM), beacon-based positioning and the like, and the sensors have the defects that the uncertainty of a perception object is increased and the instability of a system is increased under the condition of environment change.
Because the positioning technology of a single sensor has certain limitation, based on the limitation, the intelligent storage logistics robot integrates various positioning technologies such as a laser radar, a UWB sensor, an inertial navigation sensor, an ultrasonic sensor and the like, realizes the accurate sensing of the intelligent storage logistics robot to the field environment, and improves the positioning accuracy and reliability of the robot.
Disclosure of Invention
The invention aims to provide a control system and a control method for an intelligent warehouse logistics robot.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent warehouse logistics robot control system comprises a robot subsystem, a control terminal and a UWB positioning base station, and is characterized in that,
the robot subsystem comprises a main control panel, a slave control panel, a mileometer, an ultrasonic sensor, a power supply commutation module, an IMU (inertial measurement Unit), a UWB (ultra Wide band) tag, a laser radar, a photoelectric isolation module, a brushless direct current motor driver, a brushless direct current motor, a remote control receiver and a plurality of push rods for lifting or descending a goods shelf or an article by the robot;
the main control panel is a raspberry controller and is used for constructing a map, self-positioning, path planning navigation, motion control and control terminal interaction; the slave control panel is used for distance measurement of the ultrasonic sensor, data acquisition of the odometer, information acquisition of a remote controller receiver and control of the push rod;
the UWB positioning system comprises a UWB tag, a main control board, an IIC (inter integrated circuit) and an IMU (inertial measurement Unit), wherein the UWB tag is in wireless communication with a UWB positioning base station, the main control board is in real-time communication with the UWB tag in a serial port communication mode, the main control board is in real-time communication with the IMU through the IIC, and the main control board is in real-time communication; meanwhile, the main control panel controls the brushless DC motor on the robot to work through the photoelectric isolation module and the brushless DC motor driver,
the slave control board is in two-way communication with the master control board, and is connected with the remote control receiver, the odometer and a plurality of ultrasonic sensors distributed on the robot; the plurality of push rods are connected with the slave control panel through the power supply reversing module;
the main control panel is in wireless communication with an upper computer of the control terminal, and the auxiliary control panel is in communication with the handheld remote controller through the remote control receiver.
A control method of an intelligent warehouse logistics robot control system comprises the following steps:
1) the intelligent warehouse logistics robot scans the laser radar of the site environment in a manual remote control mode, the laser radar transmits data to the main control panel through the USB data interface, and the main control panel processes the data and obtains map information of the site environment;
2) the slave control board transmits the acquired data information of the odometer sensor to the master control board in real time; the main control board communicates with the IMU in real time through the IIC to obtain the attitude angle and acceleration information of the robot; the main control panel performs data layer fusion on the IMU and data information obtained by the odometer to obtain a characteristic value;
3) the main control board communicates with the laser radar in real time through a USB (universal serial bus), and obtains the relative position information of the environment where the robot is located; the slave control board transmits the acquired data information of the ultrasonic sensor to the master control board in real time; the main control board performs data layer fusion on data information obtained by the laser radar and the ultrasonic sensor to obtain a characteristic value;
4) the main control board carries out real-time communication with the UWB tag in a serial port communication mode, so that real-time distance information between the robot and the UWB positioning base station is obtained, and absolute position information characteristic values of the environment where the robot is located are finally obtained after Kalman filtering processing and trilateral positioning algorithm calculation;
5) the main control board performs feature layer fusion on a feature value obtained after fusing the odometer and the IMU, a feature value obtained after fusing the laser radar and the ultrasonic sensor and a feature value obtained by the UWB by using an improved extended Kalman filter, and finally obtains the pose of the robot;
6) the control terminal realizes data interaction with the main control panel in a wireless communication mode, so that the position of the robot is displayed in real time, and after the destination of the robot is set through the control terminal, the path is automatically planned through an A-x algorithm;
7) the robot controls four brushless direct current motors of the robot according to a planned path, in the running process of the robot, the real-time distance of obstacles around the robot is obtained by acquiring four ultrasonic sensors in real time from a control board, and when the distance between the robot and the advancing direction is smaller than a set distance, the robot stops immediately and carries out autonomous obstacle avoidance to re-plan the path;
8) when the robot reaches a set place, the main control panel transmits a control command to the auxiliary control panel through IIC communication, and the auxiliary control panel controls the push rod to ascend or descend through the power supply phase-changing module;
