Robot automatic docking method and system based on laser radar and high-reflection mark

Abstract

The invention provides a robot automatic docking method and system based on a laser radar and a high-reflection mark, and solves the technical problems that the existing docking process is easily influenced by the environment and the reliability is poor. The method comprises the following steps: acquiring the position of a charging pile and the position of a robot body according to an SLAM map, and navigating the body to enter an adjacent area of the charging pile; laser reflection data of a reflective stripe group on the charging pile are obtained in the adjacent area, the relative pose of the body and the charging pile is quantified according to the reflection data, the body pose is adjusted according to the relative pose, and the alignment state with the charging pile is established; and the driving body keeps an aligned state to approach the charging pile, in-place approaching judgment is carried out according to the multi-mode signal, and flexible mechanical connection is triggered according to a judgment result to form a charging state. An automatic docking and recharging mechanism integrating laser radar intensity perception, geometric constraint guidance, multi-mode in-place judgment and mechanical self-adaptive execution significantly improves the docking success rate and the system reliability.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, specifically to an automatic robot docking method and system based on lidar and highly reflective markings. Background Technology

[0002] Autonomous mobile robots generally rely on automatic recharging capabilities to achieve long-term continuous operation. The main drawbacks of existing recharging solutions include: Visual recognition solutions (such as QR codes and AprilTags) are susceptible to changes in lighting, dirt, and obstruction of view. Infrared or ultrasonic alignment: Low ranging accuracy, making it difficult to achieve centimeter-level centering; Magnetic navigation or buried coils: require modification of the ground environment, resulting in high deployment costs; Pure point cloud matching (such as using ICP to align the charging pile outline by iterating the nearest point): computationally complex and requires high structural consistency. Contact-type microswitches: can only serve as final confirmation and lack pre-guidance capability.

[0003] The aforementioned solutions generally suffer from poor environmental adaptability, insufficient positioning accuracy, and high docking failure rates in practical applications, especially performing poorly in low-light, reflective, or dynamic interference scenarios. Therefore, there is an urgent need for an automatic recharging and docking solution that does not rely on vision, does not require environmental modification, and possesses strong robustness. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a robot automatic docking method and system based on lidar and highly reflective markings, which solves the technical problems of existing docking processes being easily affected by the environment and having poor reliability.

[0005] The automatic robot docking method based on lidar and highly reflective markers according to embodiments of the present invention includes: The location of the charging station and the robot body are obtained based on the SLAM map, and the robot body is guided to enter the area near the charging station. The laser reflection data of the reflective strip group on the charging pile is acquired in the vicinity. The relative pose of the body and the charging pile is quantified based on the reflection data. The body pose is adjusted based on the relative pose to establish an alignment state with the charging pile. The drive unit maintains alignment as it approaches the charging pile. It determines the proximity based on multi-modal signals and triggers a flexible mechanical connection to initiate the charging process.

[0006] In one embodiment of the present invention, the navigation body entering the vicinity of the charging station includes: Based on the map built using SLAM, the location of the vehicle and the charging station is determined. The vehicle then uses a path planning algorithm to avoid obstacles and plan the optimal route, and autonomously navigates to the pre-set nearby area in front of the charging station.

[0007] In one embodiment of the present invention, establishing the alignment state with the charging pile includes: Identify reflective strips based on laser reflection data; The distance between each reflective strip is identified based on the laser reflection data; The body adjusts its position based on the difference in spacing with each reflector strip to eliminate the spacing difference.

[0008] In one embodiment of the present invention, the reflective strip group is a pair of reflective strips that are perpendicular to the ground and parallel to each other. The key position of the charging pile is mapped to the reflective strip group. The key position of the charging pile is the mapping line of the charging pile's central axis onto the surface of the charging pile. The mapping line is located between the reflective strips and parallel to the reflective strips; or it is the point on the surface of the charging pile whose center of gravity is mapped onto the surface of the charging pile. The point is located between the reflective strips and on the line connecting the centers of the reflective strips.

[0009] In one embodiment of the present invention, the driving body maintains an aligned state as it approaches the charging pile, and determines the approach position based on multi-modal signals. Based on the determination result, a flexible mechanical connection is triggered to form a charging state, including: The driving body approaches the charging pile at low speed along the normal direction of the charging pile. During the approach, the main body's course is finely adjusted according to the difference in distance between the main body and each reflective strip to ensure that it is always centered between the two reflective strips and that the direction of travel always points towards the charging pile; Approaching position is determined based on distance signal, motor current signal, and wheel speed consistency signal; When it is close to the position, the body lifting mechanism is triggered to drive the charging electrode to move in a direction and make contact with the metal contacts of the charging pile to start charging.

