Outdoor transport trolley based on solar energy and optimized backstepping control

Abstract

The invention relates to the technical field of outdoor unmanned logistics transportation equipment, in particular to an outdoor transportation trolley based on solar energy and optimized backstepping control, namely an outdoor transportation trolley adopting dual energy supply of'solar energy and kinetic energy recovery 'and'cascade-backstepping control and GPS / visual fusion positioning'. The energy supply module is provided with a solar panel, a lithium battery and a kinetic energy recovery unit. An STM32 chip is used for main control; the positioning and visual perception module comprises a GPS (Global Positioning System) and a Raspberry Pi OpenCV visual processing unit; the motion execution module is cooperatively controlled by a cascade-backstepping control method; obstacle avoidance depends on an ultrasonic sensor; the communication interaction comprises Bluetooth and a mobile phone App. According to the invention, the navigation capability is enhanced, and the carbon emission is reduced; compared with the prior art, the control precision is improved, the cascade-backstepping control algorithm is combined with GPS / visual fusion positioning, the positioning error of the open ground is reduced, and the error is still small within 30 seconds in a GPS refusal scene.

Description

Technical Field

[0001] This invention relates to the field of outdoor unmanned logistics transportation equipment technology, and in particular to an outdoor transportation vehicle based on solar energy and optimized backstepping control. Background Technology

[0002] Currently, last-mile delivery to campuses and communities mainly relies on manual handling, which is labor-intensive, inefficient, and has high carbon emissions. Existing outdoor transport vehicles generally have short ranges, relying on a single lithium battery for power. For example, pure lithium battery-powered vehicles such as Langdong Intelligent LD-AGV03 and Youwangte UWT-100 use lithium batteries as their sole power source, without auxiliary power supply or energy recovery design. Under full load, their range is ≤2.5 hours, and under empty load, their range is ≤3.5 hours. The short continuous outdoor operating time is difficult to meet daily transportation needs.

[0003] Existing outdoor transport vehicles suffer from low control precision. Traditional PID control algorithms are prone to large positioning errors and unstable path tracking in complex road conditions, especially in scenarios with weak GPS signals. For example, traditional PID-controlled outdoor transport vehicles, such as the Huace Navigation CT100 and Geek+ AGV-T20, rely on a single GPS positioning module and classic PID algorithms. Their positioning error is approximately 0.3m in open, unobstructed environments, but exceeds 0.8m in scenarios with weak GPS signals, such as under trees or near buildings. Therefore, this invention proposes an outdoor transport vehicle based on solar energy and optimized backstepping control. Summary of the Invention

[0004] To address the above issues, this invention proposes an outdoor transport vehicle based on solar energy and optimized backstepping control. By combining solar energy and kinetic energy recovery technology with optimized backstepping control algorithm, the vehicle's range and control accuracy are increased.

[0005] An outdoor transport vehicle based on solar energy and optimized backstepping control includes a vehicle body, an energy supply module, a main control module, a positioning and visual perception module, a motion execution module, an obstacle avoidance module, and a communication interaction module. The energy supply module, main control module, positioning and visual perception module, motion execution module, obstacle avoidance module, and communication interaction module are all located on the vehicle body.

[0006] The energy supply module includes a solar panel, a lithium battery, and a kinetic energy recovery unit. The solar panel is electrically connected to the lithium battery and is used to provide continuous power for the vehicle to operate on sunny days.

[0007] The main control module uses an STM32 chip to receive and process signals from various sensors and output control commands.

[0008] The positioning and visual perception module includes GPS and an OpenCV vision processing unit based on Raspberry Pi BC. The OpenCV vision processing unit runs on the Linux system of the Raspberry Pi and is used to process the images captured by the camera and calculate the lateral deviation of the vehicle, and transmit the lateral deviation to the main control module.

[0009] The motion execution module includes a brushless motor, a steering servo, a linear regulator, and a PID controller. The motion execution module achieves coordinated control of drive and steering through a cascaded-backstep motion control method, which includes the following steps:

[0010] (1) Construct a position outer loop, using the position deviation obtained by the fusion of GPS and vision as input, and output the desired wheel speed;

[0011] (2) Construct a speed loop, using the error between the desired wheel speed and the actual speed of the motor as input, and output the desired current;

[0012] (3) Construct a current inner loop, take the error between the desired current and the actual current of the motor as input, and output the PWM duty cycle to drive the brushless motor;

[0013] (4) Embed the backstepping control law in the outer loop of the position, construct the virtual control quantity in two steps, and ensure the global asymptotic stability of the position error through the Lyapunov function;

[0014] (5) Set up a visual feedforward channel to convert the lateral deviation calculated by the OpenCV visual processing unit into a speed correction amount, which is then superimposed on the desired wheel speed so that the vehicle's internal positioning error remains very small in the GPS denial scenario.

