A wireless networking-based unmanned carrying vehicle, control method device and system

Abstract

The embodiment of the application discloses a wireless networking-based unmanned carrying vehicle, a control method device and system. A first positioning data of a first wireless tag within a preset detection angle is acquired through a wireless positioning control system, a vehicle body control system is used for determining a target instruction speed according to the first positioning data and a preset first PD control algorithm, and determining a target steering angular velocity according to the first positioning data and a preset integral separation PID control algorithm, a motor driving system determines left and right drive wheel speeds based on an Ackerman steering model corresponding to an Ackerman steering mechanism, the target instruction speed and the target steering angular velocity, and controls the carrying vehicle to move according to the target steering angular velocity and the left and right drive wheel speeds. The integral separation PID control algorithm can effectively solve the overshoot problem caused by integral saturation when the carrying vehicle is steering at a large angle, realize static error compensation in the driving process, and improve the precision of speed control.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, and in particular to an unmanned transport vehicle, control method, device and system based on wireless networking. Background Technology

[0002] Agricultural material handling operations are limited by objective factors such as poor field road conditions and low traffic volume. At the same time, advanced fully automated instruments and equipment are difficult to popularize due to problems such as high operating costs and low adaptability to site and environment. Currently, rural areas still generally use simple carts for manual handling, which results in low operating efficiency and high labor costs.

[0003] The existing autonomous vehicles in the logistics and transportation field mainly rely on satellite positioning or ground fixed base stations for communication transmission in their intelligent control systems. These unmanned intelligent delivery vehicles based on 5G communication technology, coupled with drone platforms, can transport goods. However, this type of technology has high requirements for the positioning environment and is easily affected by factors such as weather, time, signal blockage, and multipath effects. In agricultural transportation environments, there are problems with unstable communication signals, and it is difficult to achieve stable following effects when multiple transport vehicles are linked together. Summary of the Invention

[0004] This application provides an unmanned transport vehicle based on wireless networking, a control method, device, and system to solve the technical problem in the related art that the communication signal of the transport vehicle based on 5G communication technology is unstable in the agricultural transportation environment, and it is difficult to achieve a stable following effect when multiple transport vehicles are connected in a chain. It can effectively improve the stable following effect when multiple transport vehicles are connected in a chain.

[0005] In a first aspect, embodiments of this application provide a wirelessly networked unmanned agricultural transport vehicle, including a vehicle control system, a wireless positioning control system, and a motor drive system. The wireless positioning control system and the motor drive system are respectively communicatively connected to the vehicle control system. The transport vehicle is steered using an Ackermann steering mechanism, and the motor drive system is connected to the Ackermann steering mechanism, wherein: The wireless positioning control system is used to acquire first positioning data of a first wireless tag within a preset detection angle, and send the first positioning data to the vehicle control system. The vehicle control system is used to determine the target command speed based on the first positioning data and the preset first PD control algorithm, and to determine the target steering angle based on the first positioning data and the preset integral separation PID control algorithm, and to send the target command speed and the target steering angle to the motor drive system. The integral separation PID control algorithm does not enable the integral term when the steering angle error is greater than the preset angle error threshold. The motor drive system is used to determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, and to control the movement of the transport trolley according to the target steering angular velocity and the speeds of the left and right drive wheels.

[0006] In a second aspect, embodiments of this application provide a control method for a wirelessly networked unmanned agricultural transport vehicle, applied to the wirelessly networked unmanned agricultural transport vehicle as described in any of the first aspects, comprising: The wireless positioning control system acquires the first positioning data of the first wireless tag within a preset detection angle. The target command speed is determined based on the first positioning data and the preset first PD control algorithm, and the target steering angular velocity is determined based on the first positioning data and the preset integral separation PID control algorithm, wherein the integral separation PID control algorithm does not enable the integral term when the steering angle error is greater than the preset angle error threshold. The speeds of the left and right drive wheels are determined based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. The movement of the transport trolley is controlled according to the target steering angular velocity and the speeds of the left and right drive wheels.

[0007] In a third aspect, embodiments of this application provide a control device for a wirelessly networked unmanned agricultural transport vehicle, applied to the wirelessly networked unmanned agricultural transport vehicle as described in any of the first aspects, comprising a positioning acquisition module, an analysis and processing module, and a control execution module, wherein: The positioning acquisition module is used to acquire the first positioning data of the first wireless tag within a preset detection angle through the wireless positioning control system. The analysis and processing module is used to determine the target command speed based on the first positioning data and the preset first PD control algorithm, and to determine the target steering angular velocity based on the first positioning data and the preset integral separation PID control algorithm, wherein the integral separation PID control algorithm does not enable the integral term when the steering angle error is greater than the preset angle error threshold. The control execution module is used to determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, and to control the movement of the transport trolley according to the target steering angular velocity and the speeds of the left and right drive wheels.

[0008] In a fourth aspect, embodiments of this application provide a wireless network-based unmanned farmland transport vehicle system, including multiple wireless network-based unmanned farmland transport vehicles as described in any of the first aspects, wherein the multiple wireless network-based unmanned farmland transport vehicles are connected in a chain network through the wireless positioning control system.

