A logistics robot

Abstract

The application provides a logistics robot, and relates to the technical field of robots.The logistics robot comprises a mobile chassis and a mechanical arm.The mechanical arm is rotatably connected to the mobile chassis through a first steering engine.The mechanical arm comprises a large arm, a small arm and a mechanical claw.The one end of the large arm is connected to the mobile chassis through a first gear set.The other end of the large arm is connected to the one end of the small arm through a second gear set.The other end of the small arm is connected to the mechanical claw.The linkage of the first steering engine, the first gear set and the second gear set not only enables the flexible operation of the mechanical arm in a multi-degree-of-freedom space, but also greatly improves the precision of grabbing.The large arm and the small arm are driven by gears, realizing efficient and accurate motion control, thereby meeting the high-precision requirements of the robot on the grabbing action.In addition, the high stability of the system enables the robot to run smoothly in a dynamic environment, reduces vibration and misoperation, improves work efficiency, and enables the robot to adapt to complex logistics application scenarios.

Description

Technical Field

[0001] This application relates to the field of robotics, and more specifically, to a logistics robot. Background Technology

[0002] Currently, global logistics robot technology is undergoing a rapid transformation from structured environments to unstructured, complex environments. The core drivers of this trend include the continued increase in demand for supply chain resilience, rising labor costs due to the gradual decline of the global demographic dividend, and the higher demands on logistics efficiency brought about by the explosive growth of e-commerce. To address these challenges, logistics systems urgently need to achieve upgrades that are more intelligent, autonomous, and adaptable.

[0003] However, existing logistics robot systems still have many limitations when facing complex scenarios. Traditional logistics vehicles and robots are mostly used in structured scenarios such as warehousing and sorting, and fixed-point transportation. Their operating environment is relatively limited, and their functions are also relatively simple. Most delivery vehicles only have the function of transporting goods and cannot realize the closed-loop operation of the entire process from picking up items to final delivery. They still need to rely on manual loading and unloading of items or the use of pre-positioned containers for transfer, which greatly limits the automation level of the system and the flexibility of the application scenarios.

[0004] To address the aforementioned issues, the technical solution of patent number CN218806229U proposes an AGV logistics vehicle with an integrated robotic arm, capable of gripping and transporting items placed at any location within a spatial range, thereby achieving a higher degree of automation and reducing human intervention. However, this solution still has significant technical shortcomings. Its robotic arm mainly relies on traditional servo motors for drive, resulting in low transmission accuracy, which makes it difficult to meet the high precision and stability requirements of gripping actions in complex environments. There is an urgent need for an intelligent logistics and distribution system with higher gripping accuracy and better stability to adapt to the complex and ever-changing logistics application scenarios of today and the future. Utility Model Content

[0005] The purpose of this application is to provide a logistics robot that addresses the shortcomings of the aforementioned technologies.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows: This application provides a logistics robot, including a mobile chassis and a robotic arm. The robotic arm is rotatably connected to the mobile chassis via a first servo motor, which is used to drive the robotic arm to rotate relative to the mobile chassis in a horizontal plane. The robotic arm includes a large arm, a small arm, and a robotic gripper. One end of the large arm is connected to a mobile chassis via a first gear set, which drives the large arm to rotate relative to the mobile chassis in a vertical plane. The other end of the large arm is connected to one end of the small arm via a second gear set, which drives the small arm to rotate relative to the large arm in a vertical plane. The other end of the small arm is connected to the robotic gripper.

[0007] Furthermore, the first gear set includes a first motor and a first pinion and a first large gear connected by a first belt drive. The first motor is mounted on the top of the mobile chassis. The output shaft of the first motor is connected to the first pinion. The first large gear is connected to the end of the upper arm away from the forearm. The first motor drives the first pinion to rotate, and the first pinion drives the upper arm to rotate via the first large gear.

[0008] Furthermore, a smooth tensioning wheel is provided between the first pinion and the first gear, and the smooth tensioning wheel abuts against the back of the first belt to adjust the tension of the first belt.

[0009] Furthermore, the second gear set includes a second motor and a second pinion and a second large gear connected by a second belt drive. The second motor is mounted on the boom, and the output shaft of the second motor is connected to the second pinion. The second large gear is connected to the end of the forearm near the boom. The second motor drives the second pinion to rotate, and the second pinion drives the forearm to rotate via the second large gear.

[0010] Furthermore, the mechanical gripper includes a palm and several gripping parts. One end of each gripping part is connected to the periphery of the palm, and the other end of each gripping part is connected to a different ball joint. A third motor is installed in the palm, and a flange is connected to the output end of the third motor. The ends of the ball joints away from the corresponding gripping parts are spaced apart and connected to the periphery of the flange. The third motor drives the flange to rotate so that the gripping parts open and close synchronously via the ball joints.

[0011] Furthermore, the mechanical gripper is movably connected to the forearm via multiple parallel second servo motors, each of which drives the mechanical gripper to rotate around a different axis of the forearm.

[0012] Furthermore, the mobile chassis includes a support platform for supporting the robotic arm and rollers respectively mounted around the bottom of the support platform, with each roller connected to the support platform via a shock absorber.

[0013] Furthermore, the logistics robot also includes a support frame mounted on top of the mobile chassis, which supports the end of the robotic arm away from the first servo motor when the robotic arm is rotated to its lowest position relative to the mobile chassis.

[0014] Furthermore, the logistics robot also includes a camera and an electronic control unit mounted on the robotic arm. The electronic control unit is electrically connected to the camera, the first servo motor, the first gear set, and the second gear set, respectively. The camera is used to identify image information of the items, and the electronic control unit is used to control the movement state of the robotic arm through the first gear set and the second gear set based on the image information identified by the camera.

[0015] Furthermore, the logistics robot also includes a distance sensor mounted on the mobile chassis. The electronic control unit is electrically connected to the mobile chassis and the distance sensor respectively. The distance sensor is used to measure the distance between the logistics robot and surrounding obstacles. The electronic control unit is also used to control the movement state of the mobile chassis based on the distance information measured by the distance sensor.

