CN110597272A - Intelligent unmanned forklift system and method based on visual navigation - Google Patents
Intelligent unmanned forklift system and method based on visual navigation Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0234—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons
- G05D1/0236—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using optical markers or beacons in combination with a laser
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0242—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using non-visible light signals, e.g. IR or UV signals
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
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Abstract
The invention relates to an intelligent unmanned forklift system and method based on visual navigation, which comprises a forklift main body, an input module, an output module and a traveling crane computer, wherein the input module and the output module are connected with the traveling crane computer and are installed on a forklift main body together; the input module comprises a pull rope type distance meter, an electronic compass, a camera, an infrared sensor, a pressure sensor and a laser distance meter; the output module comprises an arduino micro-control module, a stepping motor driver, an analog input/output module, a 16-path switching value control module, an alarm, a wifi transmission module and a cloud service data processing center; the modules are combined with each other to form an intelligent unmanned forklift system, so that the integrated unmanned operation of unmanned movement, movement correction, visual navigation, goods forking and obstacle detection of the unmanned forklift can be realized, the efficiency of the logistics field can be improved, and a large amount of labor cost can be saved.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to an intelligent unmanned forklift system and method based on visual navigation, which are applied to the field of logistics.
Background
In the world, with the rise of intelligent transformation, the industry tends to be intelligent and automatic. In many industrial production logistics transportation, a forklift is basically used as a transportation tool, but the forklift is operated manually, so that the cost is increased to a certain extent, and the efficiency is slowly improved. Therefore, many enterprises want to select an unmanned integral carrying system, and the unmanned device which needs to be developed in the system is the unmanned forklift, because the advantages of the unmanned forklift relative to the common forklift are more obvious. The unmanned forklift can achieve improvement of material handling efficiency by almost unlimited customized options and minimization of investment, resources are saved, manpower resources can be optimized through unmanned handling operation, energy utilization is optimized, and material handling can be performed in an area.
At present, many inventors of unmanned forklift development are invented and created, for example, chinese patent (application number: 201711341489.9) discloses a vision-based unmanned forklift navigation system and a positioning navigation method thereof, which mainly explains an unmanned forklift navigation method for determining and detecting a target, and lacks methods of unmanned movement, goods forking, forklift direction correction, obstacle detection and the like of a forklift, so that the methods are not perfect. A binocular vision navigation forklift type AGV control system and method is disclosed in chinese patent (application number: 201910297824.2), which only describes a vision navigation method applicable to such a scene of AGV, and does not describe a vision navigation and unmanned method of a general forklift.
Therefore, aiming at the existing problems, the invention provides an intelligent unmanned forklift system and method based on visual navigation, and the intelligent unmanned forklift system and method can realize integrated unmanned operation of forklift such as visual navigation, unmanned driving, obstacle detection, goods forking, movement direction correction and the like.
Disclosure of Invention
The invention aims to provide an intelligent unmanned forklift system and method based on visual navigation, the system can effectively improve the working efficiency in the field of logistics, reduce the production cost, realize unmanned navigation and positioning, and is accurate in navigation and positioning, low in error rate and strong in timeliness.
The invention adopts the following technical scheme for realizing the purpose:
an intelligent unmanned forklift system and method based on visual navigation are characterized in that: the forklift comprises a forklift body, an input module, an output module and a traveling crane computer, wherein the input module and the output module are connected with the traveling crane computer and are installed on the forklift body;
the input module comprises a pull rope type distance meter, an electronic compass, a camera, an infrared sensor, a pressure sensor and a laser distance meter, and the input module acquires data and inputs the data to a traveling computer for calculation to obtain a result; the device comprises a pull rope type range finder, an electronic compass, a camera, a pressure sensor, a laser range finder and a control system, wherein the pull rope type range finder is arranged on a transverse shaft of a rear wheel and used for reading a deflection angle of the rear wheel of the forklift in rotation, the electronic compass reads a magnetic field angle of the forklift, the camera reads visual image data of the movement direction of the forklift, the infrared sensor detects an obstacle in front of the forklift in the movement process, the pressure sensor obtains the weight of goods on a fork shovel, and the laser range finder is used;
the output module comprises an arduino micro-control module, a stepping motor driver, an analog input/output module, a 16-path switching value control module, an alarm, a wifi transmission module and a cloud service data processing center; the output module acquires a calculation result from the driving computer and realizes corresponding operation, wherein the arduino micro control module, the stepping motor driver, the analog input and output module and the 16-way switching value control module are collectively called as a control module, the control module controls unmanned movement and steering of the forklift, the alarm reminds the forklift to move ahead to form an obstacle, the wifi transmission module and the cloud service data processing center upload visual image data acquired by the camera from the driving computer to the cloud service data processing center in real time, target detection of goods in the image is completed at the cloud service data processing center, the result is returned to the driving computer, and visual navigation is provided for the unmanned forklift in real time.