[0009] This application embodiment acquires the first positioning data of the first wireless tag within a preset detection angle through a wireless positioning control system. The vehicle control system determines the target command speed based on the first positioning data and a preset first PD control algorithm, and determines the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm. The motor drive system determines the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. The system controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels, thereby enabling the transport trolley to automatically follow the first wireless tag in front and achieving a stable following effect under the chain connection of multiple transport trolleys. The integral separation PID control algorithm does not activate the integral term when the steering angle error is greater than a preset angle error threshold, which can effectively solve the overshoot problem caused by integral saturation when the transport trolley turns at large angles, realize static error compensation during driving, and improve the accuracy of speed control. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of a wireless network-based unmanned transport vehicle provided in an embodiment of this application; Figure 2 This is a schematic diagram of an Ackermann steering mechanism provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the communication principle of a transport trolley provided in an embodiment of this application; Figure 4 This is a schematic diagram of the geometric model of the Ackermann steering mechanism provided in the embodiments of this application; Figure 5 This is a schematic diagram showing the locational relationship between a wireless base station and a wireless tag. Figure 6 This is a flowchart of a control method for an unmanned transport vehicle based on wireless networking, provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a wireless networking-based unmanned transport vehicle control device provided in an embodiment of this application; Figure 8 This is a schematic diagram of a wireless networking-based unmanned farmland transport vehicle system provided in an embodiment of this application; Figure 9 This is a schematic diagram of another unmanned farmland transport vehicle system based on wireless networking provided in an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but additional steps not included in the drawings may also be present. The above processes can correspond to methods, functions, procedures, subroutines, subroutines, etc.

[0012] Existing agricultural transport vehicles generally focus on mechanical structure design, with overall vehicle development centered around the design and operation of anti-collision and shock-absorbing mechanical devices and object-grabbing and lifting devices. They primarily focus on the stability of object grasping and the safety of material transportation during vehicle operation, neglecting the safety protection of the vehicle's battery power system on complex road surfaces and failing to consider improving transportation time efficiency, making them unsuitable for the complex operating environment of farmland. Existing autonomous vehicles in the logistics and transportation field primarily rely on satellite positioning or fixed ground base stations for communication transmission in their intelligent control systems. For example, unmanned intelligent delivery vehicles based on 5G communication technology, combined with drone platforms, can transport materials. However, such technical solutions have high requirements for the positioning environment and are easily affected by factors such as weather, time, signal blockage, and multipath effects. In the agricultural transportation environment, communication signal instability is a problem, making it difficult to guarantee transportation efficiency.

[0013] Based on this, this application provides an unmanned transport vehicle based on wireless networking to solve the above-mentioned technical problems. This application provides an unmanned transport vehicle for farmland based on wireless positioning technology (such as Ultra Wide Band (UWB) positioning technology). The transport vehicle can use UWB positioning technology with Phase Difference of Arrival (PDOA) algorithm to construct a single master vehicle, multiple slave vehicles, and multiple base stations operation mode, realizing autonomous platooning and automatic driving. The master vehicle can automatically follow the wireless tag (such as UWB electronic tag) and automatically avoid obstacles based on the path of the preceding vehicle. Multiple slave vehicles can automatically follow the preceding vehicle, forming a chain-like expanding operation platoon. The UWB positioning technology based on the PDOA positioning algorithm can ensure the positioning accuracy of the vehicle in complex terrains such as tall crops and trellis planting, with positioning accuracy reaching the centimeter level. This effectively solves the problems of weak anti-interference capability and susceptibility to environmental factors in satellite positioning, realizing precise farmland transport operations. Simply by having a portable, loadable, and mobile UWB base station controller worn by staff or installed on the main vehicle, along with UWB tags for the slave vehicles, the intelligent farm transport vehicle can automatically follow the tags. This effectively solves the problems of inflexible steering and uneven power distribution in traditional trailer-mounted transport vehicles, which lead to driving difficulties. It also effectively solves the problem that large material transport vehicles and unmanned farm transport robots cannot enter complex and confined environments or carry out operations between rows of sheds. At the same time, it overcomes the problems of incomplete deployment of farmland signal base stations and the inability of high-precision equipment to adapt to farmland scenarios. It has extremely strong adaptability to farmland scenarios. It can not only solve the problem that large material transport vehicles and unmanned farm transport robots cannot enter complex and confined environments or carry out operations between rows of sheds, but also solve the problem of incomplete deployment of farmland signal base stations and the inability to use high-precision equipment, making it more suitable for farmland scenarios.

[0014] Figure 1 A principle block diagram of an unmanned transport vehicle based on wireless networking, according to an embodiment of this application, is provided. (Refer to...) Figure 1 The wireless networking-based unmanned transport vehicle includes a vehicle control system, a wireless positioning control system, and a motor drive system. The vehicle control system, wireless positioning control system, and motor drive system can be installed on the transport vehicle (the wireless networking-based unmanned transport vehicle). The wireless positioning control system and the motor drive system are respectively connected to the vehicle control system via wired and / or wireless communication.

[0015] The wireless positioning control system provided in this application can be configured to face the front of the transport trolley, and can be used to detect wireless tags within a preset detection angle (e.g., 120°) in the detection direction, and communicate with the detected wireless tags. For example, the wireless positioning control system can be a UWB positioning control system, the wireless tag can be a UWB tag, the wireless positioning control system can be a 433MHz wireless system, and the wireless tag can be a 433MHz tag. In one embodiment, the wireless positioning control system can acquire first positioning data of a first wireless tag within the preset detection angle and send the first positioning data to the vehicle control system. The first positioning data can be used to reflect the position of the first wireless tag relative to the wireless positioning control system.

[0016] In one embodiment, the wireless positioning control system can be configured using UWB positioning technology based on the PDOA positioning algorithm. A DW3000 chip, which combines Time Difference of Arrival (TDoA) positioning and PDOA positioning functions, is used to build the UWB module, improving the positioning accuracy of the wireless positioning control system. By acquiring the relative coordinates of UWB tags within a 120° range directly in front of the transport vehicle, multiple transport vehicles can be linked together in a chain network through the wireless positioning control system. The transport vehicles can follow each other based on distance and angle using UWB positioning technology. For example, proportional-integral (PI) control can be applied to the following distance and angle deviation to achieve platooning and following functionality. Simultaneously, the UWB base station and tags can communicate via a 2.4G wireless module network, transmitting motion control parameters such as platooning sensitivity, following distance, and upper limit of following speed in real time. The master and slave vehicles automatically calculate the differential speed between the inner and outer wheels of the left and right drive motors based on parameter changes, achieving vehicle motion control and automatic distance measurement and following functions.