9) the robot can also be manually controlled through a handheld remote controller, and manual control over the robot is achieved by collecting signals of the remote control receiver in real time from the control panel, so that the function of finishing temporary work by the robot is achieved.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention adopts a mode of combining data layer fusion and characteristic layer fusion, firstly carries out data layer fusion on an IMU and an odometer and carries out data layer fusion on a laser radar and an ultrasonic sensor to ensure the accuracy of measured data, then carries out characteristic layer fusion on characteristic values obtained after the two data layers are fused (the characteristic value obtained by carrying out data layer fusion on the IMU and the odometer and the characteristic value obtained by carrying out data layer fusion on the laser radar and the ultrasonic sensor) and information characteristic values obtained by a UWB (ultra wide band), avoids the problem of approximately reduced accuracy of data loss caused by directly carrying out characteristic fusion, realizes the accurate perception of an intelligent logistics storage robot to the field environment by adopting a mode of combining a plurality of fusion structures at multiple levels, adopts an improved extended Kalman filter for characteristic layer fusion, namely firstly corrects a sampling observation point by estimation, a group of unbiased novel observation points are constructed and substituted into an extended Kalman filter iterative algorithm model, and the process noise variance obtained in the extended Kalman filter iterative algorithm is optimally selected by using a DE (differential evolution) algorithm in the iterative process, so that the state estimation deviation is reduced, and the positioning accuracy and reliability of the robot are improved. The invention can solve the problem of large data processing capacity by matching with the raspberry pi controller, and basically does not lose or omit information because of adopting data layer fusion and storing the field environment information as much as possible, and the precision is ensured by extracting the characteristic value and then performing characteristic level fusion with other sensors.
2) The invention adopts a dual-processor distributed control mode, takes a raspberry-type microcomputer as a control core of a robot control system, can complete the same IO pin control, can run a corresponding operating system, can complete more complex task management and scheduling, can support the development of more upper-layer application, provides a wider application space for developers, and has strong processing capability. The Raspberry Pi 3model B + microcomputer comprises onboard storage of a MicroSD card slot, a 1G RAM, an ARM Cortex-A53 CPU and various peripheral communication interfaces such as SCI, SPI, IIC and the like. Several power saving modes provide the most optimal solution with flexibility in terms of computational performance, wake-up latency and power consumption. The slave control board adopts an STM32F103 singlechip as a master control chip to realize functions of acquiring odometer signals, acquiring remote control signals, acquiring ultrasonic sensor signals, controlling push rod actions and the like, and the distributed control reduces calculation errors in data processing, increases data processing precision and simplifies data operation process, thereby reducing the burden of a core processor and ensuring that a control system becomes safer and more stable; the raspberry group is matched with the slave control board, so that the running stability of the system is improved.
3) The invention adopts UWB positioning mode to obtain the absolute position of the robot, it has great advantage in indoor positioning, the positioning accuracy is high; the inertial navigation technology can detect the self attitude so as to calculate the self position, and the real-time detection performance of the inertial navigation technology is good; the laser radar can be used for detecting and recording the surrounding position environment, then an environment map of the robot is obtained through a GmappingSLAM algorithm, the robot can play a role in real-time obstacle avoidance while moving, and the detection capability of the robot on unknown objects (moving objects) is enhanced; the ultrasonic positioning of the reflection type distance measurement mode is adopted for distance measurement, the positioning mode is high in precision, the defect that the laser radar fails before a transparent or high-reflection object can be overcome, and the laser radar is used as the last defense line for robot positioning control, so that the reliability of system operation is enhanced.
4) The invention can be applied to the field of warehouse logistics, is beneficial to reducing the cost of logistics sorting and carrying, reducing the input of personnel, improving logistics management, reducing the probability of damage to goods carrying, improving the sorting efficiency of modern logistics and promoting the development of the logistics industry. In addition, it may be applied to other fields including: the material processing field, the hotel goods carrying field, the military and dangerous place field and the like have high requirements on positioning precision and operation stability.
5) A friendly human-computer interface is designed in the upper computer, functions such as the destination of the robot and the real-time position of the robot can be set through the control terminal, meanwhile, functions of storing sensor log files and alarming error information can be completed, the running state of the robot can be monitored in real time, such as the functions of displaying the electric quantity of a battery and alarming at low voltage, and functions of displaying the current speed of the robot, the distance of obstacles, the rotation angle of the robot and the like in real time are achieved.
Drawings
Fig. 1 is a hardware block diagram of a system according to the present invention.
Fig. 2 is a software structure block diagram of a master control board and a slave control board of the robot according to the present invention.