Preferably, the invention provides a motion control method of an intelligent unmanned forklift, which is mainly used for realizing forward motion, backward motion, forward left-turn motion, forward right-turn motion, backward left-turn motion and backward right-turn motion of the forklift; the method is mainly realized by a 16-path switching value control module, an analog input/output module and an arduino micro-control module; the 16-path switching value control module is used for controlling the forward and backward movement of the forklift and the on-off of throttle signals, namely setting a forward gear, a backward gear and a neutral gear, receiving a driving computer instruction, transmitting a digital signal to a forklift motor, and setting the motor in a corresponding gear; the analog input/output module controls the size of the accelerator, receives the instruction of a traveling computer and transmits an analog signal to the accelerator of the forklift so as to control the speed of the forklift; the arduino micro control module controls a stepping motor driver, and the stepping motor driver is used for controlling a stepping motor and further controlling the rotation of a steering wheel, namely the steering of the forklift.
Preferably, the invention provides a method for correcting the deviation of the forklift in the movement process of the intelligent unmanned forklift, and the forklift always deviates from the original navigation direction to drive due to the influence of the driving resistance in the movement process;
the method comprises the steps of firstly reading a reading of an electronic compass, transmitting the reading to a traveling computer, calculating a difference value between a magnetic field angle of a current forklift and a magnetic field angle of a navigation direction, calculating the difference value through the traveling computer, sending an instruction to an arduino micro control module, controlling a stepping motor driver through the arduino micro control module, controlling the stepping motor driver to rotate to enable a steering wheel to rotate to cause the body to return, finally reading the telescopic length of a pull rope type distance meter, converting the telescopic length into a deflection angle of a rear wheel of the forklift through calculation of the traveling computer, sending a corresponding instruction to the arduino micro control module through the traveling computer, controlling the stepping motor driver through the arduino micro control module, and controlling the stepping motor driver to rotate to enable the steering wheel to rotate to cause the rear wheel to return.
Preferably, the invention provides a visual navigation method in the movement process of an intelligent unmanned forklift, which mainly comprises the steps of continuously acquiring visual image data through a camera, uploading the acquired data to a cloud service data processing center through a wifi transmission module, continuously processing the visual images by the cloud service data processing center through a deep learning algorithm to complete target detection of goods, then returning the result to a traveling computer, and sending a corresponding instruction to a control module by the traveling computer to further control the movement direction and navigation of the forklift.
Preferably, the invention provides a method for accurately forking goods by an intelligent unmanned forklift, which mainly controls the forking of goods by combining a 16-path switching value control module, a pressure sensor and a laser range finder; firstly, a laser range finder acquires whether the distance between a forklift and a target cargo is within a preset threshold range, if so, a 16-way switching value control module is opened to control a port of a fork shovel, the fork shovel is lowered to fork the cargo, the weight of the cargo is continuously acquired by using a pressure sensor in the process of forking the cargo, whether the cargo is in all forks is determined, and if the weight of the cargo is reached, the cargo in all forks of the fork shovel is indicated.
Preferably, the invention provides a method for detecting whether an obstacle exists in front of the intelligent unmanned forklift in the moving process, the method is to detect whether the obstacle exists in front of the intelligent unmanned forklift in the moving process of the forklift in real time by using an infrared sensor, once the obstacle is found, a running computer directly transmits a command to control the forklift to move forward, and all motion control systems are closed to cause an alarm to respond.
Has the advantages that: the invention provides an intelligent unmanned forklift system based on visual navigation, which has more sensitive environment perception capability, and compared with the prior art, the intelligent unmanned forklift system based on visual navigation has the following advantages and effects:
(1) the system has an independent unmanned motion method, and basically realizes the unmanned motion of the forklift;
(2) the system provides a real-time effective forklift movement deviation rectifying method, and can solve the problem that the forklift generally runs in a deviation direction in the movement process;
(3) compared with a GPS positioning navigation method, the method has the advantages of high navigation precision, strong sensitivity, cost saving and convenient use;
(4) the system also provides a method for goods forking and obstacle detection, and provides good support and guarantee for the working efficiency and safety of the forklift;
the system can realize the integrated unmanned operation of visual navigation, unmanned driving, obstacle detection, cargo forking and deviation correction of the movement direction of the forklift, provides great help for the defects of the prior art of the unmanned forklift, can improve the efficiency in the field of logistics, and saves a large amount of labor cost.
