Intelligent logistics trolley adapting to environment, environment intelligent sensing system

Abstract

The utility model relates to intelligent logistics dolly of self -adaptation environment, environment intelligence sensing system and application belongs to artificial intelligence technical field, especially is logistics intelligent equipment technical field. Intelligent logistics dolly includes chassis, goods warehouse circulation mechanism, pitch driving mechanism, mechanical arm and detection frame, and environment intelligence sensing system is carried on intelligent logistics dolly, including the main control module of configuration in visual inspection device and the visual module, attitude sensor, quadrature encoder and ranging module of coupling main control module, through setting goods warehouse circulation mechanism and pitch driving mechanism on the chassis to cooperate with the mechanical arm to form the intelligent dolly that can automatically circulate and change each goods warehouse position and can automatically realize dump unloading, fills the low degree of automation of traditional freight logistics, the blank of artificial trouble.

Description

Technical Field

[0001] This utility model relates to an intelligent logistics vehicle that adapts to the environment and an intelligent environmental sensing system, belonging to the field of artificial intelligence technology, especially the field of intelligent logistics equipment technology. Background Technology

[0002] Currently, warehousing and inventory management in the traditional logistics industry still relies heavily on manual operations. This not only results in significant inefficiencies but also requires substantial investment in labor costs, leading to persistently high overall operating costs. Logistics systems built upon this traditional manual management and operational model have shown clear inadequacy in adapting to the challenges of surging business volumes and increased management complexity brought about by the rapid development of the logistics industry, making it difficult to meet the demands of efficient industry operations.

[0003] In the material handling process, traditional handling equipment also has significant limitations: its operation not only relies on manual operation throughout the entire process, but it can also only travel along pre-programmed fixed paths, lacking the ability to dynamically respond to changes in the environment. This greatly limits the application scope of traditional handling equipment in complex scenarios such as variable item placement and frequent personnel movement. It cannot autonomously avoid obstacles, nor can it accurately identify key information such as the position, shape, and size of objects, thus failing to achieve flexible and efficient handling operations.

[0004] From a practical application perspective, the shortcomings of traditional logistics models are particularly prominent in areas such as industrial production lines and warehousing logistics. On the one hand, the low efficiency of manual and traditional equipment handling directly affects the overall progress of production and logistics. On the other hand, manual operation is susceptible to fatigue and human error, which not only keeps labor costs high but also increases the error rate during handling and even increases the risk of safety accidents. At the same time, the fixed operating mode of traditional equipment also makes the production line severely lacking in flexibility and adaptability when facing changes in orders and process adjustments, making it difficult to quickly respond to fluctuations in market demand. Utility Model Content

[0005] To address the aforementioned problems, firstly, this utility model provides an environment-adaptive intelligent logistics vehicle, comprising: The chassis is connected to the wheels. The warehouse circulation mechanism includes a rotary seat fixed to the chassis and a closed frame connected to the rotary seat. Multiple warehouses are hinged on the closed frame. The rotary seat drives the closed frame to rotate to achieve the cyclical change of the position of different warehouses. A pitch drive mechanism, fixed to a rotary base, includes a drive assembly connected to each cargo compartment, the drive assembly being capable of driving the cargo compartment to rotate around the closed frame to adjust the pitch attitude of the cargo compartment. The robotic arm, fixed to the front of the chassis, is used to grab cards and put them into the warehouse; The detection frame, connected to the chassis, includes a vertical pole passing through the turntable and a horizontal bar connecting the top of the vertical pole and extending to the front side of the chassis, on which a visual inspection device is mounted.

[0006] Furthermore, the warehouse has a double-layered stepped structure, including a first warehouse and a second warehouse with a gradient difference. The first warehouse is connected to the upper side of the second warehouse, and the side of the first warehouse is provided with an ear seat that is hinged to the closed frame.

[0007] Furthermore, the pitch drive mechanism includes a pitch support fixed to the chassis, a pitch motor mounted on the pitch support, and a pitch driven wheel. The pitch motor is connected to the pitch drive wheel, and the pitch drive wheel is connected to the wire hole on the side of the first compartment via a wire through the pitch driven wheel. Driven by the pitch motor, the first compartment rotates around the closed frame in sequence through the pitch drive wheel and the pitch driven wheel to achieve pitch attitude adjustment.

[0008] Furthermore, the rotary seat includes an annular seat fixed above the chassis, and a rotary gear ring is fitted on the annular seat. The rotary motor is fixed to the chassis and its output end is connected to a rotary gear, which meshes with the rotary gear ring.

[0009] Furthermore, the annular seat is connected to a closed frame by multiple radial connecting rods. The closed frame is a polygonal frame structure with a cargo compartment installed on each side.

[0010] Furthermore, the robotic arm is mounted on an extension platform on the front side of the chassis, and the extension platform is also provided with two L-shaped support rods connecting the crossbar.