Fig. 3 is a block diagram of a multi-sensor data fusion positioning structure according to the present invention.
Fig. 4 is a block diagram of an absolute positioning solution for UWB according to the present invention.
Fig. 5 is a block diagram schematically illustrating a flow structure of an ultrasonic and lidar data fusion process according to the present invention.
Fig. 6 is a block diagram of a map scanning and generating program according to the present invention.
Fig. 7 is a software flowchart of the path planning of the main control board according to the present invention.
In the figure, 100 robot subsystems, 200 control terminals and 300UWB positioning base stations;
a first wireless transceiver module 101, a main control board 102, a UWB tag 103, a laser radar 104, a photoelectric isolation module 105, a brushless dc motor driver 106, a first brushless dc motor 107, a second brushless dc motor 108, a third brushless dc motor 109, a fourth brushless dc motor 110, an IMU 111, a slave control board 112, an odometer 113, a remote control receiver 114, a first ultrasonic sensor 115, a second ultrasonic sensor 116, a third ultrasonic sensor 117, a fourth ultrasonic sensor 118, a power supply commutation module 119, an a pushrod 120, a B pushrod 121, a C pushrod 122, and a D pushrod 123;
the upper computer 21, the second wireless transceiver module 22 and the handheld remote controller 23;
a first UWB base station 31, a second UWB base station 32, a third UWB base station 33, a first level shift module 34, a second level shift module 35, a third level shift module 36, a first base station power supply 37, a second base station power supply 38, and a third base station power supply 39.
Detailed Description
The present invention is described in detail below with reference to the following examples and drawings, but the present invention is not limited thereto.
As shown in fig. 1, the smart warehouse logistics robot control system includes three major parts, namely a robot subsystem 100, a control terminal 200 and a UWB positioning base station 300. The robot subsystem comprises a first wireless transceiving module 101, a main control board 102, a UWB tag 103, a laser radar 104, a photoelectric isolation module 105, a brushless direct current motor driver 106, a first brushless direct current motor 107, a second brushless direct current motor 108, a third brushless direct current motor 109, a fourth brushless direct current motor 110, an IMU 111, a slave control board 112, a odometer 113, a remote control receiver 114, a first ultrasonic sensor 115, a second ultrasonic sensor 116, a third ultrasonic sensor 117, a fourth ultrasonic sensor 118, a power supply commutation module 119, an A push rod 120, a B push rod 121, a C push rod 122 and a D push rod 123; the robot subsystem is installed on the robot, four brushless direct current motors control the robot to walk, four ultrasonic sensors are distributed around four corners of the robot to measure distance in real time, a remote control receiver is used for receiving control signals of a handheld remote controller, and a UWB (ultra wide band) tag, a speedometer and a laser radar are all installed on the robot; four push rods are arranged on the robot and used for lifting or lowering the goods shelf or the articles by the robot.
The UWB positioning base station 300 comprises a first UWB base station 31, a second UWB base station 32, a third UWB base station 33, a first level conversion module 34, a second level conversion module 35, a third level conversion module 36, a first base station power supply 37, a second base station power supply 38 and a third base station power supply 39, and the control terminal comprises an upper computer 21 and a second wireless transceiver module 22.
The first base station power is provided by AC220V and is connected to the input of a first UWB base station 31 connection terminal, the output of which is connected to the input of a first level shifter module 34, the output of the first level shifter module 34 being connected to the power input of the first UWB base station 31. The second base station power is supplied by AC220V and is connected to the input of a second UWB base station 32 connection terminal, the output of which is connected to the input of a second level shifter module 35, the output of the second level shifter module 35 being connected to the power input of the second UWB base station 32. The third base station power is supplied by AC220V and is connected to the input of a terminal of the third UWB base station 33, the output of the terminal is connected to the input of the level shifter module 3, and the output of the level shifter module 3 is connected to the power input of the UWB base station 3. The number of UWB base stations is at least three, the positioning effect is improved when the number of the base stations is larger, but the cost, the algorithm difficulty and the like are increased.