[0017] In one embodiment, the wireless positioning control system provided in this application includes a wireless base station (e.g., a UWB base station) and a second wireless tag (e.g., a UWB tag). The wireless base station can send a first ranging request (i.e., a ranging request to a preceding wireless tag) to a first wireless tag within a preset detection angle to obtain first positioning data. The second wireless tag, upon receiving a second ranging request from another transport trolley following behind, returns tag coordinate data for the other transport trolleys to determine the second positioning data. For example, when multiple transport trolleys are connected in a chain network, the wireless base station on each transport trolley can send a first ranging request to the preceding first wireless tag to obtain the first positioning data corresponding to the first wireless tag. The second wireless tag on each transport trolley can also receive a second ranging request from the wireless base station on a following transport trolley (in which case the second wireless tag is equivalent to the corresponding first wireless tag on the following transport trolley), so that the wireless base station on the following transport trolley can obtain the second positioning data of the second wireless tag relative to the wireless base station on the following transport trolley.

[0018] Optionally, the first wireless tag is located on another preceding transport cart or on a preceding moving target. For example, when multiple transport carts are connected in a chain network, the first wireless tag can be placed on the foremost transport cart, where the foremost transport cart is the master cart and the other transport carts are slave carts. In this case, the multiple transport carts connected in the chain network move with the master cart. Alternatively, the first wireless tag can be placed on the foremost moving target of the multiple transport carts connected in the chain network, where the moving target can be a worker or other mobile vehicle. In this case, the multiple transport carts connected in the chain network move with the worker or other mobile vehicle. This application allows the first wireless tag to be placed on another preceding transport cart or a moving target, enabling flexible control of the transport carts following the movement.

[0019] The wireless base station provided in this application can be built based on the GD / STM32F103C8T6 chip. It communicates with the DW3000 PDOA module via a serial port to obtain the raw relative coordinate data provided by the wireless tag. Through data calculation and PI control algorithm, the optimal driving speed and steering angle of the vehicle are calculated. The target speed and angle parameters are sent to the vehicle control system via the controller local area network (CAN) bus or RS485 bus to realize vehicle motion control.

[0020] The wireless tag provided in this application can be built based on the GD / STM32F103C8T6 chip. It communicates with the DWM3000 module via a serial peripheral interface (SPI) to obtain the raw relative coordinate data of the object to which the wireless tag is located. The tag's speed and turning angle are calculated from this data, and then the target speed and angle parameters are transmitted to the UWB base station via a CAN bus or RS485 bus. Optionally, to improve the practicality of the transport vehicle, the wireless tag can be designed as a detachable, independently powered portable structure. The wireless tag can be worn by the operator or connected to the main vehicle, allowing the user to freely choose the following method according to actual operational needs. The wireless tag can be equipped with a TP4065 lithium battery charging chip, featuring a reverse polarity protection circuit for the input power supply to improve charging safety. The TP4065 chip is compatible with both USB power and adapter power, and can automatically adjust the charging current through thermal feedback. It limits the charging temperature during high-power operation or in high ambient temperatures. When the USB charger is removed, the chip automatically enters a low-current state, and the battery leakage current can be controlled below 1 microamp, significantly improving the wireless tag's battery life.

[0021] This application configures a wireless positioning control system comprising a wireless base station and a second wireless tag, enabling multiple transport vehicles to establish a chain-like network connection based on the wireless base station and the second wireless tag. This allows for chain-like following of multiple transport vehicles, ensuring that the heading angle of the wireless base station is always oriented towards the first tag, achieving automatic following in terms of angle and distance. Compared to existing image recognition-based following schemes, this application achieves higher following accuracy and faster response speed, meeting the real-time requirements of farmland operations. Furthermore, the wireless base station can employ UWB positioning technology with the PDOA algorithm to achieve active following functionality for the intelligent farmland unmanned transport vehicles, achieving centimeter-level positioning accuracy. It maintains stable positioning performance in complex terrains such as tall crops and trellis planting, effectively solving the problems of weak anti-interference capability and significant susceptibility to weather, time, signal obstruction, and multipath effects in existing satellite positioning methods, thus enabling precise farmland transport operations.

[0022] In one embodiment, the transport trolley provided in this application can be powered by dual 18V aviation lithium iron phosphate batteries, coupled with a battery management system (BMS) protection circuit and an embedded battery protection structure to achieve safety protection for the battery power system. The transport trolley adopts an Ackermann steering combined with electronic differential steering design, and is equipped with 0.8kW DC brushless dual servo drive motors, providing the trolley with strong and stable power output, enhancing the transport trolley's smooth driving ability and durability in complex agricultural scenarios and on bumpy and muddy road conditions.

[0023] The transport trolley provided in this application is steered based on an Ackermann steering mechanism. The motor drive system is connected to the Ackermann steering mechanism and can control the Ackermann steering mechanism to control the angle of the two wheels connected to the Ackermann steering mechanism. The motor drive system is also connected to the travel drive motor on the transport trolley, which drives the wheels of the transport trolley to rotate, thereby driving the transport trolley to move forward or backward.

[0024] The Ackermann steering mechanism is a geometric design that allows the inner and outer wheels of a vehicle to turn at different angles, enabling the wheels to roll around the same center point, reducing tire wear and improving steering stability. Normally, the inner wheel travels a smaller radius when turning, while the outer wheel has a larger radius. If the left and right front wheels turn at the same angle, the wheels will slip laterally. The Ackermann steering mechanism, through a trapezoidal steering structure, allows the inner front wheel to turn at a slightly larger angle than the outer front wheel, causing the extended axes of the two front wheels to converge at the instantaneous steering center on the extended line of the rear axle. This allows all four wheels to roll purely around the same point, avoiding slippage. In one embodiment, the Ackermann steering mechanism on the transport trolley can be located on either the front or rear wheels. This application describes an example where the Ackermann steering mechanism is located on the front wheels of the transport trolley.