[0016] The beneficial effects of this application include: This application provides a logistics robot, including a mobile chassis and a robotic arm. The robotic arm is rotatably connected to the mobile chassis via a first servo motor, which drives the robotic arm to rotate relative to the mobile chassis in a horizontal plane. The robotic arm includes a large arm, a small arm, and a robotic gripper. One end of the large arm is connected to the mobile chassis via a first gear set, which drives the large arm to rotate relative to the mobile chassis in a vertical plane. The other end of the large arm is connected to one end of the small arm via a second gear set, which drives the small arm to rotate relative to the large arm in a vertical plane. The other end of the small arm is connected to the robotic gripper. By employing the linkage of the first servo motor, the first gear set, and the second gear set, the robotic arm can not only operate flexibly in a multi-degree-of-freedom space but also significantly improve the grasping accuracy. The large arm and small arm are driven by high-precision gears, achieving efficient and precise motion control, thereby meeting the high-precision requirements for grasping actions in complex environments. Furthermore, the high stability of this system enables the logistics robot to operate smoothly in dynamic environments, reducing vibration and misoperation, improving work efficiency, and allowing the robot to adapt to current and future complex and ever-changing logistics application scenarios. Attached Figure Description

[0017] Figure 1 This application provides a structural schematic diagram of a logistics robot. Figure 2 A schematic diagram of the structure of a robotic arm for a logistics robot provided in this application; Figure 3 This application provides an overall electrical control block diagram of a logistics robot; Figure 4 This is a diagram illustrating the actual operation of SLAM for mapping and navigation. Figure 5 Create maps and path display diagrams for the actual environment; Figure 6 A speed comparison chart of YOLOv3 with other models; Figure 7 Maps created using SLAM and deep learning.

[0018] Icons: 1-Mobile chassis; 11-Support platform; 12-Roller; 2-Robotic arm; 21-Large arm; 22-Forearm; 23-Robotic claw; 231-Palm; 232-Grasping part; 233-Ball joint link; 234-Third motor; 235-Flange; 31-First servo motor; 32-Second servo motor; 41-First motor; 42-First belt; 43-First pinion; 44-First gear; 45-Smooth tension wheel; 51-Second motor; 52-Second belt; 53-Second pinion; 54-Second gear; 6-Support frame; 7-Camera. Detailed Implementation

[0019] This application provides a logistics robot that integrates a mobile chassis 1 and a robotic arm 2, enabling efficient and precise object grasping and handling. The robotic arm 2 is connected to the mobile chassis 1 using a servo drive system. A first servo motor 31 connects the robotic arm 2 to the mobile chassis 1, allowing the robotic arm 2 to rotate relative to the mobile chassis 1 in a horizontal plane. The first servo motor 31 acts as the drive mechanism, ensuring the robotic arm 2 can rotate flexibly in the horizontal plane, thereby enabling it to grasp surrounding objects.

[0020] The robotic arm 2 comprises a large arm 21, a small arm 22, and a robotic gripper 23. One end of the large arm 21 is rotatably connected to the mobile chassis 1 via a first gear set, which drives the large arm 21 to rotate in the vertical plane. The range of motion of the large arm 21 covers the entire operating space of the logistics robot and is a key part of the robotic arm 2 in performing its tasks. The first gear set, through a precise transmission ratio, ensures the stable movement of the large arm 21 and can be appropriately adjusted according to the height and position of different items. Both the large arm 21 and the small arm 22 are constructed using carbon fiber tubing. Carbon fiber tubing possesses high strength and low weight, effectively reducing the overall weight of the robot while providing sufficient structural strength. Carbon fiber tubing clamps are used to secure the joints, preventing any loosening or displacement and ensuring the precision and stability of the robotic arm 2. To further enhance the torsional resistance and overall structural rigidity of the robotic arm 2, carbon fiber plates are used to reinforce the connection points, thereby improving the load-bearing capacity and reliability required by the robotic arm 2 during operation.

[0021] The other end of the upper arm 21 is rotatably connected to the lower arm 22 via a second gear set. This second gear set is designed to precisely control the movement of the lower arm 22 relative to the upper arm 21, allowing the lower arm 22 to rotate in a vertical plane. The lower arm 22 is connected to the mechanical gripper 23, which can perform precise gripping or releasing operations according to the different shapes of the objects and the grasping requirements. The second gear set, through a precise gear transmission system, ensures smooth and powerful movement of the lower arm 22, guaranteeing the safety and stability of the objects during grasping and placement.

[0022] By employing the linkage between the first servo motor 31 and the gear set, the robotic arm 2 not only operates flexibly in three degrees of freedom, but also significantly improves the precision of grasping. The large arm 21 and the forearm 22 are driven by a precision gear set, achieving efficient and accurate motion control, thus meeting the high-precision requirements for grasping actions in complex environments. Furthermore, the system's high stability enables the logistics robot to operate smoothly in dynamic environments, reducing vibration and misoperation, and improving work efficiency.

[0023] Through its precise design, the robotic arm 2 is able to adapt to the complex and ever-changing logistics application scenarios of today and the future. Whether it's maneuvering flexibly in confined spaces or moving stably across various complex terrains, the robotic arm 2 can achieve efficient and precise grasping functions. Furthermore, the combination of a sophisticated gear transmission system and a servo control system ensures that the robotic arm 2 maintains excellent performance under high-load, high-precision tasks. This design meets the stringent requirements of modern logistics systems for automation, precision, and efficiency, enabling logistics robots to better cope with increasingly complex and changing working environments.

[0024] Furthermore, the first gear set includes a first motor 41, a first pinion 43, and a first gear 44. The first motor 41 is mounted on top of the mobile chassis 1 of the logistics robot. The function of the first motor 41 is to provide driving force to propel the movement of the robotic arm 2. The output shaft of the first motor 41 is connected to the first pinion 43. When the first motor 41 starts, it drives the first pinion 43 to rotate. The first pinion 43 and the first gear 44 are connected by a first belt 42, and the rotation of the first pinion 43 transmits power to the first gear 44 through the first belt 42.

[0025] The first large gear 44 is connected to the end of the upper arm 21 furthest from the lower arm 22. When the first large gear 44 rotates, it drives the upper arm 21 to rotate relative to the moving chassis 1 in a vertical plane. Through this transmission structure, the movement of the robotic arm 2 can be precisely controlled by the first motor 41, achieving high-precision actions and adjustments. During rotation, the upper arm 21 can not only adapt to objects of different heights but also ensure the smoothness and stability of its movements throughout the entire working process, thereby ensuring the smooth gripping and handling of objects.