[0011] Furthermore, the crossbar is provided with claw-shaped branches, and each claw-shaped branch is provided with a visual inspection device.

[0012] Secondly, this utility model provides an environmental intelligent sensing system mounted on an intelligent logistics vehicle, comprising: The main control module configured in the vision inspection device; and the module coupled to the main control module. The vision module is capable of performing high-precision target recognition, scene feature extraction, and behavior analysis based on deep learning algorithms, providing intelligent decision support for the system. Attitude sensors are used for attitude data monitoring; Quadrature encoders are used to detect the rotation speed of the motor, and in conjunction with incremental PID algorithms, they enable closed-loop control of the motion. The ranging module is used to detect the distance from the ranging module to a fixed distance from the ground at the front of the vehicle, and to determine whether there is an obstacle in front by whether the distance exceeds a set threshold.

[0013] Furthermore, the main control module is an RT1064; the vision module is an OpenART vision module; the attitude sensor is an IMU963RA angle sensor; the quadrature encoder is a 1024-line quadrature encoder; and the ranging module is a TOF distance sensor.

[0014] The beneficial effects of this utility model are: This invention provides technical solutions from three aspects: an intelligent logistics vehicle capable of adapting to the environment, an intelligent environmental sensing system, and the application of the intelligent environmental sensing system. Firstly, by setting a cargo circulation mechanism and a pitch drive mechanism on the chassis to cooperate with the robotic arm, an intelligent vehicle is formed that can automatically cycle and change the position of each cargo compartment and automatically tilt and unload goods, filling the gap of low automation and cumbersome manual labor in traditional freight logistics. Secondly, a vision detection device is mounted on the intelligent vehicle through a detection frame. Through the combination of multiple modules such as a vision module, attitude sensor, and ranging module, an intelligent detection system is formed that can automatically identify and calculate information about the surrounding environment. This system can extract accurate coordinate information through various transformation and calculation methods and can execute feedback control, enabling the vehicle to adapt to the environment, improving the efficiency and accuracy of freight logistics. Furthermore, parameters such as tilting speed and angle can be controlled, allowing it to adapt to different types of materials. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall structure in one embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the structure of the chassis and cargo warehouse circulation mechanism in one embodiment of the present invention when they are not connected to the cargo warehouse.

[0017] Figure 3 This is a schematic diagram of the pitch drive mechanism in one embodiment of the present invention.

[0018] Figure 4 This is a top view of the overall structure in one embodiment of the present invention.

[0019] Figure 5 This is a schematic diagram of the structure of an intelligent environmental sensing system in one embodiment of the present invention.

[0020] Figure 6 A perspective diagram illustrating the principle of positioning for inverse perspective transformation (IPM).

[0021] Figure 7 This is a schematic diagram of the path planning algorithm.

[0022] In the diagram, 1. Chassis; 2. Warehouse circulation mechanism; 3. Pitch drive mechanism; 4. Robotic arm; 5. Detection frame; 6. Warehouse; 11. Traveling wheel; 12. Extension platform; 13. Support rod; 21. Closed frame; 22. Rotary gear; 23. Rotary gear ring; 24. Rotary motor; 25. Ring seat; 26. Connecting rod; 31. Pitch support; 32. Pitch motor; 33. Pitch drive wheel; 34. Pitch driven wheel; 61. First warehouse; 62. Second warehouse. Detailed Implementation

[0023] The technical solution of this utility model will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this utility model. Based on the embodiments of this utility model, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this utility model.

[0024] In the description of this utility model, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this utility model and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this utility model. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Specifically, the terms "first position" and "second position" refer to two different positions.

[0025] In the description of this utility model, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections or detachable connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this utility model based on the specific circumstances.

[0026] This utility model provides an intelligent logistics vehicle that adapts to the environment, including a chassis 1 and a cargo circulation mechanism 2 and a pitch drive mechanism 3 disposed on the chassis 1.

[0027] In some embodiments, the storage compartment 6 is a carrier for placing materials, currently taking card materials as an example. In an embodiment of the present invention, the storage compartment 6 is a double-layered stepped structure, including a first compartment 61 and a second compartment 62 with a gradient difference. The first compartment 61 is connected to the upper side of the second compartment 62. The side of the first compartment 61 is provided with an ear seat hinged to the closed frame 21. Both compartments can hold card materials, maximizing logistics efficiency.

[0028] In some embodiments, the chassis 1 adopts a double-layer rectangular chassis 1 structure connected with the walking wheels 11. In the example of Figure 2, the chassis 1 has a double-layer plate, with the detection frame 5 connected to the lower plate and the swivel base connected to the upper plate. The detection frame 5 can penetrate through the upper plate and the swivel base. This arrangement of the chassis 1 structure can effectively save space.