[0025] Among them, the steering system of the transport trolley is related to the trolley's driving safety, driving quality, and the effectiveness of automatic following control of the master and slave vehicles on complex farmland roads. The Ackermann steering mechanism provided in this application, such as... Figure 2 As shown, the servo motor in the Ackermann steering mechanism is connected to the steering tie rod via a swing arm mechanism, providing power for wheel steering. The multi-turn absolute encoder in the Ackermann steering mechanism is connected to the output shaft of the servo motor via gear transmission. The front wheel steering angle is obtained by acquiring the angular displacement of the multi-turn absolute encoder. The steering angle control adopts an integral separation PID control algorithm. This algorithm only activates the integral term when the error is small, effectively solving the overshoot problem caused by integral saturation during large-angle steering (integral saturation during large-angle steering causes the steering angle to exceed the target value, thus affecting the steering accuracy and stability of the vehicle), while realizing static error compensation during driving. This application uses the angular displacement acquired by the multi-turn absolute encoder as a feedback signal and the target steering angle as a set value input to the algorithm. The calculation result is output as the speed command of the servo motor, realizing precise control of the steering angle. For example, the wireless positioning control system obtains the target command speed and command steering angle to control the steering servo and the drive motor. The steering angle is substituted into the Ackermann steering control function to automatically calculate the inner and outer wheel differential of the left and right drive motors, realizing motion control of the vehicle body. This application utilizes an Ackermann steering mechanism and an integral-separated PID control algorithm to maintain a certain driving speed on complex road surfaces while ensuring vehicle balance and stability. It also addresses the overshoot problem during large-angle steering obstacle avoidance, improves the reliability and service life of the steering system, effectively adapts to special operating scenarios such as tall crops and trellis planting, and effectively compensates for static errors during operation.

[0026] In one embodiment, the power system of the transport vehicle provided in this application includes an embedded battery protection structure and a battery BMS protection circuit, achieving safe use of the battery power system in complex farmland environments through dual protection. Figure 2 As shown, the battery pack is fixed to the right side of the vehicle chassis. The bottom of the battery pack is protected with an 8mm thick steel plate to reduce the risk of impact, puncture, and deformation under harsh road conditions. The battery is fully encased in 1mm thick 304 stainless steel sheet metal, which effectively prevents harmful substances such as moisture, salt spray, and dust from entering the BMS and battery circuitry, while also preventing scratches and impacts during transportation and installation. The battery casing is designed with riveting handles for easy battery installation and removal. High-polymer shock-absorbing material is filled between the battery cells and the casing to provide shock absorption and protection for the cells.

[0027] The battery management system (BMS) protection circuit can be built based on the BQ76930 multi-channel battery protection integrated circuit (IC). Utilizing the BQ76930's basic voltage, current, and temperature acquisition functions, the BMS protection circuit reads and processes the acquired data from the BQ76930 registers via an STM32 control chip using integrated circuit bus (IIC) communication. This enables precise management of the battery BMS protection circuit, ensuring safety during battery charging, discharging, and use. The BMS protection circuit can also perform power metering through hardware integration, providing reliable data support for the vehicle control system to accurately calculate the battery's state of charge (SOC). It can also use MOSFETs as charge / discharge switches to execute protection actions, comparing real-time acquired voltage, current, and temperature data with preset thresholds to automatically control the battery charging and discharging process. This constructs a comprehensive battery protection system integrating overvoltage protection, undervoltage protection, overcurrent protection, short circuit protection, overtemperature protection, and low-temperature protection, effectively avoiding battery safety hazards caused by bumps and impacts on complex road surfaces, and ensuring the safety of the handling trolley and personnel. Meanwhile, it can also monitor the remaining battery power in real time, and the operation data of the transport trolley can be uploaded to the remote monitoring terminal simultaneously to realize the visual management of battery status.

[0028] In one embodiment, such as Figure 3 A schematic diagram of the communication principle of a transport trolley is provided. The motor drive system includes a travel motor controller, a steering mechanism encoder, and a steering servo motor. The travel motor controller controls the forward and backward movement of the transport trolley. The steering mechanism encoder detects the angle of the Ackerman mechanism, and the steering servo motor controls the steering of the Ackerman mechanism. Optionally, the wireless positioning control system can use a DW3000 chip to build a UWB module with a positioning accuracy of ±10cm. The wireless base station and the second wireless tag are both based on the STM32F103C8T6 chip, and the network is established through a 2.4G wireless module with a communication distance of ≥50m. The motor controller of the motor drive circuit can be based on the STM32F407 chip, and the central control signal processing board is based on the STM32F103RCT6 chip. Data transmission is achieved using a CAN bus (communication rate 500kbps) and an RS485 bus (baud rate 9600bps).

[0029] The vehicle control system provided in this application is used to determine a target command speed based on first positioning data and a preset first PD control algorithm, and to determine a target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm, and to send the target command speed and target steering angular velocity to the motor drive system. The integral separation PID control algorithm provided in this application does not activate the integral term when the steering angle error is greater than a preset angle error threshold. Optionally, a first distance between the wireless positioning control system and the first wireless tag can be determined based on the first positioning data, and the first distance and a preset target distance can be input into the preset first PD control algorithm. Based on the first PD control algorithm, the corresponding target command speed is output so that the subsequently detected first distance approaches the target distance. Optionally, a first angle between the wireless positioning control system and the first wireless tag can be determined based on the first positioning data, and the first angle and a preset target angle can be input into the preset integral separation PID control algorithm. Based on the integral separation PID control algorithm, the corresponding target steering angular velocity is output so that the subsequently detected first angle approaches the target angle.

[0030] The motor drive system is used to determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. It then controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels. For example, the target steering angular velocity is input into the Ackerman steering model, and the speed difference between the inner and outer wheels of the left and right wheels of the transport trolley is calculated using the Ackerman steering mathematical model. The speeds of the left and right drive wheels are then calculated based on the target command speed and the speed difference between the inner and outer wheels.