[0026] To further ensure the stability of the transmission system, a smooth tensioning pulley 45 is provided between the first pinion 43 and the first gear 44, abutting against the back of the first belt 42. The function of the smooth tensioning pulley 45 is to adjust the tension of the first belt 42, ensuring that the first belt 42 does not become too loose or too tight during transmission, thus preventing uneven friction or tooth skipping, thereby improving the transmission accuracy and service life of the system. By adjusting the tension, the smooth tensioning pulley 45 can maintain the stable operation of the first belt 42, ensuring that the movement of the robotic arm 2 is not affected by external interference and always maintains a highly efficient and stable transmission state.

[0027] Through the close cooperation between the first motor 41 and the gear system, combined with the tension adjustment of the smooth tension wheel 45, the movement of the entire robotic arm 2 is ensured to be precise and efficient. The various transmission components in the system coordinate with each other, achieving precise rotation of the large arm 21 and providing stable power support for the operation of the robotic arm 2. Furthermore, the use of the smooth tension wheel 45 effectively prevents belt slippage and excessive wear, extending the robot's service life and reducing maintenance costs.

[0028] This design enables logistics robots to perform high-precision material handling tasks in complex and dynamic environments, especially in applications requiring a large range of motion and high-precision control, achieving precise operation. The stability and gripping accuracy of robotic arm 2 allow the robot to adapt to items of different shapes and weights, providing more efficient logistics and delivery services and significantly improving the efficiency and reliability of automated logistics systems.

[0029] Furthermore, the second gear set includes a second motor 51, a second pinion 53, and a second large gear 54. The core function of this system is to drive the forearm 22 to complete high-precision movements. The second motor 51 is mounted on the large arm 21, and the output shaft of the second motor 51 is connected to the second pinion 53. After the second motor 51 is started, it transmits power through the output shaft, driving the second pinion 53 to rotate.

[0030] The second pinion 53 is connected to the second large gear 54 via the second belt 52, ensuring smooth power transmission. The second large gear 54 is connected to the end of the forearm 22 near the upper arm 21. Through gear linkage, the rotation of the second pinion 53 drives the rotation of the second large gear 54, thereby causing the forearm 22 to rotate accordingly in the vertical plane. With this design, the forearm 22 can be precisely adjusted in the vertical plane to meet the needs of various operating tasks.

[0031] The design of the second gear set ensures precise and efficient control of the forearm 22 during task execution. The driving precision of the second motor 51, combined with the gear transmission, makes the forearm 22 more flexible in performing tasks such as grasping, handling, and placing, while also possessing high motion stability. It can not only flexibly handle items of various heights and positions but also maintain stable grasping force and height accuracy in dynamic environments. When performing large-angle rotations or requiring high-precision grasping, the second gear set provides sufficient support, ensuring the accuracy of the forearm 22 and greatly improving the robot's adaptability and execution efficiency in complex environments. This allows the robot to better meet the high-precision and high-efficiency demands of modern automated logistics environments.

[0032] It should be noted that the first gear set uses a 3M60 large gear 44 and a 3M20 small gear 43 with a transmission ratio of 1:3 to ensure the precise rotation of the boom 21. The second gear set uses a 3M40 large gear 54 and a 3M20 small gear 53 with a transmission ratio of 1:2 to precisely drive the movement of the forearm 22. The large gear 44 and the second large gear 54 are connected to the boom 21 via an XRU1008 slewing bearing, ensuring smooth and unobstructed rotation while enabling flexible movement of the robotic arm 2. Furthermore, the connections between components use 6-35 threaded bolts, with copper washers at the bolt joints to ensure excellent sealing and prevent loosening.

[0033] Furthermore, the mechanical gripper 23 is movably connected to the forearm 22 via multiple parallel second servo motors 32. Each second servo motor 32 independently drives the mechanical gripper 23 to rotate around different axes of the forearm 22, thereby enabling precise movements of the mechanical gripper 23 in multiple directions. Specifically, the second servo motors 32 enable the mechanical gripper 23 to rotate around the forearm 22 and to swing up, down, left, and right. This design gives the mechanical gripper 23 wrist-like flexibility during operation, allowing it to easily handle various complex grasping needs.

[0034] Combining the movements of the upper arm 21 and the forearm 22, as well as the overall flexibility of the robotic arm 2, the robotic arm 2 of the logistics robot can achieve six degrees of freedom of movement. This six-degree-of-freedom movement capability enables the robotic gripper 23 to fully cover the entire grasping range in three-dimensional space, whether it is grasping items in a confined space or transporting objects at different angles and heights, it can complete the task efficiently and stably.

[0035] Furthermore, the mechanical gripper 23 includes a palm 231 and several gripping parts 232. One end of each gripping part 232 is connected to the periphery of the palm 231, and the other end of each gripping part 232 is connected to a different ball joint 233. A third motor 234 is installed in the palm, and a flange 235 is connected to the output end of the third motor 234. The ends of the ball joints 233 away from their corresponding gripping parts 232 are spaced apart and connected to the periphery of the flange 235. The third motor 234 drives the flange 235 to rotate, which in turn drives the multiple gripping parts 232 to open and close synchronously through the multiple ball joints 233. Since the connection points between each ball joint 233 and the gripping part 232 and the flange 235 are universal joints, the gripping parts 232 can achieve natural and coordinated opening and closing movements, similar to the opening and closing of human fingers, thereby completing the task of gripping and releasing objects. For example, when the third motor 234 drives the flange 235 to rotate counterclockwise, the ball joint 233 will push the gripping part 232 towards the closing direction. At this time, the gripping part 232 will move towards the object to clamp it, completing the gripping action. When the third motor 234 drives the flange 235 to rotate clockwise, it will drive the ball joint 233 to separate the gripping part 232. At this time, the gripping part 232 will move away from the object, performing the object release operation. In this way, the robotic gripper 23 can precisely control the gripping and releasing of objects. Through the precise control of the ball joint 233, the synchronous movement of multiple gripping parts 232 is achieved, and the robotic gripper 23 can maintain efficient and precise operation under various object shapes and gripping angles. In addition, when gripping and releasing objects, the robotic gripper 23 can automatically adjust the force according to the shape and weight of the object to ensure safe gripping and stable handling of the object.