[0010] In one embodiment of the present invention, it further includes: Anomaly handling process during the formation of proximity and charging connection.

[0011] In one embodiment of the present invention, the exception handling process includes: If an anomaly occurs during the approach process where the reflective strip information is lost, adjust the angle of the lidar base to ensure that the lidar can scan the reflective strip; If an abnormal surge in motor current signal occurs during the approach process, the device will be navigated to a nearby area and approach the charging station again. When a contact abnormality occurs, the drive body adjusts the orientation of the charging part according to the difference in the spacing of the reflective strips, and triggers the body lifting mechanism again to drive the charging electrode to move in a specific direction. When the same anomaly occurs repeatedly, the control unit enters a fault state and reports to the maintenance department.

[0012] The robot automatic docking system based on lidar and highly reflective markings according to an embodiment of the present invention includes: The memory is used to store the program code in the above-mentioned automatic robot docking method. The processor is used to execute the program code described above.

[0013] The robot automatic docking system based on lidar and highly reflective markers according to embodiments of the present invention includes: The initial navigation device is used to obtain the location of the charging station and the robot body based on the SLAM map, and guides the robot body to enter the vicinity of the charging station; The pose alignment device is used to acquire laser reflection data of the reflective strip group on the charging pile in the vicinity, quantify the relative pose of the body and the charging pile according to the reflection data, adjust the pose of the body according to the relative pose, and establish an alignment state with the charging pile. The adapter contact device is used to drive the main body to maintain an aligned state as it approaches the charging pile. It judges the proximity based on multi-modal signals and triggers a flexible mechanical connection to form a charging state based on the judgment result.

[0014] The automated robot docking system based on lidar and highly reflective markers in this embodiment of the invention further includes: An anomaly resolution device is used to handle anomalies during the approach and charging connection process.

[0015] This invention relates to an automatic robot docking method and system based on lidar and highly reflective markers. It proposes an automatic docking and recharging mechanism that integrates lidar intensity sensing, geometric constraint guidance, multimodal positioning judgment, and adaptive mechanical execution, significantly improving docking success rate and system reliability. Attached Figure Description

[0016] Figure 1 The diagram shown is a flowchart of an embodiment of the present invention for an automatic robot docking method based on lidar and highly reflective markers.

[0017] Figure 2 The diagram shown is an architectural schematic of an automatic robot docking system based on lidar and highly reflective markings according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more understandable, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0019] An embodiment of the present invention provides an automatic robot docking method based on lidar and highly reflective markers, as follows: Figure 1 As shown. In Figure 1 In this embodiment, the following are included: Step 100: Obtain the location of the charging station and the robot body based on the SLAM map, and navigate the robot body to the vicinity of the charging station.

[0020] Those skilled in the art will understand that in Simultaneous Localization and Mapping (SLAM) technology, the constructed map is a digital representation of the environment by the robot / intelligent device. Its core function is to provide spatial reference for device positioning, applicable to basic functions such as path planning, obstacle avoidance, and task execution under conditions of limited terminal computing power. By utilizing SLAM map data for autonomous approach to the charging station, a navigation process is formed where the robot navigates based on triggering conditions. Triggering conditions include, but are not limited to, battery level below a threshold, battery level decreasing below a threshold, and the estimated power consumption for a preset task being greater than the current battery level. The vicinity of the charging station is pre-marked manually, primarily to define the starting point area for docking between the robot and the charging station, facilitating aiming and movement. For example, the vicinity area can be manually marked or calculated, defining it as a 40cm radius area from the front of the charging station.

[0021] Step 200: Acquire laser reflection data of the reflective strip group on the charging pile in the vicinity area, quantify the relative pose of the body and the charging pile based on the reflection data, adjust the body pose based on the relative pose, and establish an alignment state with the charging pile.