[0031] In one embodiment, the vehicle control system may also be used to determine the target command speed based on the first positioning data and the preset first PD control algorithm, and to determine the target steering angular velocity based on the first positioning data and the preset integral separation PID control algorithm, and to determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed and the target steering angular velocity, and then send the target steering angular velocity and the speeds of the left and right drive wheels to the motor drive system so that the motor drive system controls the movement of the transport trolley according to the target steering angular velocity and the speeds of the left and right drive wheels.

[0032] In one possible embodiment, when determining the target steering angular velocity based on the first positioning data and the preset integral separation PID control algorithm, this application may: acquire the current orientation angle and the target orientation angle of the transport trolley, determine the steering angle error based on the current orientation angle and the target orientation angle, and compare the steering angle error with a preset angle error threshold (e.g., 0°).

[0033] In one embodiment, when the steering angle error is greater than a preset angle error threshold, the target steering angle velocity is determined based on the first positioning data and a preset second PD control algorithm. That is, the integral term is not activated when the steering angle error is greater than the preset angle error threshold, thereby reducing overshoot caused by integral saturation during large-angle steering. When the steering angle error is less than or equal to the preset angle error threshold, the target steering angle velocity is determined based on the first positioning data and a preset PID control algorithm. That is, the integral term is activated when the steering angle error is less than or equal to the preset angle error threshold, improving the trolley's angle following speed. This application determines whether to activate the integral term based on the comparison result between the steering angle error and the preset angle error threshold, improving the trolley's angle following speed while reducing overshoot caused by integral saturation during large-angle steering, thus enhancing the trolley's following control effect.

[0034] In one possible embodiment, when determining the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, this application may proceed as follows: determining the wheel steering angle based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity; and determining the speeds of the left and right drive wheels based on the wheel steering angle and the target command speed.

[0035] For example, the target steering angular velocity is input into the Ackerman steering model corresponding to the Ackerman steering mechanism. Based on the Ackerman steering control function corresponding to the Ackerman steering model, the target steering angular velocity is inversely solved to obtain the wheel steering angles of the left and right wheels. The speeds of the left and right drive wheels are determined based on the wheel steering angles and the target command speed. This allows the motor drive system to drive the transport trolley to rotate the left and right wheels of the front wheels using the wheel steering angles, and to drive the speeds of the left and right rear wheels according to their respective speeds. A multi-turn absolute encoder can collect the actual steering angle of the front wheels in real time. The motor drive system can substitute the target command speed and target steering angular velocity provided by the vehicle control system into the Ackerman steering control function to complete the inverse Ackerman steering calculation and automatically match the target steering angular velocity with the speeds of the left and right drive wheels. This application determines the wheel steering angle based on the Ackerman steering model and determines the speeds of the left and right drive wheels based on the wheel steering angles and the target command speed. This, combined with the Ackerman steering mechanism of the transport trolley, achieves electronic differential steering, reducing wheel slippage during steering and improving steering smoothness and terrain adaptability.

[0036] In one embodiment, the present application determines the wheel steering angle based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. This can be achieved by: determining a first ratio of the first product of the target steering angular velocity and the front and rear wheelbases to the target command speed, and determining the arctangent of the first ratio as the wheel steering angle.

[0037] The geometric model of the Ackermann steering mechanism provided in this application is as follows: Figure 4 As shown, using the turning angle of the trolley's center as an equivalent parameter, the following is derived based on the principle of triangle similarity and the formula for the radius of rotation: , Further results were obtained: , According to the formula for the radius of rotation, we know that... Substituting R into the above formula yields the wheel steering angle: Where L is the front and rear wheelbase, W is the left and right wheel track width, and θ is the wheel steering angle. For the target command speed, The target steering angular velocity is shown in the diagram, where O represents the center of the vehicle's turn. Ackerman steering ensures that the perpendicular lines of all the vehicle's wheels point towards the center when the vehicle is turning. This application determines the wheel steering angle by dividing the target steering angular velocity by a first product of the front and rear wheelbases by a first ratio to the target command speed. The arctangent of this first ratio is then used to accurately calculate the wheel steering angle. This, combined with the Ackerman steering mechanism of the transport trolley, enables electronic differential steering, reducing wheel slippage during turning and improving steering smoothness and terrain adaptability.

[0038] In one embodiment, when determining the speeds of the left and right drive wheels based on the wheel steering angle and the target command speed, this application may involve: calculating a second product of a first multiple, the front and rear wheelbases, the left and right wheelbases, and the tangent of the wheel steering angle; multiplying the target command speed and the difference between the preset base and the second product to obtain the speed of the left drive wheel; and multiplying the target command speed and the preset base and the sum of the second product to obtain the speed of the right drive wheel. Here, the first multiple is 2, and the preset base is 1. Based on this, the speeds of the left and right drive wheels provided by this application can be determined by the following formula: V L =Vx×(1-2LW×tanθ) V R =Vx×(1+2LW×tanθ) Among them, V L V is the speed of the left drive wheel. RLet L be the speed of the right drive wheel, W be the wheelbase between the front and rear wheels, and θ be the wheel steering angle. Optionally, after determining the speeds of the left and right drive wheels, their speeds can be limited within a preset speed range, thus limiting their amplitude. After determining the speeds of the left and right drive wheels, the drive motors corresponding to the left and right rear wheels of the transport trolley can be controlled according to their respective speeds to control the rotation of the left and right rear wheels. This application calculates the second product of a first multiple, the wheelbase between the front and rear wheels, the wheel track between the left and right wheels, and the tangent of the wheel steering angle. The left drive wheel speed is obtained by multiplying the target command speed by the difference between the target command speed and the preset base and the second product. The right drive wheel speed is obtained by multiplying the sum of the target command speed and the preset base and the second product. This achieves differential speed control of the transport trolley, reducing wheel slippage during steering and improving steering smoothness and terrain adaptability.