[0036] It should be noted that each second servo motor 32 is connected to the robotic gripper 23 and the forearm 22 via a multi-functional servo motor bracket, allowing the robotic gripper 23 to rotate flexibly and swing up, down, left, and right relative to the forearm 22. The multi-functional servo motor bracket, robotic gripper 23, and forearm 22 are connected by M3 copper posts, which not only improves the stability of the connection but also ensures good insulation performance. Furthermore, the robotic gripper 23 is a three-jaw gripper, and its opening and closing motion is transmitted through three M3RC ball joints 233, achieving multi-degree-of-freedom movement and ensuring the accuracy and efficiency of the grasping action.

[0037] The flexibility and efficiency of the entire robotic arm 2 system are perfectly demonstrated through its six degrees of freedom of motion. The coordinated operation of the upper arm 21, forearm 22, and robotic gripper 23 enables the robot to adapt to complex and ever-changing environments. Whether it's grasping delicate objects or moving heavy loads, robotic arm 2 can complete tasks smoothly and accurately. The robot not only improves work efficiency but also makes automated logistics operations more intelligent and efficient, enabling its widespread application in modern logistics systems and driving the rapid development of intelligent logistics.

[0038] Furthermore, the mobile chassis 1 includes a support platform 11 and rollers 12 mounted around the bottom of the support platform 11. Each roller 12 is connected to the support platform 11 via a negative pressure shock absorber. The negative pressure shock absorber not only reduces vibration but also provides elastic support, enabling the entire chassis to maintain stable operation in different working environments. This four-wheel drive system provides powerful power support, allowing the robot to move flexibly on various terrains and adapt to sudden changes in acceleration, ensuring the stability of the chassis and robotic arm 2 system.

[0039] The negative pressure shock absorber generates elastic force through the pressure of springs and hydraulic fluid to support the weight of the entire vehicle. Specifically, when the robot encounters uneven ground or experiences sudden large accelerations or rotations during operation, the negative pressure shock absorber effectively absorbs the impact force, reduces the propagation of vibrations, and prevents system instability or precision errors in the robotic arm 2 caused by vibrations. The elastic support of the negative pressure shock absorber not only buffers the impact of large accelerations but also ensures that the support platform 11 maintains stable movement under any operating conditions, thereby ensuring the normal operation of the robotic arm 2 and other electronic components within the robot.

[0040] Furthermore, the damping intensity of the negative pressure shock absorber is adjustable, with 16 adjustment levels. Users can flexibly adjust the damping intensity according to actual load requirements and working environment to adapt to different transportation and work tasks. For example, when the robot is carrying heavy objects, the damping intensity can be increased to ensure stronger support; while during light-load operation, a lower damping level can be adjusted to further optimize energy consumption and movement flexibility. This adjustable damping intensity provides the robot with extremely high adaptability and flexibility, enabling it to cope with various complex dynamic environments.

[0041] The combination of a four-wheel drive system and negative pressure shock absorbers ensures efficient robot movement in complex terrain, maintaining stable operation, especially on rough or uneven surfaces. Secondly, the adjustable nature of the shock absorbers allows the robot to adjust in real time according to different load conditions and environmental requirements, improving its adaptability and stability. Finally, this design greatly enhances the robot's adaptability in dynamic environments, maintaining precise operation of the robotic arm 2 and stable operation of the robot system whether driving at high speed, turning, or performing grasping and handling tasks, thus enabling the logistics robot to achieve higher reliability and efficiency in automated operations.

[0042] Furthermore, the logistics robot also includes a support frame 6 installed on top of the mobile chassis 1. The function of the support frame 6 is to ensure that when the robotic arm 2 is rotated to the lowest position, it can support the end of the robotic arm 2 away from the first servo motor 31, so that there are two connection positions between the robotic arm 2 and the mobile chassis 1, thereby ensuring the stable state of the robotic arm 2 when working at a low position or when it stops working, and improving the stability and safety of the overall system.

[0043] Furthermore, the logistics robot also includes a camera 7 and an electronic control unit (ECU) mounted on the robotic arm 2. The camera 7 is responsible for capturing and recognizing image information of objects in real time, and the ECU processes and controls the movements of the robotic arm 2 based on this image information. Through electrical connections with the first servo motor 31, the second servo motor 32, the first motor 41, the second motor 51, and the third motor 234, the ECU precisely controls the movement of each joint and component of the robotic arm 2, enabling the robot to perform high-precision grasping, handling, and placement operations.

[0044] To achieve a wider grasping range, the robot is equipped with two 720P driverless UVC cameras 7. The first camera 7 is mounted on the front palm 231 of the robotic gripper 23, used to directly identify and track the position and shape of objects, ensuring the accuracy of the gripper 23 when contacting objects. The second camera 7 is suspended at a higher position via a vertical rod and connected to the output shaft of the GS90 servo motor, allowing the camera 7 to rotate relative to the robotic arm 2, responsible for scanning a wider range of the environment, capturing information about objects at a distance, and improving the flexibility and accuracy of object recognition. The two cameras 7 work together to provide comprehensive visual data from object contact to environmental perception, enabling the robot to efficiently identify and process objects in complex environments. The electronic control unit (ECU) is not only responsible for receiving image data from the cameras 7, but also for adjusting the motion state of the robotic arm 2 based on the image information. The ECU can precisely control the movement of the robotic arm 2, ensuring that the upper arm 21, forearm 22, and robotic gripper 23 operate flexibly and stably during object grasping and handling. The precise coordination of these gear sets enables the robotic arm 2 to adjust from multiple angles according to the specific position, shape, and posture of the object, achieving an all-round, three-dimensional grasping capability.

[0045] It should be noted that both the first servo motor 31 and the second servo motor 32 are SUPER HV super servos, providing high torque and high-precision control to ensure the movement of the robotic arm 2. The first motor 41, the second motor 51, and the third motor 234 are all DJI 3508 DC brushless geared motors. These motors have powerful output and their speed and direction are precisely adjusted through a C620 brushless motor speed controller to ensure stable power output. The first servo motor 31 is connected to the rotating platform through an XRU2012 slewing bearing, driving the overall rotation of the robotic arm 2.

[0046] In addition, the logistics robot includes a distance sensor mounted on the mobile chassis 1 to measure the distance between the robot and surrounding obstacles. The electronic control unit (ECU) connects the distance sensor to the drive mechanism of the rollers 12 on the mobile chassis 1 via electrical connections, acquiring environmental information in real time and controlling the movement of the mobile chassis 1. Through this integrated control, the ECU can dynamically adjust the movement state of the robot's rollers 12 based on the real-time data measured by the distance sensor, thereby effectively avoiding obstacles and optimizing path planning in a dynamic environment, ensuring the robot possesses a high degree of autonomy and flexibility when performing tasks.