Drawings
FIG. 1 is a system hardware block diagram of the present invention;
FIG. 2 is a schematic flow chart of the operation of the system of the present invention;
FIG. 3 is a schematic flow chart of the motion control method of the present invention;
FIG. 4 is a flow chart illustrating a method of correcting forklift deflection during movement according to the present invention;
FIG. 5 is a flow chart diagram of a visual navigation method of the present invention;
FIG. 6 is a schematic flow diagram of a method of precision forking of a cargo of the present invention;
fig. 7 is a schematic flow chart of the obstacle detection method of the present invention.
Detailed Description
The invention is further illustrated by the following specific examples.
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Example 1: as shown in the attached figure 1, the forklift truck comprises a forklift truck main body, an input module, an output module and a traveling crane computer, wherein the input module and the output module are connected with the traveling crane computer;
the input module comprises a pull rope type distance meter, an electronic compass, a camera, an infrared sensor, a pressure sensor and a laser distance meter, and the input module acquires data and inputs the data to a traveling computer for calculation to obtain a result;
the device comprises a pull rope type range finder, an electronic compass, a camera, a pressure sensor, a laser range finder and a control system, wherein the pull rope type range finder is arranged on a transverse shaft of a rear wheel and used for reading a deflection angle of the rear wheel of the forklift in rotation, the electronic compass reads a magnetic field angle of the forklift, the camera reads visual image data of the movement direction of the forklift, the infrared sensor detects an obstacle in front of the forklift in the movement process, the pressure sensor obtains the weight of goods on a fork shovel, and the laser range finder is used;
the output module comprises an arduino micro-control module, a stepping motor driver, an analog input/output module, a 16-path switching value control module, an alarm, a wifi transmission module and a cloud service data processing center; the output module acquires a calculation result from the driving computer and realizes corresponding operation;
wherein arduino micro control module, step motor driver, analog input/output module, 16 way switching value control module is collectively called control module, this control module control fork truck's unmanned motion and turn to, the siren reminds fork truck motion the place ahead to have the barrier, wifi transmission module and cloud service data processing center are real-time to upload to cloud service data processing center from the visual image data that driving computer obtained the camera, and accomplish the target detection of goods in the image at cloud service data processing center, and return the result for driving computer, provide visual navigation in real time for unmanned fork truck.
Example 2: as shown in fig. 2, a schematic diagram of the operation flow of the whole system is shown, and the whole operation steps are as follows:
step S21: firstly, judging whether the forklift is in a static state, and if so, starting an intelligent unmanned forklift system;
step S22: after the intelligent unmanned forklift system is started, initializing operation is executed, a 16-path switching value control module, an analog input/output module and an arduino micro-control module are respectively initialized, whether the initialization operation is finished or not is judged, and if yes, the next step S23 is executed; if not, returning to the initialization operation;
step S23: after initialization is completed, the forklift starts to move, in the moving process, an infrared sensor is used for detecting a front obstacle in the moving process of the forklift, meanwhile, a camera is used for detecting target goods in real time, and then a laser range finder is used for detecting the distance between the forklift and the target goods;
step S24: when the forklift moves, the target goods are locked and the distance between the target goods and the target goods is detected, and meanwhile, whether the forklift is deviated or not is judged by utilizing the pull rope type distance meter and the electronic compass, if yes, the deviation of the movement is corrected; if not, the next step S25 is executed;
step S25: and (3) the forklift continues to advance, when the laser range finder detects that the distance between the forklift and the target goods is within a threshold range, the terminal is reached, meanwhile, the 16-way switching value control module is opened to control the port of the fork shovel, the goods are successfully forked by using the pressure sensor, finally, the intelligent forklift system is closed, and the operation is finished.
Example 3: as shown in fig. 3, a flow chart of the motion control method of the present invention is shown, and the whole operation process is implemented in the whole system operation process, and the steps are as follows:
step S31: firstly, the system starts to operate, a traveling computer is used for sending an instruction, and the instruction is transmitted to three modules, namely a 16-path switching value control module, an analog input/output module and an arduino micro-control module;
step S32: then, a 16-path switching value control module is used for controlling the forward and backward of the forklift and the on-off of an accelerator signal; the analog input/output module is used for controlling the size of an accelerator and the speed of the forklift; and controlling a stepping motor driver by using the arduino micro-control module, wherein the stepping motor driver controls a stepping motor, so that the whole operation process is finished.