[0029] In the example shown in Figure 2, the warehouse circulation mechanism 2 includes a rotary seat fixed to the chassis 1 and a closed frame 21 connected to the rotary seat. Multiple warehouses 6 are hinged to the closed frame 21. The rotary seat drives the closed frame 21 to rotate, thereby changing the position of different warehouses 6. The rotary seat includes an annular seat 25 fixed to the upper plate of the chassis 1. A rotary gear ring 23 is fitted on the annular seat 25. A rotary motor 24 is fixed to the chassis 1 and its output end is connected to a rotary gear 22. The rotary gear 22 meshes with the rotary gear ring 23, so that the rotary motor 24 drives the rotary gear 22 to rotate the rotary gear ring 23 and the annular seat 25 together, thereby driving the closed frame 21 to rotate and adjust the position of different warehouses 6.

[0030] In some embodiments, the annular seat 25 is connected to a closed frame 21 via a plurality of radially arranged connecting rods 26. The closed frame 21 is a polygonal frame structure with a cargo compartment 6 installed on each side. In the examples of Figures 2 and 4, the closed frame 21 is a pentagonal structure, so its five sides are connected to five cargo compartments 6 respectively. The five cargo compartments 6 are centrally symmetrically distributed, and the position of one cargo compartment 6 can be adjusted every 72 degrees of rotation of the turntable.

[0031] In some embodiments, the closed frame 21 is a frame formed by five carbon rods connected by a three-way joint, which in addition to connecting two adjacent carbon rods, also connects to the connecting rod 26.

[0032] In some embodiments, the pitch drive mechanism 3 is fixed to the rotary base and includes a drive assembly connected to each cargo compartment 6. The drive assembly can drive the cargo compartment 6 to rotate around the closed frame 21 to adjust the pitch attitude of the cargo compartment 6. In the example of FIG3, the pitch drive mechanism 3 includes a pitch support 31 fixed to the chassis 1, a pitch motor 32 mounted on the pitch support 31, and a pitch driven wheel 34. The pitch motor 32 is connected to the pitch drive wheel 33. The pitch drive wheel 33 is connected to the wire hole on the side of the first cargo compartment 61 via the pitch driven wheel 34 through a wire. Driven by the pitch motor 32, the first cargo compartment 61 is rotated around the closed frame 21 in sequence through the pitch drive wheel 33 and the pitch driven wheel 34 to achieve pitch attitude adjustment.

[0033] In some embodiments, the pitch drive wheel 33 is connected to the wire hole on the side of the first compartment 61 via a high-strength PE wire through the pitch driven wheel 34, thereby improving durability and tensile strength.

[0034] When the storage bin 6 tilts downward under the drive of the pitch drive mechanism 3, the material is tipped over; when it tilts upward, the tipping ends. Since each storage bin 6 is individually connected to a pitch drive mechanism 3, the actions of the storage bin 6 in any direction can be controlled independently without being interfered with by the actions of other storage bins 6.

[0035] In some embodiments, the robotic arm 4 is fixed to the extension platform 12 on the front side of the chassis 1. The extension platform 12 is also provided with two L-shaped support rods 13 connected to the crossbar. The robotic arm 4 can grab cards and enter the cargo compartment 6 on the front side of the chassis 1, and then switch to other cargo compartments 6 through the rotary table to cooperate with the robotic arm 4 for feeding. The detection frame 5 is connected to the chassis 1. It includes a vertical rod that passes through the turntable and a horizontal bar that connects to the top of the vertical rod and extends to the front side of the chassis 1. The horizontal bar is provided with claw-shaped branches, and each claw-shaped branch is provided with a visual inspection device.

[0036] Example 1 like Figure 5 As shown, this embodiment provides an intelligent environmental perception system mounted on an intelligent logistics vehicle, including: a main control module configured in a vision inspection device; and a vision module, an attitude sensor, an orthogonal encoder, and a ranging module coupled to the main control module.

[0037] The NXP i.MX RT1064 chip, used as the main control module, possesses powerful real-time data processing and multi-task instruction scheduling capabilities, serving as the central control unit in the system architecture. The RT1064 establishes a full-duplex serial communication link with the OpenART vision module via a Universal Asynchronous Receiver / Transmitter (UART) interface, achieving stable and reliable data interaction. Based on the Arm® Cortex®-M7 core architecture, with a clock speed of up to 600MHz, the RT1064 boasts excellent real-time processing capabilities and highly integrated peripheral resources, making it particularly suitable for industrial automation and IoT applications. The RT1064 series provides rich hardware interface resources, including a 2D graphics acceleration engine, a camera interface, and various memory interfaces (such as SDRAM and NOR Flash). In terms of communication interfaces, the controller integrates a complete connectivity solution, including multiple UART, SPI, I²C bus interfaces, a full-speed USB OTG interface, dual 10 / 100M Ethernet MACs, and a 3-channel CAN bus controller. To meet real-time control requirements, the RT1064 is also equipped with high-speed GPIO, a CAN-FD interface, and a synchronous parallel memory controller. These features enable it to meet the real-time requirements of complex control systems.