[0047] To ensure the robot's adaptability in dynamic environments, the distance sensor employs the HY-SRF05 ultrasonic ranging sensor, which, in conjunction with the GS90 servo motor and SR-602 infrared sensor, enables obstacle detection within a 120° range. When an object is detected at a distance of less than 30cm or infrared light is sensed, the system immediately issues a command, forcing the robot to stop moving forward, thereby avoiding collisions with obstacles and ensuring the robot's safety during operation.

[0048] By combining camera 7, distance sensors, and intelligent control with an electronic control unit, the logistics robot not only possesses strong material handling capabilities but also adapts to environmental and task requirements, maintaining efficient and stable operational performance. Whether performing precision operations in confined spaces or navigating and grasping in complex obstacle environments, this design enables the robot to autonomously complete tasks, improving work efficiency and reducing human intervention. This solution fully meets the requirements of modern automated logistics operations for high precision, high efficiency, and high stability, and has broad application prospects.

[0049] Furthermore, such as Figure 3 As shown, the electronic control unit includes a chassis drive control unit, a camera control unit, a robotic arm 2 control unit, a sensor control unit, and a host computer communication unit. The electronic control unit uses an STM32F405RGT6 microcontroller based on the Cortex-M4 core as the main control core. Each control unit is controlled by an independent STM32 microcontroller and uses a CAN bus for low-level communication. Each control unit is independent but can also transmit data to each other, which greatly improves the independence, portability, and maintainability of each module. At the same time, it communicates with the host computer through the host computer communication unit, together forming the low-level communication architecture, and together with the battery power source, forming the low-level power architecture.

[0050] Furthermore, in logistics and distribution, good battery life is essential for successful delivery. At the same time, since there may be no operator or the operator may be using remote control, it is difficult to perform real-time monitoring and maintenance of the robot. Therefore, to ensure that each control structure can operate relatively independently, and that a failure in one part does not affect the operation of the main body, a dual-power supply structure is adopted.

[0051] The robot's main power supply is divided into two parts: the host computer power supply unit and the underlying circuit power supply unit. Since each module in the host computer unit is a standardized industrial-grade product, it requires relatively low current and rarely experiences circuit failures such as overheating, short circuits, or open circuits. Therefore, a centralized power supply scheme is adopted (each module is directly powered by the power supply). Each unit is relatively independent and does not affect others, facilitating unified control and management of the power supply. However, the underlying circuit units require higher current. To reduce the power supply current while ensuring the normal operation of each module, the power supply unit adopts a distributed power supply (connected tier by tier, with lower tiers powered by higher tiers).

[0052] The chassis motor supports two selectable control methods: (1) 50-500HZ PWM (Pulse Width Modulation) control. (2) CAN bus command control (including position feedback). In order to monitor the motor output in real time and realize closed-loop control of the motor, the chassis motor adopts CAN bus command control.

[0053] The chassis motor control employs an incremental PID algorithm, which means the digital controller's output is only the increment of the control quantity. Incremental PID is used when the actuator requires an increment, rather than an absolute position value. By using "Serial Chart" software to observe the response curve and adjust the PID parameters, the motor's response speed is made relatively stable, accurate, and fast, achieving the best driving effect.

[0054] Furthermore, in the distributed control system of a robot, the choice of communication method is crucial. Communication between the host computer and the lower-level controllers must satisfy the requirements of simple hardware connection and convenient expansion, while also ensuring high reliability and real-time performance. The robot communication network adopts the CAN bus as the communication standard, employing a two-level distributed structure with host and lower-level computers. The host computer is responsible for overall system management, kinematic calculations, trajectory planning, etc., while the lower-level computers consist of multiple CPUs, each controlling one module. The CAN bus is a serial communication network that effectively supports distributed and real-time control. Compared with general communication networks, it has the advantages of high reliability, real-time performance, and flexibility, making it very suitable as a communication method in robot control systems.

[0055] The characteristics of CAN in robot communication include: low cost; extremely high bus utilization; long data transmission distance (up to 10 km); high data transmission rate (up to 1 Mbps); the ability to decide whether to receive or block a message based on its ID; reliable error handling and detection mechanisms; automatic retransmission after the transmitted information is corrupted; automatic disconnection of nodes from the bus in case of serious errors; and messages do not contain source or destination addresses, but only use identifiers to indicate functional and priority information.

[0056] In the robot communication system provided in this embodiment, the host computer connects to the CAN network via a self-designed USB-to-CAN unit to communicate with the underlying modules. The control strategy employs both host and slave computers: Host computer: Responsible for the overall system management, kinematic calculations, trajectory planning, etc.

[0057] USB to CAN module: Converts information sent from the industrial control computer's USB port into CAN messages and sends them to the CAN bus network.

[0058] Chassis control module: Receives motor speed information and feeds back motor detection information.

[0059] Global Positioning System (GPS): Provides location information to ensure the robot moves to the target location. GPS connects to the electronic control unit (ECU) to acquire the robot's position data in real time. Through autonomous navigation, GPS helps the robot plan paths in complex environments and corrects its position based on real-time data, ensuring the robot can accurately complete its predetermined tasks.

[0060] The STM32F405RGT6 chip integrates a CAN controller for the CAN interface circuit. To complete the transmission and reception of data frames, an external CAN bus transceiver chip is required. The CAN bus transceiver chip used is the NXP Semiconductors TJA1050 chip.

[0061] Sensor module: Monitors the robot's status in real time.

[0062] Furthermore, the sensor module includes a flame sensor and a smoke sensor electrically connected to the electronic control unit for detecting potential fires. Furthermore, the sensor module includes an ATK-MPU6050 accelerometer that is electrically connected to the electronic control unit, used to acquire and transmit the real-time attitude of the logistics robot.

[0063] Furthermore, the sensor module includes a raindrop sensor connected to the electronic control unit, which can monitor precipitation in real time during patrol and actively avoid rain, further improving the robot's adaptability in different environments.

[0064] Furthermore, the logistics robot is equipped with 110×80mm solar panels and TP4056 charging boards to ensure power supply and charging protection. The solar panels provide additional energy support for the robot, especially during long-term task execution, preventing performance degradation due to insufficient battery power. The number of solar panels and batteries can be expanded as needed to ensure normal robot operation, unaffected by the demands of motors or other high-power modules.