[0039] In one embodiment, this application determines the first positioning data of the first wireless tag based on the phase difference of arrival algorithm. The wireless base station is equipped with two or more receiving antennas. By calculating the phase difference between the UWB signal arriving at different receiving antennas in the wireless base station, the signal propagation time difference can be calculated, and then the relative distance between the tag and the base station can be calculated. Combined with the real-time speed of the master and slave vehicles, an adaptive following strategy is formulated to ensure a safe platooning distance. Alternatively, a multi-antenna array on the wireless base station can be used to detect the azimuth angle of the wireless tag relative to the wireless base station, outputting the raw relative coordinate data (first positioning data), providing a positioning basis for UWB following control. The phase difference of arrival algorithm only requires two receiving antennas in the wireless base station to complete the wireless tag positioning. With a close distance between the master and slave vehicles, positioning errors are significantly reduced, making it suitable for close-range platooning operations in farmland.

[0040] Optionally, such as Figure 5 A schematic diagram illustrating the positional relationship between a wireless base station and a wireless tag is provided, where A and B are two antennas on the wireless base station, and the tag is the wireless tag itself. The formula for calculating the first positioning data can be derived by combining the principles of signal time-of-flight and phase difference. For example, the relationship between phase difference and distance difference is: in, λ The carrier wavelength of the wireless signal (UWB signal). γ This is the difference in path length between the two antennas on the wireless base station for the wireless signal to reach the wireless signal (the path length can be determined based on the time difference between the antenna transmitting and receiving the wireless signal). Φ This represents the phase difference between the signals received by the two antennas on the wireless base station. Using trigonometric relationships, the formula for calculating the two-dimensional coordinates (first positioning data) of the wireless tag can be obtained: Where (x, y) are the x and y coordinates corresponding to the first positioning data, d is the distance between the two antennas on the wireless base station, s is the first distance from the antenna farthest from the wireless tag (antenna A) on the wireless base station to the wireless tag, and r is the difference between the first distance and the second distance from the antenna closest to the wireless tag (antenna B) on the wireless base station to the wireless tag. This application uses a servo motor to provide power to the steering system, a multi-turn absolute encoder to accurately collect the steering angle, and an integral separation PID control algorithm to effectively solve the overshoot problem caused by integral saturation during large-angle steering. At the same time, it realizes static error compensation during driving, completely solving the problem of low speed control precision in most existing unmanned farm transport vehicles on the market. The vehicle's power control system uses a UWB positioning algorithm to obtain the target command speed and steering angle, controlling the steering servo and drive motors. By substituting the steering angle into the Ackermann steering mathematical model, it automatically calculates the differential speed between the inner and outer wheels of the left and right drive motors, achieving small-radius steering. This allows it to adapt to various agricultural scenarios, including typical hilly terrain in southern China, orchards, greenhouses, small fields, tall crops, and trellis planting, overcoming terrain limitations and solving the problem of large tractors and agricultural drones being unable to enter complex and confined environments or conduct inter-row operations. Through wireless communication between the UWB base station controller and the UWB tag, it accurately obtains the X-axis distance and Z-axis heading angle between the base station and the tag, ensuring the base station's heading angle always points towards the tag, achieving automatic angle and distance following. Compared to existing image recognition-based following solutions, this invention offers higher following accuracy and faster response speed, meeting the real-time requirements of farmland operations.

[0041] The above describes a system that acquires first positioning data of a first wireless tag within a preset detection angle through a wireless positioning control system. The vehicle control system determines the target command speed based on the first positioning data and a preset first PD control algorithm, and determines the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm. The motor drive system determines the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. It then controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels, enabling the transport trolley to automatically follow the first wireless tag in front. This achieves a stable following effect when multiple transport trolleys are linked in a chain. The integral separation PID control algorithm does not activate the integral term when the steering angle error exceeds a preset angle error threshold, effectively solving the overshoot problem caused by integral saturation when the transport trolley turns at large angles. This achieves static error compensation during driving and improves the accuracy of speed control.

[0042] Figure 6A flowchart of a wireless networking-based unmanned transport vehicle control method provided in this application embodiment is given. This wireless networking-based unmanned transport vehicle control method can be applied to the wireless networking-based unmanned transport vehicle for farmland provided in any of the above embodiments. The wireless networking-based unmanned transport vehicle control method provided in this application embodiment can be executed by a wireless networking-based unmanned transport vehicle control device. This wireless networking-based unmanned transport vehicle control device can be implemented by hardware and / or software and integrated into a wireless networking-based unmanned transport vehicle control equipment (e.g., a vehicle body control system).

[0043] The following description uses a wireless network-based unmanned transport vehicle (UGV) control device as an example to illustrate the control method for a wireless network-based UGV. (Reference) Figure 6 The control method for the unmanned transport vehicle based on wireless networking includes: S101: Obtain the first positioning data of the first wireless tag within the preset detection angle through the wireless positioning control system.

[0044] S102: Determine the target command speed based on the first positioning data and the preset first PD control algorithm, and determine the target steering angular velocity based on the first positioning data and the preset integral separation PID control algorithm, wherein the integral separation PID control algorithm does not activate the integral term when the steering angle error is greater than the preset angle error threshold.

[0045] S103: Determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, and control the movement of the transport trolley according to the target steering angular velocity and the speeds of the left and right drive wheels.

[0046] The specific control method of the unmanned farmland transport vehicle control method based on wireless networking provided in this application can refer to any of the above embodiments and can achieve the corresponding technical effects. This application will not elaborate further.

[0047] The above describes a system that acquires first positioning data of a first wireless tag within a preset detection angle through a wireless positioning control system. The vehicle control system determines the target command speed based on the first positioning data and a preset first PD control algorithm, and determines the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm. The motor drive system determines the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. It then controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels, enabling the transport trolley to automatically follow the first wireless tag in front. This achieves a stable following effect when multiple transport trolleys are linked in a chain. The integral separation PID control algorithm does not activate the integral term when the steering angle error exceeds a preset angle error threshold, effectively solving the overshoot problem caused by integral saturation when the transport trolley turns at large angles. This achieves static error compensation during driving and improves the accuracy of speed control.