[0038] In terms of communication interfaces, the STM32F4 only integrates basic UART and SPI interfaces, making it difficult to support the multi-protocol communication needs of the Industrial Internet of Things (IIoT). The RT1064, on the other hand, integrates dual 10 / 100M Ethernet MACs, a 3-channel CAN-FD bus controller, and a high-speed USB OTG interface, allowing simultaneous access to industrial fieldbuses and cloud networks for remote monitoring and real-time data interaction. For example, in smart factory production line monitoring, the RT1064 can collect data from multiple sensors in real time via the CAN-FD bus and upload the data to a cloud server via Ethernet. The STM32F4, due to its interface limitations, requires additional communication expansion modules to achieve similar functionality. Furthermore, the RT1064 is equipped with a 2D graphics acceleration engine, offering a significant advantage in Human-Machine Interface (HMI) development, enabling rapid rendering of complex graphical interfaces. The STM32F4, however, experiences lag when processing high-resolution graphics.

[0039] The vision module adopts the OpenART vision module, which can perform high-precision target recognition, scene feature extraction and behavior analysis based on deep learning algorithms, providing intelligent decision support for the system.

[0040] The attitude sensor uses the IMU963RA angle sensor, in which the IMU963RA inertial measurement unit is connected to the system to continuously monitor the overall vehicle's acceleration, angular velocity and other attitude data, providing important information for the system's attitude adjustment and balance control during dynamic operation.

[0041] The RT1064 connects to four quadrature encoders and their corresponding four motors via data cables. The quadrature encoders can accurately feed back information such as the speed and angle of the four motors, enabling the RT1064 to perform precise control of the motors and ensure the stability and accuracy of the system's motion.

[0042] The quadrature encoder employs a 1024-line quadrature encoder, which offers a significant advantage in accuracy compared to Hall effect sensors. Hall effect sensors output pulse signals by detecting changes in magnetic fields, but their resolution is typically below 200 lines and they are susceptible to electromagnetic interference, leading to pulse counting errors. In contrast, the 1024-line encoder, through a 4x frequency multiplication technique, can increase the resolution to 4096 count points / revolution. Combined with differential drive circuitry and digital filtering algorithms, it maintains stable signal output even in strong electromagnetic environments. In precision positioning scenarios, the position error of a Hall effect sensor can reach ±1°, while the repeatability of the 1024-line encoder can reach ±0.01°. The 1024-line quadrature encoder uses a quadrature encoding method with two-phase output signals, A and B, which are 90° out of phase. Mechanically, the encoder features a compact hollow shaft design with an inner diameter of 8mm, allowing direct mounting on the motor shaft and reducing transmission errors. The bearing system uses high-precision ball bearings with a rated speed of up to 10,000 rpm, radial runout of less than 5μm, and axial runout of less than 10μm, ensuring stable signal output even at high speeds. With an IP54 protection rating, it effectively resists dust and water droplet intrusion, making it suitable for harsh industrial environments.

[0043] The biggest advantage of the IMU963RA lies in its multi-sensor fusion. Single-axis or three-axis gyroscopes can only measure angular velocity and cannot provide acceleration and absolute orientation information. Over prolonged use, they are prone to integral drift, leading to a continuous accumulation of attitude calculation errors. The IMU963RA, however, uses built-in accelerometers and magnetometers, combined with a Kalman filter algorithm, to correct gyroscope drift errors in real time, achieving stable attitude measurement over extended periods. For example, in indoor positioning scenarios, a single-axis gyroscope might produce an angular deviation of more than 10° within minutes, while the IMU963RA, through multi-sensor data fusion, can control the error within ±0.5°.

[0044] The ranging module employs a Time-of-Flight (TOF) ranging module, which is also connected to the RT1064 via a data cable. Based on the time-of-flight principle, it measures distance and can effectively perceive information such as obstacles in the surrounding environment, assisting the system in achieving obstacle avoidance and navigation functions. These hardware components work together, complementing each other's strengths, to build a fully functional and responsive intelligent hardware system, widely applicable to robotics, intelligent mobile devices, and many other fields. While the TOF ranging module performs excellently in obstacle avoidance for intelligent vehicles, other ranging technologies such as ultrasonic and LiDAR have irreplaceable advantages in different scenarios. Compared to ultrasonic sensors, the TOF module represents a qualitative leap in accuracy and anti-interference capabilities. Ultrasonic sensors calculate distance by measuring the round-trip time of sound waves, and are significantly affected by ambient temperature and humidity, with a measurement error of approximately ±1cm at 25°C, and a noticeable blind zone (typically >10cm). The TOF module uses 940nm infrared light pulses, achieving a time resolution of 100ps, corresponding to a distance resolution of 1.5cm. Combined with a Kalman filter algorithm, the measurement error can be controlled within ±2mm. In close-range detection scenarios, ultrasonic sensors may produce erroneous readings due to the superposition of sound wave reflections, while the optical bandpass filter and digital phase-locked loop technology of the TOF module can effectively suppress multipath interference.