[0015] The obstacle avoidance module includes an ultrasonic sensor for detecting obstacles and controlling the braking distance of the vehicle, and can select either a stop-and-wait mode or an obstacle avoidance mode.

[0016] The communication interaction module includes a Bluetooth unit and a mobile app developed based on MIT App Inventor;

[0017] Furthermore, the main frame of the vehicle is constructed by splicing aluminum profiles and 3D printed parts;

[0018] Furthermore, in the energy supply module, the kinetic energy recovery unit is connected to the brushless motor and the lithium battery, and is used to switch the brushless motor to power generation mode when the vehicle decelerates or goes downhill, converting kinetic energy into electrical energy and storing it in the lithium battery.

[0019] Furthermore, in the positioning and visual perception module, the GPS is encapsulated in a lightweight material shell and fixed to an unobstructed area on the top of the vehicle via a shock-absorbing bracket; the nine-axis accelerometer gyroscope, combined with a Kalman filter algorithm, eliminates zero-drift errors and has a built-in temperature compensation mechanism; the OpenCV vision processing unit performs the following steps:

[0020] (1) Control the frame rate of the camera to acquire road surface images;

[0021] (2) Call the threshold segmentation and Canny edge detection algorithm of the OpenCV library to extract path features from the acquired image. The path features include lane lines, boundary lines or preset marker lines.

[0022] (3) Using the extracted path feature midline as a reference, calculate the pixel offset between the current imaging center of the vehicle and the path feature midline;

[0023] (4) Based on the camera calibration parameters, the pixel offset is converted into the actual physical lateral deviation and transmitted to the STM32 main control module via serial port.

[0024] Furthermore, in the motion execution module, the PID controller works in conjunction with the steering servo and the brushless motor to achieve coordinated control of steering and drive, as well as differential compensation.

[0025] Furthermore, in the motion execution module, the Lyapunov function has the following specific form:

[0026] Let the position error of the trolley be ε1=χ-χ d χ represents the actual position. d The target position is; the velocity error is... α1 is the virtual control variable constructed in the first step, representing the desired velocity; then the Lyapunov function is designed in two steps in the backstep control:

[0027] Step 1: Construct V1 for position error ε1

[0028] Property: V1 is a positive definite function. V1 = 0 only when ε1 = 0, and V1 > 0 in other cases.

[0029] Derivative: Substitute virtual control quantity k1 is the control gain; therefore, we can obtain Guarantee ε1 convergence.

[0030] Step 2: Introduce velocity error ε2 to construct global V2

[0031] Property: V2 is still a positive definite function, and V2 = 0 when ε1 = 0 and ε2 = 0.

[0032] Derivative: Combining the actual control quantity α2 designed in the second step of the backstep control law, including ε1, ε2 and gain k2>0, it can be derived that: because Negative definite, only when ε1=ε2=0 Therefore, the system position error ε1 is globally asymptotically stable and eventually converges to zero.

[0033] Furthermore, the main control module is connected to the energy supply module, the positioning and vision perception module, the motion execution module, the obstacle avoidance module, and the communication module respectively via a serial port;

[0034] Furthermore, the mobile app is used to display the vehicle's battery level, coordinates, and speed information in real time.

[0035] Compared with the prior art, the significant advantages of this invention are:

[0036] (1) The ingenuity of this invention lies in the fact that it solves the two core pain points of range and control accuracy at the same time through the synergistic design of dual energy scheme and optimized backstep control algorithm, rather than a simple superposition of single technologies;

[0037] (2) The present invention enhances the endurance by using a dual energy scheme of solar energy and kinetic energy recovery, which enhances the endurance on sunny days and reduces carbon emissions;

[0038] (3) The control accuracy of the present invention is increased. By combining the cascaded-backstep control algorithm with GPS / visual fusion positioning, the positioning error in open areas is reduced, and the error is still small within 30 seconds in GPS rejection scenarios. Attached Figure Description

[0039] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0040] Figure 1 This is an overall framework diagram of the various modules of the present invention;

[0041] Figure 2 This is a flowchart of the cascaded-backstep motion control method of the present invention.