The power supply input end of the UWB tag is connected to the +5V output end of the main control board, and the RXD and TXD terminals are respectively connected with the TXD and RXD terminals of the main control board; six input terminals of the photoelectric isolation module are respectively connected to output terminals GPIP.7, GPIP.0, GPIP.2, GPIP.3, GPIP.21 and GPIP.22 of the main control board, and six output terminals of the photoelectric isolation module are respectively connected to ENA, IN1, IN2, IN3, IN4 and ENB terminals (ENA and ENB are enable terminals, IN1-4 bit input terminals) of the brushless direct current motor driver; the USB interface of the laser radar is connected to the USB interface of the main control panel; the SDA terminal and the SCL terminal of the IMU are respectively connected to the SDA.1 terminal and the SCL.1 terminal of the main control board; the SDA.1 and SCL.1 terminals of the main control board are respectively connected with the PB9 and PB8 terminals of the slave control board; the ethernet port of the first radio transceiver module 1 is connected to the ethernet port of the main control board.
Differential signal pins CHO, CH1 and CH2 of the remote controller receiver are respectively connected to input terminals PA8, PA9 and PA10 of the slave control board, and the handheld remote controller 23 and the remote controller receiver realize data communication; the input terminals of the first ultrasonic sensor 15, the second ultrasonic sensor 16, the third ultrasonic sensor 17 and the fourth ultrasonic sensor 18 are respectively correspondingly connected with the signal output terminals PB5, PB2, PC10 and PC11 terminals of the slave control board, and the output terminals of the first ultrasonic sensor 15, the second ultrasonic sensor 16, the third ultrasonic sensor 17 and the fourth ultrasonic sensor 18 are respectively correspondingly connected with the signal input terminals PA0, PA1, PA2 and PA3 terminals of the slave control board; two input ends of the power supply phase-changing module are respectively connected to output terminals PE13 and PE14 of the slave control board, the output end of the power supply phase-changing module is respectively connected to the power supply input end of the push rod A-the push rod D, and the push rod A-the push rod D are both electric push rods.
The main control board is a Raspberry Pi 3model B + microcomputer developed by Raspberry Pi foundation, comprises onboard storage of a MicroSD card slot, a RAM of 1G and an ARM Cortex-A53 CPU, and is provided with various peripheral communication interfaces such as SCI, SPI, IIC and the like. Several power saving modes provide the most optimal solution with flexibility in terms of computational performance, wake-up latency and power consumption. The raspberry pi 4B can be selected by the main control board, and the running speed of the raspberry pi can be further improved.
The slave control board is an STM32F103 produced by Italian Semiconductor (ST), the core of the slave control board is Cortex-M3, and the slave control board integrates various functions of a timer, CAN, ADC, SPI, IIC, USB, UART and the like, and the maximum working frequency is 72 MHZ.
The UWB base stations (31-33) and the UWB tags are developed based on DW1000 chips developed by Decawave corporation, the UWB tags are communicated with all the base stations in a wireless communication mode, and the measured distances between the positioning tags and all the base stations are transmitted to a main control board Raspberry Pi 3model B + microcomputer in a serial communication mode after calculation processing.
The remote control receiver is FS-IA6B, which has 6 channels, frequency range: 2.4055-2.475GHZ, the number of wave bands is 140, the transmitting power is not higher than 20DBM, and the receiving sensitivity is-105 DBM. The hand-held remote controller has a wireless transmission function and can be wirelessly controlled with the remote control receiver.
The IMU is MPU6050, the angular speed full-grid sensing range is + -250, + -500, + -1000 and + -2000 °/sec (dps), the fast and slow motion can be accurately tracked, and the user programmable accelerator full-grid sensing range is + -2 g, + -4 g, + -8 g and + -16 g. The product transmission is transparent to IIC up to 400 kHz.
The laser radar is RPLIDARA2 produced by Silan technology, the range of the distance measurement is 0.15-18 m, the resolution ratio of the distance measurement is less than 0.5 mm, the angular resolution is 0.9 degree, the scanning angle is 0-360 degrees, the time of single distance measurement is 0.25 second, and the scanning frequency is 10 HZ.
The ultrasonic sensor is an HC-SR04 module, the working voltage is 5V, the working frequency is 40kHz, the detection distance can reach 700cm, and the lowest detection distance is as low as 2 cm.
The odometer adopts an incremental photoelectric encoder to carry out dead reckoning, and consists of a grating disk, a light-emitting device and a detection grating, and the output level is CMOS.
The photoelectric isolation module adopts an optical coupler for isolation, the signal voltage of an input end is 3.3-24V, the power supply voltage of an output end is 6-30V, and the maximum response time of four independent working paths is 1M times/second.
The push rod is XTL100, the power supply mode is direct current, the stroke is 150mm, the push rod has the function of a limit switch, and the push rod can stop at any position.
The hand-held remote controller is FS-I6, which has 6 channels, data resolution: 1024 levels, input voltage 6V, modulation mode: GFSK, system mode: the second generation enhanced version automatic frequency modulation digital system has a low voltage alarm function.