[0065] Furthermore, the electronic control unit (ECU) provides position commands to control robotic arm 2, requiring a precise position loop. Therefore, a three-loop control system—position-speed-current—is calibrated. From the inside out, the loops are the current loop, speed loop, and position loop.

[0066] The specific functions of the three rings are as follows: Current loop: The input of the current loop is the output after the PID adjustment of the speed loop. The difference between the input setpoint of the current loop and the feedback value of the current loop is used for PID adjustment within the current loop and output to the motor. The output of the current loop is the motor current.

[0067] Speed ​​Loop: The input to the speed loop is the output of the position loop PID control and the feedforward value of the position setting, called the "speed setting". The difference between the "speed setting" and the "speed loop feedback" value is used for PID control (including proportional gain and integral processing) in the speed loop, and the output is the "current loop setpoint" mentioned above. The speed loop feedback comes from the encoder feedback value processed by the "speed calculator".

[0068] Position Loop: The input to the position loop is the external pulse. After smoothing and filtering, and calculation by the electronic gear, the external pulse becomes the "position loop setting." The setting and the pulse signal from the encoder feedback, after being calculated by the deviation counter, are then processed by the PID control (proportional gain control, no integral or derivative components) of the position loop. The sum of this output and the feedforward signal for the position setting constitutes the speed loop setting mentioned above. The feedback of the position loop also comes from the encoder.

[0069] Furthermore, the electronic control unit adopts an industrial computer and uses the Ubuntu operating system to coordinate and control various functions of the platform, including simultaneous localization and mapping (SLAM) technology, vehicle body and nozzle control based on the ROS operating system, and a remote transmission system for camera images, thermal sensor images, and voice based on wireless bridge technology.

[0070] The specific workflow is as follows: If the robot is manually controlled using the operator's hand control method, the operator can visually observe the robot and judge the robot's real-time status by observing the data transmitted back from the onboard sensors (such as laser sensors and attitude sensors). The operator can send motion commands to the industrial control computer via a remote terminal using input devices such as a handle through a 5.8GHz wireless network. After processing by the industrial control computer, the commands are sent to the chassis to make the robot move and achieve the purpose of control.

[0071] If an autonomous navigation system is used to control the robot, after the industrial control computer receives radar data and various sensor data sent from the underlying layer, it comprehensively uses SLAM and autonomous navigation technology based on A* heuristic algorithm and Dijkstra algorithm to send motion commands to the chassis while drawing and correcting the site map in real time, so as to make the robot move in actual motion, thus achieving the purpose of autonomous navigation mapping and control.

[0072] Target recognition can be applied in both of the above control methods. Target features can be obtained through pre-input or deep learning methods, and target recognition algorithms can be used to identify the predetermined target. For example, residents in areas with sudden outbreaks of epidemics can be identified, and then thermal imaging can be used to monitor the residents' body temperature. Residents with abnormal body temperature can be marked on the map drawn by the autonomous navigation system.

[0073] Furthermore, SLAM includes the following steps: feature extraction, data association, state estimation, state update, and feature update. These steps update the robot's position estimation information. Since the robot position information obtained through motion estimation has significant errors, it cannot be solely relied upon to estimate the robot's position. After obtaining the robot position estimate using the robot's motion equations, the surrounding environment information obtained from the ranging unit is used to correct the robot's position. This correction process is generally achieved by extracting environmental features and then re-observing the position of these features after the robot has moved.

[0074] In urban environments, the presence of tall buildings leads to multipath effects, causing significant errors in GPS measurements, potentially reaching 5 to 10 meters or even more. This problem can be addressed by combining laser SLAM with GPS. GPS provides a coarse location within the error range, while the robot's onboard LiDAR obtains location data and features, which are then matched with the GPS map for precise local positioning within a small area. This achieves both map acquisition and accurate positioning. Simultaneously, SLAM provides real-time updated surrounding maps, aiding in obstacle avoidance during robot movement and enhancing safety and stability. By equipping the robot with GPS, a self-stabilizing gimbal, and LiDAR, and employing an original autonomous navigation technology based on the A* heuristic and Dijkstra's algorithms, combined with high-precision GPS maps and real-time SLAM positioning for path planning, the robot can achieve autonomous navigation and complete its assigned tasks. Figure 4 As shown, this figure is an actual operation diagram of autonomous navigation using a two-dimensional planar map and autonomous navigation technology based on the A* heuristic algorithm and Dijkstra's algorithm. The green line in the figure is the planned route for exploring the location environment using autonomous navigation, and the red line is the path taken by the robot.

[0075] In terms of sensors, the RPLIDAR A2 lidar is used, which utilizes the triangulation principle to perform 360° laser ranging on obstacles within an 18-meter range and transmits the range to the algorithm layer.

[0076] For autonomous mapping, the Cartographer algorithm is used. Graph-optimized algorithms have lower performance overhead and faster data convergence in situations involving large maps and complex terrain. Its core components, as shown in the figure below, can be divided into three parts: front-end matching, loop closure detection, and back-end optimization. Front-end matching first scans and matches new radar scan data with existing data, then uses the least squares method to optimize the insertion of newly acquired submaps. After insertion, it utilizes branch localization and pre-calculated grids to perform local and global loop closure.

[0077] Since Cartographer uses a large number of subgraphs to store the map, accumulated errors will occur after running the calculation for a long time. Therefore, the following loop closure detection is required to optimize the pose of all subgraphs.

[0078] Loop closure detection: If the pose of the current scan and a certain scan in all the created subgraphs are sufficiently close in distance, then a loop closure can be found using a certain scan matching strategy. Cartographer uses a branch and bound optimization method to optimize the search and improve efficiency. Once a sufficiently good match is obtained, the existence of a loop closure has been detected.

[0079] Backend optimization: This involves optimizing the camera pose. It optimizes the poses within all sub-images by using the currently scanned pose and a pose from the closest matching sub-image.

[0080] like Figure 5 As shown, this diagram illustrates the mapping and path display of the actual environment, with the green curve representing the path during exploration. The real-world environment requires the robot to perform fully autonomous mapping and scanning. An exploration algorithm, utilizing a multi-layered grid map, is employed to maximize the exploration of unknown areas in unfamiliar environments while ensuring the safety of the robot and its surroundings. GNSS positioning hardware is also included, using triangulation to obtain real-time coordinates, supplemented by base station positioning and Wi-Fi information to significantly enhance positioning accuracy. After loading the school's coordinate map, fixed-point navigation can be achieved, meeting practical needs such as campus patrols.