Example 4: as shown in fig. 4, a flow diagram of the method for correcting the deviation of the forklift in the movement process of the present invention is given, and the whole operation process is realized in the whole system operation process, and the steps are as follows:
step S41: firstly, starting system operation, continuously reading the reading of the electronic compass in real time in the motion process, and returning the reading to a traveling crane computer;
step S42: the traveling crane computer calculates the difference value between the reading and the target angle;
step S43: the running computer sends a corresponding instruction to the arduino micro-control module according to the difference value;
step S44: the arduino micro-control module controls a stepping motor driver to control the stepping motor to operate, so that a steering wheel is rotated to enable the vehicle body to be aligned;
step S45: reading the reading of the pull rope type distance meter and returning the reading to the traveling crane computer;
step S46: the traveling crane computer calculates the deflection angle of the rear wheel according to the reading of the pull rope type distance meter;
step S47: the driving computer sends corresponding instructions to the arduino micro-control module according to the calculated deflection angle of the rear wheel, so that the steering wheel is rotated to enable the tire to be aligned, and the whole operation process is finished.
Example 5: as shown in fig. 5, a flow chart of the visual navigation method of the present invention is shown, and the whole operation process is implemented in the whole system operation process, and the steps are as follows:
step S51: firstly, starting system operation, and starting a camera to continuously acquire visual image data of target goods in real time;
step S52: transmitting the acquired visual image data to a cloud service data processing center through a wifi communication module;
step S53: the cloud service data processing center processes data by using a deep learning target detection algorithm, identifies the position of a cargo in an image, and obtains the result;
step S54: returning the result of the accurate identification and detection of the target cargo to the traveling crane computer through the wifi communication module;
step S55: and the traveling crane computer sends an instruction to the corresponding system module according to the obtained corresponding identification result, so that the navigation and positioning of the forklift are completed, and the whole operation process is finished.
Example 6: as shown in fig. 6, a schematic flow chart of the method for accurately forking goods according to the present invention is shown, and the whole operation process is implemented in the whole system operation process, and the steps are as follows:
step S61: firstly, starting system operation, and uninterruptedly detecting the distance between a forklift and goods by a laser range finder in real time;
step S62: in the process of detecting the distance, the traveling crane computer always judges whether the distance is within a set threshold range, if so, the traveling crane computer controls the forklift to stop moving and controls the fork shovel to fall down; if not, controlling the forklift to continue to advance until the distance is within the threshold range;
step S63: after the fork gets the goods, detect goods weight through pressure sensor, in case reach the weight of goods, in then the article fork, whole operation process is ended.
Example 7: fig. 7 is a schematic flow chart of the obstacle detection method of the present invention, wherein the whole operation process is implemented in the whole system operation process, and the steps are as follows:
step S71: firstly, the system starts to operate, the infrared sensor continuously detects whether an obstacle exists in the advancing direction of the forklift in real time, and if so, the next step S72 is operated; if not, the forklift continues to move forwards;
step S72: when the obstacle in front is detected, the obstacle is returned to the traveling crane computer, the traveling crane computer controls to interrupt all the motion control modules and complete the emergency braking operation, and meanwhile, the alarm responds, and the whole operation process is finished.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. An intelligent unmanned forklift system and method based on visual navigation are characterized in that: the forklift comprises a forklift body, an input module, an output module and a traveling crane computer, wherein the input module and the output module are connected with the traveling crane computer and are installed on the forklift body;
the input module comprises a pull rope type distance meter, an electronic compass, a camera, an infrared sensor, a pressure sensor and a laser distance meter, and the input module acquires data and inputs the data to a traveling computer for calculation to obtain a result;
the device comprises a pull rope type range finder, an electronic compass, a camera, a pressure sensor, a laser range finder and a control system, wherein the pull rope type range finder is arranged on a transverse shaft of a rear wheel and used for reading a deflection angle of the rear wheel of the forklift in rotation, the electronic compass reads a magnetic field angle of the forklift, the camera reads visual image data of the movement direction of the forklift, the infrared sensor detects an obstacle in front of the forklift in the movement process, the pressure sensor obtains the weight of goods on a fork shovel, and the laser range finder is used;
the output module comprises an arduino micro-control module, a stepping motor driver, an analog input/output module, a 16-path switching value control module, an alarm, a wifi transmission module and a cloud service data processing center; the output module acquires a calculation result from the driving computer and realizes corresponding operation;
wherein arduino micro control module, step motor driver, analog input/output module, 16 way switching value control module is collectively called control module, this control module control fork truck's unmanned motion and turn to, the siren reminds fork truck motion the place ahead to have the barrier, wifi transmission module and cloud service data processing center are real-time to upload to cloud service data processing center from the visual image data that driving computer obtained the camera, and accomplish the target detection of goods in the image at cloud service data processing center, and return the result for driving computer, provide visual navigation in real time for unmanned fork truck.