The upper computer 21 is a PC (personal computer) and has a friendly man-machine interface, and the destination of the robot and the real-time position of the robot can be set and displayed through the control terminal; the system can also realize TCP network communication with a robot subsystem and complete the functions of sensor (including a milemeter, an ultrasonic sensor, a laser radar, a UWB tag and an IMU) log file storage and error information alarm; the running state of the robot is monitored in real time, such as the functions of battery power display and low-voltage alarm, the functions of displaying the current speed of the robot, the distance of obstacles, the rotation angle of the robot and the like in real time.
The control method of the intelligent warehouse logistics robot control system comprises the following steps:
1) firstly, the robot is in a manual remote control mode to scan a laser radar for a field environment, the laser radar transmits data to a main control panel through a USB data interface, and the main control panel processes the data through a GmappingSLAM algorithm and obtains map information of the field environment.
2) The slave control board transmits the acquired data information of the odometer sensor to the master control board in real time; the main control board communicates with the IMU in real time through the IIC to obtain information such as an attitude angle and acceleration of the robot; the main control panel performs data layer fusion characteristic value on data information obtained by the IMU and the odometer by using a Bayesian filtering algorithm;
3) the main control board communicates with the laser radar in real time through a USB (universal serial bus), and obtains the relative position information of the environment where the robot is located; the slave control board transmits the acquired data information of the ultrasonic sensor to the master control board in real time; the main control panel performs data layer fusion characteristic values on data information obtained by the laser radar and the ultrasonic waves by using a Bayesian filtering algorithm;
4) the main control board carries out real-time communication with the UWB tag in a serial port communication mode, so that real-time distance information between the robot and the UWB positioning base station is obtained, and absolute position information characteristic values of the environment where the robot is located are finally obtained after Kalman filtering processing and trilateral positioning algorithm calculation;
5) the main control board performs feature layer fusion on a feature value obtained after fusing the odometer and the IMU, a feature value obtained after fusing the laser radar and the ultrasonic sensor and a feature value obtained by the UWB by using an improved extended Kalman filter, and finally obtains the pose of the robot;
6) the control terminal realizes data interaction with the main control panel in a wireless communication mode, so that the position of the robot is displayed in real time, and after the destination of the robot is set through the control terminal, the path is automatically planned through an A-x algorithm.
7) The robot controls four brushless direct current motors of the robot according to the planned path, in the running process of the robot, the real-time distance of obstacles around the robot is obtained by collecting four ultrasonic sensors in real time from a control board, and when the distance between the robot and the advancing direction is smaller than a set distance, the robot stops immediately and automatically avoids obstacles to plan the path again.
8) When the robot reaches a set place, the main control panel transmits a control command to the auxiliary control panel through IIC communication, and the auxiliary control panel controls the corresponding push rod to perform ascending or descending actions through the power supply phase-changing module.
9) The robot can also be manually controlled through a handheld remote controller, and the manual control of the robot can be realized through collecting signals of the remote control receiver in real time from the control panel, so that the function of finishing temporary special work by the robot is realized.
The characteristic layer fusion in the invention is to extract the characteristics of the data fusion unit of the data layer to obtain a characteristic vector, and then fuse the characteristic vectors extracted by each fusion unit; and the data layer fusion is to directly fuse the observation data of each sensor and then extract a feature vector from the fused data.
The system software flow related by the invention is as follows:
the system software consists of a main control panel unit and a slave control panel unit, wherein the main control panel unit software mainly comprises the contents of map construction, self positioning, path planning navigation, motion control, control terminal interaction and the like. The slave control panel unit STM32F103 single chip microcomputer mainly comprises the functions of ultrasonic sensor ranging, odometer data acquisition, remote controller receiver information acquisition and push rod control.
As shown in fig. 2, the main controller program mainly comprises an instantiation program of a class, a multi-sensor fusion algorithm program, a control program, and an upper computer communication program. The instantiation procedures of the class comprise a Kalman filter procedure, a trilateration procedure and a path planning procedure, the instantiation of the class is to create an instance of the class, the specific object of the class is convenient for calling of a subsequent procedure, and the instantiation of the class can be reused. The multi-sensor fusion algorithm program mainly comprises an IMU and odometer data fusion program, an ultrasonic wave and laser radar data fusion program, a UWB absolute positioning resolving program and a characteristic layer fusion program. The control program mainly comprises a remote control program, a push rod control program and a motor control program. The remote control program is used for processing data sent by the handheld remote controller, when the robot is in a remote control mode, the control program actively inquires the data, and when the robot is in an automatic mode, the control program actively inquires the data after path processing; when the robot reaches a target point, a push rod control signal is sent to the slave control board; the control motor program mainly controls the control speed and direction of four brushless direct current motors through PWM signals. The upper computer communication program is a new process, a Socket class is instantiated to monitor and open 1000 ports to analyze data issued by the upper computer, and the data is uploaded to the upper computer through json library formatting processing.