[0048] Figure 7 A schematic diagram of a wireless network-based unmanned transport vehicle control device according to an embodiment of this application is provided. This wireless network-based unmanned transport vehicle control device can be applied to the wireless network-based unmanned farmland transport vehicle provided in any of the above embodiments. (Reference) Figure 7 The wireless networking-based unmanned transport vehicle control device includes a positioning acquisition module 701, an analysis and processing module 702, and a control execution module 703.

[0049] The system includes a positioning acquisition module 701, which acquires first positioning data of a first wireless tag within a preset detection angle through a wireless positioning control system; an analysis and processing module 702, which determines the target command speed based on the first positioning data and a preset first PD control algorithm, and determines the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm, wherein the integral separation PID control algorithm does not activate the integral term when the steering angle error is greater than a preset angle error threshold; and a control execution module 703, which determines the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, and controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels.

[0050] The above describes a system that acquires first positioning data of a first wireless tag within a preset detection angle through a wireless positioning control system. The vehicle control system determines the target command speed based on the first positioning data and a preset first PD control algorithm, and determines the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm. The motor drive system determines the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. It then controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels, enabling the transport trolley to automatically follow the first wireless tag in front. This achieves a stable following effect when multiple transport trolleys are linked in a chain. The integral separation PID control algorithm does not activate the integral term when the steering angle error exceeds a preset angle error threshold, effectively solving the overshoot problem caused by integral saturation when the transport trolley turns at large angles. This achieves static error compensation during driving and improves the accuracy of speed control.

[0051] It is worth noting that in the embodiments of the above-described wireless networking-based unmanned transport vehicle control device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application. The wireless networking-based unmanned transport vehicle control device provided above can be used to execute the wireless networking-based unmanned transport vehicle control method provided in any of the above embodiments, and has the corresponding functions and beneficial effects. Technical details not described in detail in the above embodiments can be found in the wireless networking-based unmanned transport vehicle provided in any embodiment of this application.

[0052] Figure 8 This application provides a schematic diagram of a wireless networking-based unmanned farmland transport vehicle system, as illustrated in an embodiment of this application. (Refer to...) Figure 8 The wireless networking-based unmanned farmland transport vehicle system includes multiple wireless networking-based unmanned farmland transport vehicles as provided in any of the above embodiments, and the multiple wireless networking-based unmanned farmland transport vehicles are connected in a chain network through a wireless positioning control system.

[0053] The first wireless tag can be located on the moving target in front, for example, the first wireless tag is placed on the moving target at the front of multiple transport carts connected in a chain network. The moving target can be a worker or other mobile tool. At this time, the multiple transport carts connected in the chain network move with the worker or other mobile tool.

[0054] For example, assuming the wireless base station and second wireless tag on the transport cart are a UWB base station and a UWB tag, the UWB base station controller can be installed on the main vehicle, and the UWB wireless tags can be worn by the workers. After the equipment is turned on, the UWB base station and UWB tag form a network, and the UWB base station obtains the relative coordinates of the UWB tag in real time. A PI control algorithm is used to adjust the following distance and angle deviation, outputting the target driving speed and steering angle of the main vehicle. Optionally, the proportional coefficient of the integral-separated PID control algorithm is set to 0.6, the integral coefficient to 0.2, and the derivative coefficient to 0.1. The main vehicle sends its own coordinate data and motion parameters to the slave vehicle through the UWB base station, and the slave vehicle automatically follows the main vehicle through the PI control algorithm. Optionally, the following distance is set to 1.5m, and the upper limit of the following speed is set to 1.2m / s. The steering angle is collected by a multi-turn absolute encoder, and the integral-separated PID control algorithm outputs servo motor speed commands based on the deviation between the target steering angle and the actual angle, achieving precise control of the steering angle. The motor controller inputs the steering angle into the Ackermann steering mathematical model, automatically calculates the differential speed between the inner and outer wheels of the left and right drive motors, and controls the motor speed to achieve smooth steering and driving of the vehicle. In this embodiment, the unmanned agricultural transport vehicle can achieve autonomous platooning between master and slave vehicles. In orchard greenhouse scenarios, the vehicle can achieve a small radius turn of 0.5m, a following positioning accuracy of ±8cm, and a driving speed control error of ≤0.05m / s, effectively completing material transport operations between rows of greenhouse trellises.

[0055] In one embodiment, such as Figure 9 Another schematic diagram of a farmland unmanned transport vehicle system based on wireless networking is provided. The first wireless tag can also be located on other transport vehicles in front. For example, when multiple transport vehicles are connected in a chain network, the first wireless tag can be placed on the transport vehicle at the front of the line. The transport vehicle at the front of the line is the master vehicle, and the other transport vehicles are slave vehicles. At this time, the multiple transport vehicles connected in the chain network move with the master vehicle.

[0056] For example, UWB wireless tags are installed on the front of the main vehicle, and UWB base station controllers are installed on multiple slave vehicles, constructing a chain formation mode of 1 master and multiple slaves. Optionally, the following distance is set to 2.0m, the upper limit of the following speed is set to 1.0m / s, the proportional coefficient of the integral separation PID control algorithm is set to 0.8, the integral coefficient is set to 0.15, and the derivative coefficient is set to 0.12. In one embodiment, the following distance can also be set to 1.0m, the upper limit of the following speed is set to 0.8m / s, and the proportional coefficient of the integral separation PID control algorithm is set to 0.5, the integral coefficient is set to 0.25, and the derivative coefficient is set to 0.08. The transport vehicle of this embodiment can achieve automatic formation driving in a tall cornfield scenario, with a following positioning accuracy of ±9cm. The vehicle body can flexibly turn between corn rows with a width of 1.0m, effectively solving the problem that large equipment cannot enter tall crop fields for operation.