2. The intelligent unmanned forklift system and method based on visual navigation according to claim 1, characterized in that: the arduino micro control module, the 16-path switching value control module, the analog input/output module and the stepping motor driver are combined to control the forward movement, the backward movement, the forward left-turn movement, the forward right-turn movement, the backward left-turn movement and the backward right-turn movement of the forklift.
3. The intelligent unmanned forklift system and method based on visual navigation according to claim 1, characterized in that: in order to solve the problem that the forklift can deviate from the original navigation direction to drive under the influence of driving resistance in the movement process, the arduino micro-control module can also be combined with an electronic compass and a pull rope type distance meter to realize the deviation rectifying operation of the movement direction of the forklift;
the method comprises the steps of firstly reading a reading of an electronic compass, transmitting the reading to a traveling computer, calculating a difference value between a magnetic field angle of a current forklift and a magnetic field angle of a navigation direction, calculating the difference value through the traveling computer, sending an instruction to an arduino micro control module, controlling a stepping motor driver through the arduino micro control module, controlling the stepping motor driver to rotate to enable a steering wheel to rotate to cause the body to return, finally reading the telescopic length of a pull rope type distance meter, converting the telescopic length into a deflection angle of a rear wheel of the forklift through calculation of the traveling computer, sending a corresponding instruction to the arduino micro control module through the traveling computer, controlling the stepping motor driver through the arduino micro control module, and controlling the stepping motor driver to rotate to enable the steering wheel to rotate to cause the rear wheel to return.
4. The intelligent unmanned forklift system and method based on visual navigation according to claim 1, characterized in that: the visual navigation is mainly characterized in that visual image data are continuously acquired through a camera, the acquired data are uploaded to a cloud service data processing center through a wifi transmission module, the cloud service data processing center continuously utilizes a deep learning algorithm to process visual images to complete target detection of goods, then results are returned to a traveling computer, and the traveling computer sends corresponding instructions to a control module to control the movement direction and navigation of the forklift.
5. The intelligent unmanned forklift system and method based on visual navigation according to claim 1, characterized in that: the 16-path switching value control module, the pressure sensor and the laser range finder are combined to control the goods forking process; firstly, a laser range finder acquires whether the distance between a forklift and a target cargo is within a preset threshold range, if so, a 16-way switching value control module is opened to control a port of a fork shovel, the fork shovel is lowered to fork the cargo, the weight of the cargo is continuously acquired by using a pressure sensor in the process of forking the cargo, whether the cargo is in all forks is determined, and if the weight of the cargo is reached, the cargo in all forks of the fork shovel is indicated.
6. The intelligent unmanned forklift system and method based on visual navigation according to claim 1, characterized in that: the obstacle detection means that whether an obstacle exists in front is detected through an infrared sensor in the movement process of the forklift, once the obstacle exists in front of the movement, a running computer directly sends an instruction to control the forklift to move forwards, all movement control systems are closed, and an alarm is caused to respond.
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| CN201911009779.2A CN110597272A (en) | 2019-10-23 | 2019-10-23 | Intelligent unmanned forklift system and method based on visual navigation |
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| CN111115511A (en) * | 2019-12-31 | 2020-05-08 | 广东电网有限责任公司 | Unloading and loading method based on intelligent navigation forklift |
| CN111634859A (en) * | 2020-06-10 | 2020-09-08 | 江苏省特种设备安全监督检验研究院 | Intelligent forklift control system |
| CN113104776A (en) * | 2021-05-13 | 2021-07-13 | 湖北奥瑞金制罐有限公司 | Tin printing scheduling system and method based on unmanned forklift |
| CN113552868A (en) * | 2020-04-22 | 2021-10-26 | 西门子股份公司 | Navigation method and navigation device of fire-fighting robot |
| CN114137986A (en) * | 2021-11-29 | 2022-03-04 | 中兴耀维科技江苏有限公司 | Autonomous operation platform of unmanned forklift |
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