The slave controller program comprises an ultrasonic data acquisition program, a remote control receiver signal processing program, a control push rod program and a mileage data acquisition program. The ultrasonic data acquisition program mainly acquires the distance between each ultrasonic sensor and the barrier; the signal processing program of the remote control receiver reads the PWM pulse signal of the receiver in real time and calculates the time of high level so as to solve the information of the handheld remote controller; a program for controlling the push rod program to control the push rod action by controlling the change of the GPIO pin; and the odometer data acquisition program finishes data acquisition of the odometer.
As shown in fig. 3, the present invention relates to a block diagram of a multi-sensor data fusion positioning structure:
(1) the slave control panel transmits the real-time acquired data information of the odometer sensor to the master control panel; the main control board communicates with the IMU in real time through the IIC to obtain information such as an attitude angle and acceleration of the robot; the main control panel performs data layer fusion characteristic value on data information obtained by the IMU and the odometer by using a Bayesian filtering algorithm;
(2) the main control board communicates with the laser radar in real time through a USB (universal serial bus), and obtains the relative position information of the environment where the robot is located; the slave control board transmits the acquired data information of the ultrasonic sensor to the master control board in real time; the main control panel performs data layer fusion characteristic values on data information obtained by the laser radar and the ultrasonic waves by using a Bayesian filtering algorithm;
(3) the main control board carries out real-time communication with the UWB tag in a serial port communication mode, so that real-time distance information between the robot and the UWB positioning base station is obtained, and absolute position information characteristic values of the environment where the robot is located are finally obtained after Kalman filtering processing and trilateral positioning algorithm calculation;
(4) and the main control panel performs feature layer fusion on the feature value obtained after fusing the odometer and the IMU, the feature value obtained after fusing the laser radar and the ultrasonic wave and the feature value obtained by the UWB by using an improved extended Kalman filter to finally obtain the pose of the robot.
The specific process of the improved extended kalman filter for feature layer fusion is as follows: firstly, correcting sampling observation points by using least square estimation to obtain a group of novel unbiased sampling observation points for the following extended Kalman filter iterative algorithm. Secondly, establishing an extended Kalman algorithm model, wherein the algorithm flow comprises the following steps: 1) a state equation; 2) observing an equation; 3) predicting the state; 4) predicting covariance in one step; 5) calculating a filtering gain; 6) updating the state; 7) updating a covariance matrix; 8) process noise; 9) the process noise variance. Finally, selecting an optimal solution from the process noise variance solved in the step 9) by using a DE (differential evolution) algorithm, wherein the algorithm flow comprises the following steps: 1) initializing a population; 2) performing mutation operation; 3) performing cross operation; 4) And selecting operation, and finally applying the process noise variance obtained by solving the optimal solution to the step 9 to the next iteration process.
The specific process of the Bayesian filtering algorithm for data layer fusion is as follows: firstly, system input comprises: 1) state observation and action from 1 to t; 2) observing the model; 3) a state transition model of the action; 4) prior probability distribution of system states. The next is the desired output, i.e. the probability of late delay of the state (confidence probability of the state) is calculated.
As shown in fig. 4, the absolute positioning solution structure block diagram of UWB according to the present invention:
(1) the main control board reads distance data between the UWB tag and the UWB base station in real time through a serial port;
(2) the main control board filters the obtained data through a Kalman filter;
(3) the main control board is resolved through a Trilateration Trilateration positioning algorithm;
(4) the main control board obtains absolute position information of the robot.
As shown in fig. 5, the structure block diagram of the ultrasonic wave and lidar data fusion program related to the present invention:
(1) the main control board communicates with the laser radar in real time through a USB (universal serial bus), and obtains the relative position information of the environment where the robot is located;
(2) the slave control board transmits the acquired data information of the ultrasonic sensor to the master control board in real time;
(3) the main control panel performs Bayesian filtering algorithm fusion on data information obtained by the laser radar and the ultrasonic waves;
(4) the main control board obtains the relative position of the robot and the surrounding environment information.