[0057] The above describes a system that acquires first positioning data of a first wireless tag within a preset detection angle through a wireless positioning control system. The vehicle control system determines the target command speed based on the first positioning data and a preset first PD control algorithm, and determines the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm. The motor drive system determines the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. It then controls the movement of the transport trolley based on the target steering angular velocity and the speeds of the left and right drive wheels, enabling the transport trolley to automatically follow the first wireless tag in front. This achieves a stable following effect when multiple transport trolleys are linked in a chain. The integral separation PID control algorithm does not activate the integral term when the steering angle error exceeds a preset angle error threshold, effectively solving the overshoot problem caused by integral saturation when the transport trolley turns at large angles. This achieves static error compensation during driving and improves the accuracy of speed control.

[0058] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments provided herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. An unmanned transport vehicle based on wireless networking, characterized in that, The system includes a vehicle body control system, a wireless positioning control system, and a motor drive system. The wireless positioning control system and the motor drive system are communicatively connected to the vehicle body control system. The transport trolley is steered using an Ackermann steering mechanism, and the motor drive system is connected to the Ackermann steering mechanism. Specifically: The wireless positioning control system is used to acquire first positioning data of a first wireless tag within a preset detection angle, and send the first positioning data to the vehicle control system. The vehicle control system is used to determine the target command speed based on the first positioning data and the preset first PD control algorithm, and to determine the target steering angle based on the first positioning data and the preset integral separation PID control algorithm, and to send the target command speed and the target steering angle to the motor drive system. The integral separation PID control algorithm does not enable the integral term when the steering angle error is greater than the preset angle error threshold. The motor drive system is used to determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, and to control the movement of the transport trolley according to the target steering angular velocity and the speeds of the left and right drive wheels.

2. The unmanned transport vehicle based on wireless networking according to claim 1, characterized in that, The step of determining the target steering angular velocity based on the first positioning data and a preset integral separation PID control algorithm includes: The current orientation angle and target orientation angle of the transport trolley are obtained, and the steering angle error is determined based on the current orientation angle and the target orientation angle. When the steering angle error is greater than a preset angle error threshold, the target steering angular velocity is determined based on the first positioning data and the preset second PD control algorithm; When the steering angle error is less than or equal to a preset angle error threshold, the target steering angular velocity is determined based on the first positioning data and a preset PID control algorithm.

3. The unmanned transport vehicle based on wireless networking according to claim 1, characterized in that, The determination of the left and right drive wheel speeds based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity includes: The wheel steering angle is determined based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity; The speeds of the left and right drive wheels are determined based on the wheel steering angle and the target command speed.

4. The unmanned transport vehicle based on wireless networking according to claim 3, characterized in that, The determination of the wheel steering angle based on the Ackermann steering model corresponding to the Ackermann steering mechanism, the target command speed, and the target steering angular velocity includes: A first ratio is determined between the first product of the target steering angular velocity and the front and rear wheelbases and the target command speed, and the arctangent of the first ratio is determined as the wheel steering angle.

5. The unmanned transport vehicle based on wireless networking according to claim 3, characterized in that, Determining the speeds of the left and right drive wheels based on the wheel steering angle and the target command speed includes: Calculate the second product of the first multiple, the front and rear wheelbases, the left and right wheelbases, and the tangent of the wheel steering angle; The left drive wheel speed is obtained by multiplying the difference between the target command speed and the preset base value by the second product, and the right drive wheel speed is obtained by multiplying the sum of the target command speed and the preset base value by the second product.

6. The unmanned transport vehicle based on wireless networking according to claim 1, characterized in that, The wireless positioning control system includes a wireless base station and a second wireless tag. The wireless base station is used to send a first ranging request to the first wireless tag within the preset detection angle to obtain the first positioning data. The second wireless tag is used to return tag coordinate data when it receives a second ranging request sent by another transport trolley, so that the other transport trolley can determine the second positioning data.

7. The unmanned transport vehicle based on wireless networking according to claim 1, characterized in that, The first wireless tag is located on another transport vehicle preceding it, or on a moving target preceding it.

8. A control method for an unmanned transport vehicle based on wireless networking, applied to the unmanned transport vehicle based on wireless networking as described in any one of claims 1-7, characterized in that, include: The wireless positioning control system acquires the first positioning data of the first wireless tag within a preset detection angle. The target command speed is determined based on the first positioning data and the preset first PD control algorithm, and the target steering angular velocity is determined based on the first positioning data and the preset integral separation PID control algorithm, wherein the integral separation PID control algorithm does not enable the integral term when the steering angle error is greater than the preset angle error threshold. The speeds of the left and right drive wheels are determined based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity. The movement of the transport trolley is controlled according to the target steering angular velocity and the speeds of the left and right drive wheels.

9. A control device for an unmanned transport vehicle based on wireless networking, applied to the unmanned transport vehicle based on wireless networking as described in any one of claims 1-7, characterized in that, It includes a location acquisition module, an analysis and processing module, and a control execution module, wherein: The positioning acquisition module is used to acquire the first positioning data of the first wireless tag within a preset detection angle through the wireless positioning control system. The analysis and processing module is used to determine the target command speed based on the first positioning data and the preset first PD control algorithm, and to determine the target steering angular velocity based on the first positioning data and the preset integral separation PID control algorithm, wherein the integral separation PID control algorithm does not enable the integral term when the steering angle error is greater than the preset angle error threshold. The control execution module is used to determine the speeds of the left and right drive wheels based on the Ackerman steering model corresponding to the Ackerman steering mechanism, the target command speed, and the target steering angular velocity, and to control the movement of the transport trolley according to the target steering angular velocity and the speeds of the left and right drive wheels.

10. A wireless networking-based unmanned transport vehicle system, characterized in that, It includes multiple unmanned transport vehicles based on wireless networking as described in any one of claims 1-7, wherein the multiple unmanned transport vehicles based on wireless networking are connected in a chain network through the wireless positioning control system.

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