[0081] Furthermore, the logistics robot utilizes the ROS control package to complete control tasks. Specifically, the logistics robot uses an industrial computer based on an Intel Core 10th generation i5 platform with an x64 architecture as the host computer, and combines it with the ROS Melodic Morenia robot operating system based on Ubuntu 18.04 to control the robot chassis and perform low-level communication. Wireless communication between the robot and the control console uses a 5.8GHz bridge communication scheme.

[0082] Logistics robots use ROS as their basic operating system. Through packages such as `base_controller`, they achieve the control of the robot body, completing the interaction between the robot and hardware, and enabling flexible control of the vehicle in complex environments. `ros_control` is the middleware provided by ROS between the application and the robot, containing a series of controller interfaces, transmission interfaces, hardware interfaces, controller toolkits, etc., which can help robot applications be quickly deployed and improve development efficiency. At the same time, the ROS framework can be used to collect relevant data, providing necessary data support for SLAM (Simultaneous Localization and Mapping), enabling autonomous mapping and navigation, and autonomous control in unknown environments. This allows the robot to perform a range of tasks such as patrolling residential areas and fire prevention.

[0083] Furthermore, a wireless bridge is used to establish multi-segment network communication, including that of logistics robots, to enable multi-party data transmission.

[0084] The communication system of the logistics robot is responsible for facilitating two-way information exchange between the front and rear, including data communication, video signals, and audio signals. The video and audio signals are transmitted wirelessly via microwave equipment, while control commands are executed by a wired or wireless remote control system. The logistics robot is equipped with a terminal connected by a wireless bridge, enabling reliable information transmission with the control or information receiving end. It can transmit camera images to obtain direct image information of the target environment. It can also transmit thermal data from thermal sensors to measure the target's body temperature characteristics. Furthermore, it can transmit voice data for direct voice communication.

[0085] Furthermore, to meet the requirements of logistics robots in assisting in the rescue of trapped individuals, a deep learning network is used for object identification, accurately marking the location of objects on a map. The deep learning network adopts Darknet, with the YOLO V3 framework, and uses deep learning methods for object detection. Compared with traditional object detection methods, such as corner detection, it has advantages such as high speed, high real-time performance, and higher accuracy.

[0086] YOLO v3 achieves 20 FPS on a Pascal Titan X for processing 608x608 images, and 57.9% mAP@0.5 on COCOtest-dev. Figure 6As shown, the results are similar to those of RetinaNet, but the speed is 4 times faster. The YOLOv3 model is more complex than previous models, requiring a trade-off between speed and accuracy by changing the size of the model structure. In short, YOLO v3's prior detection system reuses a classifier or localizer to perform the detection task. The model is applied to multiple locations and scales of the image. Regions with higher scores are considered detection results. Furthermore, compared to other object detection methods, this application applies a single neural network to the entire image, which divides the image into different regions, thus predicting the bounding boxes and probabilities of each region. These bounding boxes are weighted by the predicted probabilities. Therefore, the model in this application has a significant advantage over classifier-based systems. The model in this application examines the entire image during testing, so the prediction utilizes global information in the image. Unlike R-CNN, which requires thousands of single object images, prediction is performed through a single network evaluation, making the prediction speed extremely fast—1000 times faster than R-CNN and 100 times faster than Fast R-CNN.

[0087] Furthermore, the logistics robot, equipped with camera 7, first collects facial image information of relevant residents in the community to construct a face dataset, and then trains it using a VGG-Face network built with PyTorch. The collected image information is transmitted to the central information processing unit. The central information processing unit performs simple sharpening processing on the collected image information, first using OpenCV + dlib libraries to implement face detection to determine whether a face exists in the image. Then, it uses OpenCV affine transformations to achieve face alignment, and subsequently uses the trained model to perform face matching. If the result matches the final recipient, the data is uploaded and the object is retrieved.

[0088] The Faster R-CNN deep learning framework is implemented in C++ and can be ported to ROS for target detection in robots. As long as the size of the pre-trained sample set is sufficient, deep learning algorithms can easily handle even challenging targets.

[0089] Furthermore, the logistics robot uses 4G equipment to communicate with base stations, access the internet, and communicate with servers on the internet that have public IP addresses. The servers then send the data to the main control computer. Simply put, the delivery vehicle uses 4G to access the internet and exchanges data with the main control computer via relay. This method enables ultra-low latency transmission of real-time images from camera 7, obtaining direct image information of the target environment. It also enables thermal data transmission from thermal sensors to measure the target's body temperature characteristics. Furthermore, it enables voice transmission for direct voice calls. It also enables beyond-line-of-sight communication with the delivery vehicle; as long as the robot is within 4G network coverage, it can exchange data with the local computer.

[0090] Furthermore, logistics robots equipped with thermal imaging devices can measure the body temperature of detected individuals and remind them to pay attention to their health.

[0091] Furthermore, the target's location is obtained by using a deep learning-based target detection algorithm to detect the target's position relative to the robot within the field of view. This is supplemented by thermal imaging and sound detection to achieve the goal of identifying special personnel through multi-feature fusion.

[0092] The identified target locations are used to calculate their positions on the map. All updates to existing objects are published. If new objects are added, they are added to the object array and individually labeled. These new objects are then marked on the 2D map built using SLAM and a standard TIFF image is generated and saved for viewing. A map built using Cartographer SLAM and deep learning is shown below. Figure 7 As shown, the map closely matches the test site. Red markers on the map indicate locations where objects need to be picked up, simulated using special markers. Blue markers indicate locations where delivery is required, obtained through deep learning object detection. After comparison with the test site, only a few objects were not marked due to low light or targets exceeding the range of the depth camera hardware. Furthermore, the accuracy of the marked locations is very high, meeting the robot's requirements for autonomous mapping and localization in unknown environments, thus enabling it to complete delivery tasks.

[0093] Furthermore, target tracking employs the high-speed, high-precision CSRT algorithm, combined with the YOLO v3 deep learning framework.

[0094] Furthermore, YOLO v3 uses multiple independent logistic classifiers to classify each bounding box without sacrificing accuracy. The classification loss employs binary cross-entropy loss.

[0095] Three boxes are predicted for each scale. The anchors are designed using clustering to obtain nine cluster centers, which are then evenly distributed among the three scales according to their size.