As shown in FIG. 6, the present invention relates to a map scanning and generating program structure block diagram
(1) The main control board obtains an upper computer map building instruction and calibrates the linear velocity and the angular velocity;
(2) the robot is moved in the environment through manual control;
(3) the main control board reads the data information of the laser radar through a USB to acquire the pose of the robot;
(4) the main control board builds and stores a graph through a GmappingSLAM algorithm;
as shown in fig. 7, the present invention relates to a solution flow of the main control board a route planning:
(1) dividing a search area into grids with equal size;
(2) putting the starting point into an opening list;
(3) searching reachable grid points around the node, and skipping the points of the closed list, wherein the points are used as father grids of the points;
(4) deleting the point from the opening list, adding the point into the closing list, and calculating the value of F, G and H of the point, wherein F is the estimation value of the minimum cost path from the starting node to the target node through the node n, G is the actual cost of the path from the starting node to the node n, and H is the estimation cost of the possible optimal path from the node n to the target node; n is a positive integer;
(5) judging whether the value of F is minimum, if so, finding successfully, deleting the point from the open list, adding the point into the closed list, and entering the step (6); if not, adding the point into a closing list;
(6) and (4) judging whether the point is a target point, if so, ending the path searching, otherwise, continuing the searching process, and returning to the step (3).
The fusion strategy of the invention is to fuse information on the same level to obtain information after high-level fusion, and then fuse the information into other corresponding information fusion levels, wherein the closer the fused information is to an information source, the higher the obtained precision is.
The invention can be applied to the warehouse sorting operation of small and medium-sized pieces and various types, is suitable for the warehouse with the area of tens of thousands of square meters, can be used for various industries such as e-commerce, retail, 3PL, medicine, shoes and clothes, food, daily necessities, industry, automobile manufacturing and the like, and can also be used for assembly workshops of large-scale factories. In addition, it may be applied to other fields including: the material processing field, the military and dangerous place field, etc.
Claims (7)
1. An intelligent warehouse logistics robot control system comprises a robot subsystem, a control terminal and a UWB positioning base station, and is characterized in that,
the robot subsystem comprises a main control panel, a slave control panel, a mileometer, an ultrasonic sensor, a power supply commutation module, an IMU (inertial measurement Unit), a UWB (ultra Wide band) tag, a laser radar, a photoelectric isolation module, a brushless direct current motor driver, a brushless direct current motor, a remote control receiver and a plurality of push rods for lifting or descending a goods shelf or an article by the robot;
the main control panel is a raspberry controller and is used for constructing a map, self-positioning, path planning navigation, motion control and control terminal interaction; the slave control panel is used for distance measurement of the ultrasonic sensor, mileage data acquisition, information acquisition of a remote controller receiver and control of the push rod;
the UWB positioning system comprises a UWB tag, a main control board, an IIC (inter integrated circuit) and an IMU (inertial measurement Unit), wherein the UWB tag is in wireless communication with a UWB positioning base station, the main control board is in real-time communication with the UWB tag in a serial port communication mode, the main control board is in real-time communication with the IMU through the IIC, and the main control board is in real-time; meanwhile, the main control panel controls the brushless DC motor on the robot to work through the photoelectric isolation module and the brushless DC motor driver,
the slave control board is in two-way communication with the master control board, and is connected with the remote control receiver, the odometer and a plurality of ultrasonic sensors distributed on the robot; the plurality of push rods are connected with the slave control panel through the power supply reversing module;
the main control panel is in wireless communication with an upper computer of the control terminal, and the auxiliary control panel is in communication with the handheld remote controller through the remote control receiver.
2. The control system of claim 1, wherein the Raspberry Pi 3model B + microcomputer or Raspberry Pi 4B; the slave control board is an STM32F103 singlechip manufactured by Italian semiconductor corporation.
3. The control system of claim 1, wherein the IMU is MPU6050, the lidar is rpidara 2 manufactured by miralan technology, and the ultrasonic sensor is model HC-SR 04.