[0045] A TOF (Time of Flight) ranging module is a distance measurement device that works based on the Time of Flight principle. Its core is to emit light pulses and accurately calculate the time interval from the emission of the light pulse to its reception after being reflected by an object.

[0046] Example 2 This embodiment provides an application of intelligent environmental perception for a vehicle, employing an intelligent environmental perception system, including: Inverse Perspective Transform (IPM) positioning captures images of ground coordinate paper using a visual inspection device, projects these images onto a bird's-eye view using inverse perspective transformation, eliminates perspective distortion, and extracts precise global coordinate information. Combined with calibration parameters and a homography matrix, it achieves high-precision mapping from image pixel coordinates to the world coordinate system, with a positioning accuracy of ±2mm. Figure 6 As shown.

[0047] Transform coordinate relationships, including transforming the pixel coordinate system to the image coordinate system and the image coordinate system to the world coordinate system; Pixel coordinate system to image coordinate system: The pixel coordinate system uses the top-left corner of the image as its origin and discrete pixels as its unit of measurement. It is mainly used to describe the position of image data in computer storage and display. However, this coordinate system lacks physical scale information and cannot be directly applied to distance calculation and object localization in real space. To solve this problem, it is necessary to convert the pixel coordinate system into an image coordinate system with millimeters as its unit of measurement through the camera intrinsic parameter matrix K.

[0048]

[0049] The intrinsic parameter matrix K contains the core inherent parameters of the camera, specifically in the form of: in, ; Image coordinate system to world coordinate system: The image coordinate system describes the physical position of an object on the camera's imaging plane, but it cannot determine its absolute position in the real world. Therefore, it is necessary to convert the image coordinate system to the world coordinate system using the extrinsic parameter matrix [R|t]. This can be represented by Euler angles or quaternions; the translation vector t is a 3×1 vector, representing the position of the camera's optical center in the world coordinate system.

[0050]

[0051] The extrinsic parameter matrix consists of a rotation matrix R and a translation vector t, where the rotation matrix R is a 3×3 orthogonal matrix used to describe the rotation angle of the camera relative to the world coordinate system.

[0052] Image recognition and classification model: The lightweight MobileNetV2 neural network is used as the backbone feature extraction network, trained through transfer learning within the Tensorflow framework, to achieve real-time recognition and classification of different card categories. The model is deployed on an embedded OpenART vision module, achieving an inference speed of over 15 FPS and a recognition accuracy exceeding 98%. The training process of the deep learning image classification model based on the MobileNetV2 architecture is described below, for use in the cargo recognition system of an intelligent handling vehicle. This model can accurately identify different categories of cargo cards, providing core visual perception capabilities for the vehicle's automated sorting and handling functions.

[0053] The model employs depthwise separable convolutions and inverse residual structures, significantly reducing computation and parameter count while maintaining high accuracy, making it ideal for embedded device deployment. The input is a 128×128×3 RGB image with a width controlled by α=0.35, reducing the network channel count to 35% of the original, balancing accuracy and efficiency. Pre-trained weights leverage the feature extraction capabilities of the large-scale ImageNet dataset. Input pixel values ​​are normalized from [0, 255] to the [-1, 1] interval, meeting the input requirements of MobileNetV2 pre-trained models and enhancing convergence stability and generalization ability. Feature extraction is performed using 19 inverse residual blocks (bottleneck blocks) from MobileNetV2, employing linear bottleneck layers and shortcut connections to avoid non-linear activation functions destroying low-dimensional features. Depthwise separable convolutions significantly reduce parameter count and computational complexity. The output dimension is dynamically adjusted according to the task, using a softmax activation function to output a probability distribution, providing normalized class confidence for multi-class tasks. Finally, validation is performed using a real-world dataset to achieve optimal results.

[0054] Path planning algorithms: such as Figure 7 As shown, the Ant Colony Algorithm (ACO) is used for global path planning of multiple target points to solve for the optimal traversal sequence, significantly reducing the empty driving distance and operation time of the vehicle. The algorithm dynamically adapts to environmental changes and supports real-time obstacle avoidance and path replanning. Its core idea is to find the optimal path through swarm intelligence and positive feedback mechanisms. In the multi-target point global path planning of the intelligent transport vehicle, this algorithm simulates the behavior of ants releasing and sensing pheromones in the environment to dynamically construct the traversal sequence of target points, thereby significantly reducing the empty driving distance and operation time.

[0055] Example 3 The chassis 1 of this invention adopts a four-wheel independent drive structure, which supports three degrees of freedom motion in the plane (forward, lateral, and rotation) and achieves flexible and precise positioning.