[0042] Figure label:

[0043] 1. Solar panel; 2. Brushless motor; 3. Lithium battery; 4. Positioning and visual perception module; 5. Obstacle avoidance module; 6. Motion execution module; 7. Main control module; 8. Communication interaction module. Detailed Implementation

[0044] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0045] An outdoor transport vehicle based on solar energy and optimized backstepping control includes a vehicle body, an energy supply module, a main control module 7, a positioning and visual perception module 4, a motion execution module 6, an obstacle avoidance module 5, and a communication interaction module 8. The energy supply module, main control module 7, positioning and visual perception module 4, motion execution module 6, obstacle avoidance module 5, and communication interaction module 6 are all located on the vehicle body.

[0046] Specifically, the vehicle body uses an aluminum profile frame as the core load-bearing structure, and is equipped with 3D printed brackets and shock-absorbing brackets to fix each module.

[0047] The energy supply module includes a solar panel 1, a lithium battery 3, and a kinetic energy recovery unit. The solar panel 1 is electrically connected to the lithium battery 3 and is used to provide continuous power for the vehicle to operate on sunny days.

[0048] Specifically, solar panel 1 uses a 20W monocrystalline silicon photovoltaic panel, fixed to the top of the vehicle body with an aluminum bracket. It directly powers low-voltage equipment such as the main control module 7 and the positioning and vision perception module 4, and also replenishes the lithium battery 3 through the charge / discharge management module, ensuring continuous operation of the vehicle for at least 4 hours under sunny conditions. The kinetic energy recovery unit consists of a rectifier and filter circuit and a voltage detection module, connected to the brushless motor 2. When the vehicle decelerates, goes downhill, or brakes, the brushless motor 2 switches to generator mode, converting the output AC power to 12V DC power through the rectifier and filter circuit. The voltage detection module monitors the output voltage in real time. When the voltage is ≥11.5V, the charge / discharge management module recharges the lithium battery 3. Approximately 5%-8% of energy can be recovered during a single downhill or rapid deceleration process, improving energy efficiency.

[0049] The main control module 7 uses an STM32 chip to receive and process signals from various sensors and output control commands.

[0050] Specifically, such as Figure 1 As shown, the main control module is connected to the power supply module, positioning and vision perception module 4, motion execution module 6, obstacle avoidance module 5, and communication interaction module 8 via serial ports.

[0051] The positioning and visual perception module 4 includes GPS and an OpenCV vision processing unit based on Raspberry Pi. The OpenCV vision processing unit runs on the Linux system of the Raspberry Pi and is used to process the images captured by the camera and calculate the lateral deviation of the vehicle, and transmit the lateral deviation to the main control module.

[0052] Specifically, the GPS is encapsulated in a lightweight shell and fixed to an unobstructed area on the top of the vehicle using a shock-absorbing bracket; a nine-axis accelerometer gyroscope, combined with a Kalman filter algorithm, eliminates zero-drift errors and has a built-in temperature compensation mechanism; the OpenCV vision processing unit performs the following steps:

[0053] (1) Control the frame rate of the camera to acquire road surface images;

[0054] (2) Call the threshold segmentation and Canny edge detection algorithm of the OpenCV library to extract path features from the acquired image. The path features include lane lines, boundary lines or preset marker lines.

[0055] (3) Using the extracted path feature midline as a reference, calculate the pixel offset between the current imaging center of the vehicle and the path feature midline;

[0056] (4) Based on the camera calibration parameters, the pixel offset is converted into the actual physical lateral deviation and transmitted to the STM32 main control module via serial port.

[0057] The motion execution module 6 includes a brushless motor 2, a steering servo, a linear regulator, and a PID controller. The motion execution module achieves coordinated control of drive and steering through a cascaded-backstep motion control method. Figure 2 As shown, the cascaded-backstep motion control method includes the following steps:

[0058] (1) Construct a position outer loop, using the position deviation obtained by the fusion of GPS and vision as input, and output the desired wheel speed;

[0059] (2) Construct a speed loop, using the error between the desired wheel speed and the actual speed of the motor as input, and output the desired current;

[0060] (3) Construct a current inner loop, take the error between the desired current and the actual current of the motor as input, and output the PWM duty cycle to drive the brushless motor;

[0061] (4) Embed the backstepping control law in the outer loop of the position, construct the virtual control quantity in two steps, and use the Lyapunov function to make the position tracking error asymptotically stable.