4. A control method of an intelligent warehouse logistics robot control system comprises the following steps:
1) the intelligent warehouse logistics robot scans the on-site environment through a manual remote control mode, the laser radar transmits data to the main control panel through the USB data interface, and the main control panel processes the data and obtains map information of the on-site environment;
2) the slave control board transmits the acquired data information of the odometer sensor to the master control board in real time; the main control board communicates with the IMU in real time through the IIC to obtain the attitude angle and acceleration information of the robot; the main control panel performs data layer fusion on the IMU and data information obtained by the odometer to obtain a characteristic value;
3) the main control board communicates with the laser radar in real time through a USB (universal serial bus), and obtains the relative position information of the environment where the robot is located; the slave control board transmits the acquired data information of the ultrasonic sensor to the master control board in real time; the main control board performs data layer fusion on data information obtained by the laser radar and the ultrasonic sensor to obtain a characteristic value;
4) the main control board carries out real-time communication with the UWB tag in a serial port communication mode, so that real-time distance information between the robot and the UWB positioning base station is obtained, and absolute position information characteristic values of the environment where the robot is located are finally obtained after Kalman filtering processing and trilateral positioning algorithm calculation;
5) the main control board performs feature layer fusion on a feature value obtained after fusing the odometer and the IMU, a feature value obtained after fusing the laser radar and the ultrasonic sensor and a feature value obtained by the UWB by using an improved extended Kalman filter, and finally obtains the pose of the robot;
6) the control terminal realizes data interaction with the main control panel in a wireless communication mode, so that the position of the robot is displayed in real time, and after the destination of the robot is set through the control terminal, the path is automatically planned through an A-x algorithm;
7) the robot controls four brushless direct current motors of the robot according to a planned path, in the running process of the robot, the real-time distance of obstacles around the robot is obtained by acquiring four ultrasonic sensors in real time from a control board, and when the distance between the robot and the advancing direction is smaller than a set distance, the robot stops immediately and carries out autonomous obstacle avoidance to re-plan the path;
8) when the robot reaches a set place, the main control panel transmits a control command to the auxiliary control panel through IIC communication, and the auxiliary control panel controls the push rod to ascend or descend through the power supply phase-changing module;
9) the robot can also be manually controlled through a handheld remote controller, and manual control over the robot is achieved through collecting signals of the remote control receiver in real time from the control panel, so that the function of completing temporary work by the robot is achieved.
5. The control method according to claim 4, wherein the data layer fusion in step 2) and step 3) is performed by using a Bayesian filtering algorithm.
6. The control method according to claim 4, characterized in that the operation state of the robot can be monitored in real time on a human-computer interface of the control terminal, including battery power display and low-voltage alarm; meanwhile, the system can display the current speed of the robot, the distance of the obstacle and the rotation angle of the robot in real time, and has the functions of sensor log file storage and error information alarm.
7. Use of the smart warehouse logistics robot control system of any one of claims 1-6 wherein the system can be used for material handling, hotel handling, military and hazardous location robot positioning control.
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| CN111880543A (en) * | 2020-08-05 | 2020-11-03 | 蒙泽新 | Indoor robot positioning control system based on UWB |
| CN112113565A (en) * | 2020-09-22 | 2020-12-22 | 温州科技职业学院 | Robot positioning system for agricultural greenhouse environment |
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| CN112797986A (en) * | 2021-02-07 | 2021-05-14 | 江西省智能产业技术创新研究院 | Intelligent logistics robot positioning system and method based on unmanned autonomous technology |
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| CN113093761A (en) * | 2021-04-08 | 2021-07-09 | 浙江中烟工业有限责任公司 | Warehouse robot indoor map navigation system based on laser radar |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111880543A (en) * | 2020-08-05 | 2020-11-03 | 蒙泽新 | Indoor robot positioning control system based on UWB |
| CN112113565A (en) * | 2020-09-22 | 2020-12-22 | 温州科技职业学院 | Robot positioning system for agricultural greenhouse environment |
| CN112179332A (en) * | 2020-09-30 | 2021-01-05 | 劢微机器人科技(深圳)有限公司 | Hybrid positioning method and system for unmanned forklift |
| CN112797986A (en) * | 2021-02-07 | 2021-05-14 | 江西省智能产业技术创新研究院 | Intelligent logistics robot positioning system and method based on unmanned autonomous technology |
| CN112965494A (en) * | 2021-02-09 | 2021-06-15 | 武汉理工大学 | Control system and method for pure electric automatic driving special vehicle in fixed area |
| CN113093761A (en) * | 2021-04-08 | 2021-07-09 | 浙江中烟工业有限责任公司 | Warehouse robot indoor map navigation system based on laser radar |
| CN113282042A (en) * | 2021-05-28 | 2021-08-20 | 广东广兴牧业机械设备有限公司 | Control system and control method of intelligent dung cleaning robot in pig house |
| CN114593728A (en) * | 2022-03-29 | 2022-06-07 | 青岛科技大学 | Robot positioning system based on multi-sensor fusion |
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