[0096] Scale 1: Add some convolutional layers after the base network to output box information.

[0097] Scale 2: Upsample (x2) from the second-to-last convolutional layer in Scale 1 and add it to the last 16x16 feature map. After passing through multiple convolutions again, the output box information is twice as large as that in Scale 1.

[0098] Scale 3: Similar to Scale 2, it uses a 32x32 feature map.

[0099] Furthermore, robotic arm 2 is a mechanical device that mimics the functions of a human arm to replace manual labor. However, its complex mechanical structure makes motion control of the multi-degree-of-freedom robotic arm 2 in three-dimensional space a challenge. This application provides two control schemes for motion planning to enable users to control the motion of robotic arm 2: First, the forward kinematics of the degrees of freedom are directly controlled via a handle. Through program encapsulation, control methods adapted to various handles are implemented. Handle information is encapsulated and converted in multiple layers to achieve real-time display of the robotic arm 2's posture and joint control.

[0100] The second method involves dragging the target position of the end effector with the mouse to perform motion planning in three-dimensional space, thus realizing inverse kinematics. After selecting the target position, an algorithm can calculate a corresponding obstacle avoidance path and display the robot arm's control and attitude in real time.

[0101] These two control schemes are based on ROS melodic on Ubuntu 18.04. The host computer is remotely connected to the industrial control computer via a network bridge, and connected to the CAN network through a self-developed USB to CAN unit. Through a self-designed communication protocol, it communicates with various underlying modules to complete the motion control and module control of the robotic arm 2.

[0102] The control scheme is designed using a top-down approach. Motion Control Layer: The motion control layer is the core of this software and is developed in C++. It completes both forward and inverse kinematics control. The forward kinematics part identifies and encapsulates handle information, supporting multiple handle controls. Each servo motor degree of freedom is controlled via the key settings on the handle. The inverse kinematics part uses OMPL (an open-source motion planning library) and advanced sampling-based kinematic algorithms (composed of RRT, PRM, etc.) for obstacle avoidance inverse kinematics planning. The planning results will be sent out, completing the autonomous motion planning of robotic arm 2.

[0103] Attitude Display Layer: In this layer, the forward and inverse kinematics information processed by the motion control layer will be published to the real-time attitude display, so that the ideal attitude of the robotic arm 2 can be seen in real time on the local device.

[0104] Data Transmission Layer: This layer is responsible for the conversion and transmission of motion control information, and is developed using C++ and the Boost library. It converts kinematic information into a self-designed communication protocol, writes the protocol data packets to the serial port, and converts them into CAN protocol information for transmission to the CAN network.

[0105] In addition, these two control schemes are also compatible with other mechanical devices that require kinematic control, such as robots and intelligent vehicles, and can be applied in various industrial production and scientific research fields.

[0106] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A logistics robot, characterized in that, It includes a mobile chassis and a robotic arm. The robotic arm is rotatably connected to the mobile chassis via a first servo motor. The first servo motor is used to drive the robotic arm to rotate relative to the mobile chassis in the horizontal plane. The robotic arm includes a large arm, a small arm, and a robotic gripper. One end of the large arm is connected to a mobile chassis via a first gear set, which drives the large arm to rotate relative to the mobile chassis in a vertical plane. The other end of the large arm is connected to one end of the small arm via a second gear set, which drives the small arm to rotate relative to the large arm in a vertical plane. The other end of the small arm is connected to the robotic gripper.

2. The logistics robot according to claim 1, characterized in that, The first gear set includes a first motor and a first pinion and a first large gear connected by a first belt drive. The first motor is mounted on the top of the mobile chassis. The output shaft of the first motor is connected to the first pinion. The first large gear is connected to the end of the upper arm away from the forearm. The first motor drives the first pinion to rotate, and the first pinion drives the upper arm to rotate via the first large gear.

3. The logistics robot according to claim 2, characterized in that, A smooth tensioning wheel is also provided between the first pinion and the first gear. The smooth tensioning wheel abuts against the back of the first belt to adjust the tension of the first belt.

4. The logistics robot according to any one of claims 1 to 3, characterized in that, The second gear set includes a second motor and a second pinion and a second large gear connected by a second belt drive. The second motor is mounted on the boom, and the output shaft of the second motor is connected to the second pinion. The second large gear is connected to the end of the forearm near the boom. The second motor drives the second pinion to rotate, and the second pinion drives the forearm to rotate via the second large gear.

5. The logistics robot according to any one of claims 1 to 3, characterized in that, The mechanical gripper includes a palm and several gripping parts. One end of each gripping part is connected to the periphery of the palm, and the other end of each gripping part is connected to a different ball joint. A third motor is installed in the palm, and a flange is connected to the output end of the third motor. The ends of the ball joints away from the corresponding gripping parts are spaced apart and connected to the periphery of the flange. The third motor drives the flange to rotate so that the gripping parts open and close synchronously via the ball joints.

6. The logistics robot according to any one of claims 1 to 3, characterized in that, The mechanical gripper is movably connected to the forearm via multiple parallel second servo motors, each of which drives the mechanical gripper to rotate around a different axis of the forearm.

7. The logistics robot according to any one of claims 1 to 3, characterized in that, The mobile chassis includes a support platform for supporting the robotic arm and rollers mounted around the bottom of the support platform, with each roller connected to the support platform via a shock absorber.

8. The logistics robot according to any one of claims 1 to 3, characterized in that, The logistics robot also includes a support frame mounted on top of the mobile chassis, which supports the end of the robotic arm away from the first servo motor when the robotic arm is rotated to its lowest position relative to the mobile chassis.

9. The logistics robot according to any one of claims 1 to 3, characterized in that, The logistics robot also includes a camera and an electronic control unit (ECU) mounted on the robotic arm. The ECU is electrically connected to the camera, the first servo motor, the first gear set, and the second gear set. The camera is used to identify image information of the items, and the ECU is used to control the movement of the robotic arm through the first and second gear sets based on the image information identified by the camera.

10. The logistics robot according to claim 9, characterized in that, The logistics robot also includes a distance sensor mounted on a mobile chassis. The electronic control unit is electrically connected to both the mobile chassis and the distance sensor. The distance sensor is used to measure the distance between the logistics robot and surrounding obstacles. The electronic control unit is also used to control the movement of the mobile chassis based on the distance information measured by the distance sensor.

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