[0062] (5) Set up a visual feedforward channel to convert the lateral deviation calculated by the OpenCV visual processing unit into a speed correction amount, which is then superimposed on the desired wheel speed so that the vehicle's internal positioning error remains very small in the GPS denial scenario.

[0063] Specifically, the motion execution module uses a brushless motor 2 and a steering servo as its core, paired with an RT9080 linear regulator to achieve power supply isolation between high and low voltage power consumption devices, and integrates a cascaded-backstep motion control method with a PID controller. The specific form of the Lyapunov function is as follows:

[0064] Let the position error of the trolley be ε1=χ-χ d χ represents the actual position. d The target position is; the velocity error is... α1 is the virtual control variable constructed in the first step, representing the desired velocity; then the Lyapunov function is designed in two steps in the backstep control:

[0065] Step 1: Construct V1 for position error ε1

[0066] Property: V1 is a positive definite function. V1 = 0 only when ε1 = 0, and V1 > 0 in other cases.

[0067] Derivative: Substitute virtual control quantity k1 is the control gain; therefore, we can obtain Guarantee ε1 convergence.

[0068] Step 2: Introduce velocity error ε2 to construct global V2

[0069] Property: V2 is still a positive definite function, and V2 = 0 when ε1 = 0 and ε2 = 0.

[0070] Derivative: Combining the actual control quantity α2 designed in the second step of the backstep control law, including ε1, ε2 and gain k2>0, it can be derived that: because Negative definite, only when ε1=ε2=0 Therefore, the system position error ε1 is globally asymptotically stable and eventually converges to zero.

[0071] The obstacle avoidance module 5 includes an ultrasonic sensor for detecting obstacles and controlling the braking distance of the vehicle, and can select either a stop-and-wait mode or an obstacle avoidance mode.

[0072] Specifically, the sensor connects to the STM32 main control module via the GPIO interface and outputs obstacle distance data in real time with a period of 100-200ms. When the detected obstacle distance is ≤30cm, the module outputs a deceleration command; when the distance is ≤15cm, it outputs a braking signal. At the same time, it adaptively selects the mode according to the obstacle position: if it is a front obstacle, it triggers a stop waiting mode and pushes a reminder to the mobile app via Bluetooth; if it is a side obstacle, it starts the obstacle avoidance mode.

[0073] The communication interaction module 8 includes a Bluetooth unit and a mobile app developed based on MIT App Inventor. The mobile app is used to display the car's battery level, coordinates, and speed information in real time.

[0074] Compared with traditional outdoor transport lithium battery vehicles, this invention has a range that is more than 2 hours longer and a positioning error that is 60% lower than traditional outdoor transport vehicles controlled by PID.

[0075] The core of this invention is the deep synergy between "dual energy supply" and "optimized control algorithm". Within the dual energy system, solar power directly reduces the load on the lithium battery, and kinetic energy recovery recharges redundant electrical energy. Within the control algorithm system, GPS / visual fusion positioning fills signal blind spots, and cascaded-backstep control converts deviations into precise commands, significantly reducing positioning errors. The error is minimal within 30 seconds of GPS interruption. The two systems provide cross-dimensional support, with stable power supply ensuring algorithm accuracy and precise control reducing energy consumption, forming a closed loop and achieving breakthroughs in both range and accuracy.