[0056] An incremental PID controller is used to perform closed-loop speed control on the hub motor of each walking wheel, which improves the response speed and anti-interference ability and reduces the trajectory tracking error. Specifically, it includes sequentially executing speed calculation, speed coordinate system establishment and speed synthesis, position integral calculation and coordinate update, control command generation and motion synthesis.

[0057] (1) Speed ​​calculation: The traveling wheel 11 is a Mecanum wheel, typically consisting of a hub and multiple inclined rollers surrounding the hub, with the axes of these rollers at a 45° angle to the hub plane. When the hub rotates, the rollers generate a component force perpendicular to the direction of hub rotation, thus achieving omnidirectional movement. For a common four-wheel Mecanum wheel layout (with four wheels located at the four corners of a rectangle), its velocity decomposition follows the rules shown below.

[0058] Left front wheel (LF): velocity components are X-Y+Z Right front wheel (RF): velocity component is X+Y-2 Left rear wheel (LB): velocity components are X+Y+Z Right rear wheel (RB): velocity component is XY-2 Where X represents the robot's velocity component in the X-axis direction, Y represents the robot's velocity component in the Y-axis direction, and Z represents the robot's rotational velocity component. This velocity decomposition method allows the robot to independently control three degrees of freedom: translation in the X-axis direction, translation in the Y-axis direction, and rotation about the Z-axis.

[0059] (2) Coordinate system establishment and velocity synthesis: First, `actualspeed` is defined as the encoder read value, and `setspeed` is the desired speed. Then, a structure named `Motor` is defined to represent motor-related parameters and states. It includes the motor number `num`, the forward and reverse PWM control pins `pin_sct_forward` and `pin_sct_backward`, the encoder timer channel `channel_qtimer` and its related pin `pin_qtimer_LSB`; it also includes the pulse count `pulse`, the encoder return value `qtimer`, the set speed `setspeed`, the actual speed `actualspeed` and its historical value `actualspeed_last`, and the motor speed `motorspeed`; it also includes the error value `err` required for PID control and its historical values ​​`err_last` and `err_next`, as well as the PID coefficients `Kp`, `Ki`, and `Kd`, ultimately outputting the control signal through the PWM output.

[0060] YawAngle is a variable representing the yaw angle (rotation angle around the Z-axis) of a robot or vehicle. It is calculated by integrating the angular velocity and using a filtering algorithm, and the angle is constrained to within ±180° using modulo operations. In this paper, it will provide the steering angle requirements of the vehicle during movement.

[0061] The following describes the process of converting encoder data to global coordinates.

[0062] actualspeed_x = (motorLF. actualspeed + motorRF. actualspeed +motorLB. actualspeed + motorRB. actualspeed) / 4; actualspeed_y = (-motorLF. actualspeed + motorLB. actualspeed +motorRF. actualspeed - motorRB. actualspeed) / 4; The X and Y velocity components of the robot in the local coordinate system are calculated by weighted summation of the actual velocities of the four wheels. This calculation method is based on the inverse kinematics of the Mecanum wheel, converting the wheel velocities into the robot's body velocity. Since the robot may rotate around the Z-axis, it is necessary to transform the velocity components in the local coordinate system to the global coordinate system.

[0063] actualspeed_x = actualspeed_x fastcos (YawAngle) + actualspeed_y fastsin(YawAngle); actualspeed_y = actualspeed_y fastcos (YawAngle) - actualspeed_x fastsin (YawAngle); This achieves the transformation of the robot's velocity components in the global coordinate system, converting the robot's own coordinate system velocity to the global coordinate system through coordinate rotation. When the robot's yaw angle is yaw angle, coordinate rotation is needed to transform the velocity components in the robot's coordinate system to the global coordinate system. This is crucial in navigation, SLAM, and other scenarios because path planning is typically based on the global coordinate system.

[0064] (3) Location integration and coordinate update: After obtaining the velocity components in the global coordinate system, the robot's position is calculated through numerical integration.

[0065] x = actualspeed_x 0.008150f; y = actualspeed_y 0.006720f; coordinate_x += x; coordinate y += y; Here, 0.008150f and 0.006720f are scaling factors used to convert velocity into displacement increments. By continuously accumulating displacement increments, the robot achieves positioning in the global coordinate system.

[0066] (4) Synthesis of control commands and motion: The dir_control() function implements the mapping from global speed commands to the speeds of each wheel.

[0067] void dir_control (float X_speed, float Y_speed, float Z_speed) { motorLF.setspeed = X_speed - Y_speed + Z_speed; motorRF.setspeed = X_speed + Y_speed - Z_speed; motorLB.setspeed = X_speed + Y_speed + Z_speed; motorRB.setspeed = X_speed - Y_speed - Z_speed; } This mapping relationship is the correct solution to Mecanum wheel kinematics, which enables the robot to achieve the desired motion by properly allocating the speed of each wheel.

[0068] For incremental PID: In automatic control systems, the Proportional-Integral-Derivative (PID) control algorithm has become one of the most widely used control strategies due to its simple structure, strong robustness, and wide adaptability. Incremental PID, as an important variant of the PID algorithm, achieves dynamic adjustment of the system output by calculating the increment of the control variable, demonstrating unique advantages in many fields.

[0069] (1) Basic Principles Incremental PID algorithm, based on real-time monitoring of system error, linearly combines the three control components (proportional (P), integral (I), and derivative (D)) to calculate the increment of the control quantity, and then adjusts the output of the actuator. Its core idea is to dynamically adjust the control intensity according to the error change between the current moment and the previous moment, so that the system output quickly and stably approaches the target value.

[0070] In practical control systems, the system output is affected by various factors, such as load changes and interference signals, causing the output value to deviate from the target value. The incremental PID algorithm continuously detects the error and calculates the appropriate control increment based on the historical information of the error, thereby driving the actuator to correct the output deviation and achieving precise control of the system.

[0071] (2) Comparison with positional PID Compared to positional PID controllers, incremental PID controllers have a greater computational burden. Positional PID controllers require integration of the error, resulting in a higher computational load. Incremental PID controllers, on the other hand, only need to calculate the control increment based on the error at the current and previous two time points. This simplifies the calculation process and reduces the computational requirements of the controller, making them particularly suitable for resource-constrained embedded systems. Incremental PID controllers output only the increment of the control quantity each time. Even if the controller malfunctions, the impact is relatively small, unlike positional PID controllers which directly output large changes in the control quantity, thus avoiding severe system shocks and improving system stability and reliability. Positional PID controllers are prone to significant overshoot during system startup, shutdown, or large changes in setpoints; incremental PID controllers, however, can adjust the control increment promptly based on error trends, effectively suppressing overshoot and allowing the system to reach a stable state more quickly, resulting in better dynamic response performance.

[0072] (3) Analysis of advantages and disadvantages The advantages of incremental PID control algorithms lie in their simple calculation, low resource consumption, good dynamic performance, and strong anti-interference ability, making them particularly suitable for situations with frequent disturbances and changing system parameters. Furthermore, because their output is an increment of the control quantity, they have a small impact on the system and can effectively protect the actuators. However, this algorithm also has certain limitations. It relies on the control quantity from the previous moment; if there is an error in the control quantity from the previous moment, it will affect the current control. In addition, the presence of the integral term may cause a lag in the system response, requiring special handling in some systems with extremely high real-time requirements.

[0073] (4) Program Analysis Incremental PID algorithm is mainly used for vehicle motor speed control. Its core code is shown in the figure. It achieves precise speed control and direction adjustment by dynamically adjusting the motor PWM value. In the speed_Incream function, the pid_speed_realize function is the core of incremental PID control. It calculates the control increment based on the error between the current speed and the target speed, and then adjusts the motor PWM value accordingly.

[0074] void speed_Incream() { if (Contor_delay<100) { motorLF.pwm += pid_speed_realize (p_motorLF) / 2; motorRF.pwm += pid_speed_realize (p_motorRF) / 2; motorLB.pwm += pid_speed_realize (p_motorLB) / 2; motorRB.pwm += pid_speed_realize (p_motorRB) / 2; Contor_delay++; } Else { Contor_delay = 0; motorLF.pwm += pid_speed_realize (p_motorLF); motorRF.pwm += pid_speed_realize (p_motorRF); motorLB.pwm += pid_speed_realize (p_motorLB); motorRB.pwm += pid_speed_realize (p_motorRB); } motor_control(); } When `Contor_delay` is less than 100, the motor PWM is gradually adjusted in small increments to avoid sudden speed changes; when `Contor_delay` reaches 100, a larger adjustment is made, and `Contor_delay` is reset. This staged adjustment strategy helps achieve smooth and fast speed regulation. The `pid_speed_realize` function is speed control code implemented based on an incremental PID algorithm, as shown in the figure below. It achieves precise adjustment of the motor speed by calculating the increment of the control quantity. The function receives a pointer `x` to a `Motor` structure, where `x` represents the state information of the four wheels: `motorLF`, `motorRF`, `motorLB`, and `motorRB`. First, the current motor speed error is calculated ( x).err, i.e., target velocity ( x).setspeed and actual speed ( The difference between x)actualspeed provides the basic data for subsequent PID calculations.