[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0078] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An outdoor transport vehicle based on solar energy and optimized backstepping control, characterized in that, It includes a vehicle body, an energy supply module, a main control module, a positioning and visual perception module, a motion execution module, an obstacle avoidance module, and a communication interaction module. The energy supply module, the main control module, the positioning and visual perception module, the motion execution module, the obstacle avoidance module, and the communication interaction module are all located on the vehicle body. The energy supply module includes a solar panel, a lithium battery, and a kinetic energy recovery unit. The solar panel is electrically connected to the lithium battery and is used to provide continuous power for the vehicle to operate on sunny days. The main control module uses an STM32 chip to receive and process signals from various sensors and output control commands. The positioning and visual perception module includes GPS and an OpenCV vision processing unit based on Raspberry Pi. The OpenCV vision processing unit runs on the Linux system of the Raspberry Pi and is used to process the images captured by the camera and calculate the lateral deviation of the vehicle, and transmit the lateral deviation to the main control module. The motion execution module includes a brushless motor, a steering servo, a linear regulator, and a PID controller. The motion execution module achieves coordinated control of drive and steering through a cascaded-backstep motion control method, which includes the following steps: (1) Construct a position outer loop, using the position deviation obtained by the fusion of GPS and vision as input, and output the desired wheel speed; (2) Construct a speed loop, using the error between the desired wheel speed and the actual speed of the motor as input, and output the desired current; (3) Construct a current inner loop, take the error between the desired current and the actual current of the motor as input, and output the PWM duty cycle to drive the brushless motor; (4) Embed the backstepping control law in the outer loop of the position, construct the virtual control quantity in two steps, and use the Lyapunov function to make the position tracking error asymptotically stable. (5) Set up a visual feedforward channel to convert the lateral deviation calculated by the OpenCV visual processing unit into a speed correction amount, which is then superimposed on the desired wheel speed so that the vehicle's internal positioning error remains very small in the GPS denial scenario. The obstacle avoidance module includes an ultrasonic sensor for detecting obstacles and controlling the braking distance of the vehicle, and can select either a stop-and-wait mode or an obstacle avoidance mode. The communication interaction module includes a Bluetooth unit and a mobile app developed based on MIT App Inventor.

2. The transport trolley based on optimized backstepping control according to claim 1, characterized in that, The main frame of the vehicle is made of aluminum profiles and 3D printed parts.

3. A transport trolley based on optimized backstepping control according to claim 1, characterized in that, In the energy supply module, the kinetic energy recovery unit is connected to the brushless motor and the lithium battery, and is used to switch the brushless motor to power generation mode when the car decelerates or goes downhill, converting kinetic energy into electrical energy and storing it in the lithium battery.

4. A transport trolley based on optimized backstepping control according to claim 1, characterized in that, In the positioning and visual perception module, the GPS is encapsulated in a lightweight shell and fixed to an unobstructed area on the top of the vehicle by a shock-absorbing bracket; the nine-axis accelerometer gyroscope, combined with a Kalman filter algorithm, eliminates zero-drift errors and has a built-in temperature compensation mechanism; the OpenCV vision processing unit performs the following steps: (1) Control the frame rate of the camera to acquire road surface images; (2) Call the threshold segmentation and Canny edge detection algorithm of the OpenCV library to extract path features from the acquired image. The path features include lane lines, boundary lines or preset marker lines. (3) Using the extracted path feature midline as a reference, calculate the pixel offset between the current imaging center of the vehicle and the path feature midline; (4) Based on the camera calibration parameters, the pixel offset is converted into the actual physical lateral deviation and transmitted to the STM32 main control module via serial port.

5. A transport trolley based on optimized backstepping control according to claim 1, characterized in that, In the motion execution module, the PID controller works in conjunction with the steering servo and the brushless motor to achieve coordinated control of steering and drive, as well as differential compensation.

6. A transport trolley based on optimized backstepping control according to claim 1, characterized in that, In the motion execution module, the Lyapunov function has the following specific form: Let the position error of the trolley be ε1=χ-χ d χ represents the actual position. d The target position is; the velocity error is... α1 is the virtual control variable constructed in the first step, representing the desired velocity; then the Lyapunov function is designed in two steps in the backstep control: Step 1: Construct V1 for position error ε1 Property: V1 is a positive definite function. V1 = 0 only when ε1 = 0, and V1 > 0 in other cases. Derivative: Substitute virtual control quantity k1 is the control gain; therefore, we can obtain Guarantee ε1 convergence. Step 2: Introduce velocity error ε2 to construct global V2 Property: V2 is still a positive definite function, and V2 = 0 when ε1 = 0 and ε2 = 0. Derivative: Combining the actual control quantity α2 designed in the second step of the backstep control law, including ε1, ε2 and gain k2>0, it can be derived that: because Negative definite, only when ε1=ε2=0 Therefore, the system position error ε1 is globally asymptotically stable and eventually converges to zero.

7. A transport trolley based on optimized backstepping control according to claim 1, characterized in that, The main control module is connected to the energy supply module, the positioning and vision perception module, the motion execution module, the obstacle avoidance module, and the communication interaction module via serial ports, respectively.

8. A transport trolley based on optimized backstepping control according to claim 1, characterized in that, The mobile app is used to display the car's battery level, coordinates, and speed information in real time.

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