[0075] void speed_Incream() { if (Contor_delay<100) { motorLF.pwm += pid_speed_realize (p_motorLF) / 2; motorRF.pwm += pid_speed_realize (p_motorRF) / 2; motorLB.pwm += pid_speed_realize (p_motorLB) / 2; motorRB.pwm += pid_speed_realize (p_motorRB) / 2; Contor_delay++; } Else { Contor_delay = 0; motorLF.pwm += pid_speed_realize (p_motorLF); motorRF.pwm += pid_speed_realize (p_motorRF); motorLB.pwm += pid_speed_realize (p_motorLB); motorRB.pwm += pid_speed_realize (p_motorRB); } motor_control(); } In the core calculation section, the system uses the incremental PID formula to linearly combine the proportional, integral, and derivative control components to achieve precise closed-loop control of the motor speed. Among these, ( x).Kp (( x).err - ( x).err_next) is the proportional term, which calculates the difference between the current error and the error at the previous moment, enabling it to quickly respond to error changes and adjust the control intensity in a timely manner; x).Ki ( x).err, as an integral term, effectively eliminates the steady-state error of the system by continuously accumulating the current error; x).Kd (( x).err - 2 ( x).err_next + ( x).err_last) is the differential term, which analyzes the trend of error changes to predict the dynamic behavior of the system, thereby suppressing overshoot. After the calculation is complete, the function passes ( x).err_last = ( x).err_next; and ( x).err_next = ( `x).err;` updates the error history to prepare for the next calculation. The final returned speed increment `incrementSpeed` will be directly used to adjust the motor's PWM value, ensuring that the motor's actual speed can quickly and stably track the target speed.

[0076] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. An intelligent logistics vehicle that adapts to its environment, characterized in that, include: The chassis is connected to the wheels. The warehouse circulation mechanism includes a rotary seat fixed to the chassis and a closed frame connected to the rotary seat. Multiple warehouses are hinged on the closed frame. The rotary seat drives the closed frame to rotate to achieve the cyclical change of the position of different warehouses. A pitch drive mechanism, fixed to a rotary base, includes a drive assembly connected to each cargo compartment, the drive assembly being capable of driving the cargo compartment to rotate around the closed frame to adjust the pitch attitude of the cargo compartment. The robotic arm, fixed to the front of the chassis, is used to grab cards and put them into the warehouse; The detection frame, connected to the chassis, includes a vertical rod passing through the swivel base and a horizontal bar connecting the top of the vertical rod and extending to the front side of the chassis, on which a visual inspection device is mounted.

2. The intelligent logistics vehicle according to claim 1, characterized in that, The warehouse has a double-layered stepped structure, including a first warehouse and a second warehouse with a gradient difference. The first warehouse is connected to the upper side of the second warehouse, and the side of the first warehouse is provided with an ear seat that is hinged to the closed frame.

3. The intelligent logistics vehicle according to claim 2, characterized in that, The pitch drive mechanism includes a pitch support fixed to the chassis, a pitch motor mounted on the pitch support, and a pitch driven wheel. The pitch motor is connected to the pitch drive wheel, and the pitch drive wheel is connected to the wire hole on the side of the first compartment via a wire through the pitch driven wheel. Driven by the pitch motor, the first compartment rotates around the closed frame in sequence through the pitch drive wheel and the pitch driven wheel to achieve pitch attitude adjustment.

4. The intelligent logistics vehicle according to claim 1, characterized in that, The rotary base includes an annular base fixed above the chassis, and a rotary gear ring is fitted on the annular base. The rotary motor is fixed to the chassis and its output end is connected to a rotary gear, which meshes with the rotary gear ring.

5. The intelligent logistics vehicle according to claim 4, characterized in that, The annular seat is connected to a closed frame by multiple radial connecting rods. The closed frame is a polygonal frame structure with a cargo compartment installed on each side.

6. The intelligent logistics vehicle according to claim 1, characterized in that, The robotic arm is mounted on an extension platform on the front side of the chassis, and the extension platform is also provided with two L-shaped support rods that connect to the crossbar.

7. The intelligent logistics vehicle according to claim 1, characterized in that, The crossbar is provided with claw-shaped branches, and each claw-shaped branch is provided with a visual inspection device.

8. An intelligent environmental sensing system, mounted on the intelligent logistics vehicle according to any one of claims 1-7, characterized in that, include: The main control module configured in the vision inspection device; and the main control module The vision module is capable of performing high-precision target recognition, scene feature extraction, and behavior analysis based on deep learning algorithms, providing intelligent decision support for the system. Attitude sensors are used for attitude data monitoring; Quadrature encoders are used to detect the rotation speed of the motor, and in conjunction with incremental PID algorithms, they enable closed-loop control of the motion. The ranging module is used to detect the distance from the ranging module to a fixed distance from the ground at the front of the vehicle, and to determine whether there is an obstacle in front by whether the distance exceeds a set threshold.

9. The environmental intelligent sensing system according to claim 8, characterized in that, The main control module is RT1064; the vision module is an OpenART vision module; the attitude sensor is an IMU963RA angle sensor; the quadrature encoder is a 1024-line quadrature encoder; and the ranging module is a TOF distance sensor.

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