[0022] Those skilled in the art will understand that reflective strips (such as 3M Scotchlite™ reflective film) have high reflectivity for light signals, with a significantly higher reflectivity for visible or infrared lasers than materials in the natural environment, thus clearly highlighting the environmental background. Simultaneously, the optical sensors on the robot body forming the SLAM map can identify brightness and distance differences in the reflective areas. Innovatively, reflective strips are used to form reference points for key locations on the charging station, and the significant brightness differences are used to establish a direct mapping between key locations and brightness significance. A quantitative benchmark for measuring the difference from key locations is established through a group of reflective strips. The abstract geometric relationships simplify the complexity of signal acquisition and processing, significantly reducing the demand for terminal computing power and effectively ensuring the real-time performance and stability of object perception and motion feedback. In application, the recognition of relative pose and the adjustment of the robot body's pose have significant advantages over existing technologies, enabling rapid and reliable alignment with the charging station. The simplest form of a reflective strip group can be a pair of reflective strips perpendicular to the ground and parallel to each other. The key position of the charging pile can be a mapping line from the charging pile's central axis onto its surface, located between and parallel to the parallel reflective strips; or it can be a point on the charging pile's surface mapping its center of gravity, located between the parallel reflective strips and on the line connecting their centers. The height, width, and spacing of the reflective strips can be preset, for example, a spacing of 10 cm, a height of 5-10 cm, and a width of 0.5-1 cm. The robot body quantifies its relative pose to the charging pile by judging the difference in spacing with each reflective strip and makes adjustments to establish an alignment state that meets charging requirements.

[0023] Step 300: The drive body maintains the alignment state and approaches the charging pile. It judges the proximity based on the multi-modal signal and triggers the flexible mechanical connection to form a charging state based on the judgment result.

[0024] Those skilled in the art will understand that maintaining alignment is necessary during the approach to a charging station. The alignment status is quantified by the differences in reflection data between reflective strips to correct alignment errors during the approach. Innovatively, the stable state of the charging unit's final reliable contact with the charging station is decoupled into different modal states, enabling the mutual verification of the approach status using multimodal signals. Modes can include distance, current, stability, etc. The flexible charging connection uses the mechanical structure of the contacts to form an active mating contact, avoiding coupling with the approach process and preventing potential damage or hazards from a rigid connection.

[0025] The main technical advantages of the automatic robot docking method based on lidar and highly reflective markers in this invention are as follows: Highly environmentally robust: It does not rely on visible light and is suitable for complex indoor scenarios such as darkness, bright light, and smoke; No environmental modifications required: simply attach reflective strips to the charging stations, resulting in extremely low deployment costs; Multiple safety safeguards: Through multi-modal judgment, collisions or docking failures caused by misjudgments are avoided; Mechanical adaptive design: Flexible mechanical connections protect charging contacts and extend service life; High versatility: Applicable to various wheeled service robot platforms.

[0026] like Figure 1 As shown, in one embodiment of the present invention, it further includes: Step 400: Anomaly handling process during the formation of proximity and charging connection.

[0027] To address unexpected situations such as loss of lidar reflection data, failure of multimodal signal verification, and inability to complete flexible mechanical connections during the approach process due to unforeseen interference, a corresponding troubleshooting process is established to overcome uncertainties in approach and connection, improve the success rate of automatic docking, reduce the probability of false alarms, and improve the efficiency of operation and maintenance resource utilization.

[0028] like Figure 1 As shown, in one embodiment of the present invention, step 100 includes: Step 110: Based on the SLAM map, perform path planning and autonomously navigate the main body to the preset nearby area in front of the charging station.

[0029] The robot determines the positions of itself and the charging station based on a map built using SLAM (Simultaneous Localization and Mapping). It then autonomously navigates to a pre-defined area near the charging station using path planning algorithms (such as A* and DWA) for obstacle avoidance and optimal route planning. At this point, automatic docking enters the initialization state. Depending on the robot's structure, the base portion used for electrical connection and the base portion for the LiDAR can be a synchronous follow-up structure or an independently moving structure. That is, the LiDAR base portion operates independently, while the electrical connection base portion moves based on the drive signal generated by processing the LiDAR's laser reflection data. For example... Figure 1 As shown, in one embodiment of the present invention, step 200 includes: Step 210: Identify reflective strips based on laser reflection data.

[0030] Utilizing the high reflectivity of reflective strips, the 2D / 3D LiDAR on the robot displays reflective strips with significantly higher intensity values ​​than the environmental background in its scanning field of view, exhibiting high stability. The laser reflection data includes synchronously output point cloud data containing both range and intensity information. Based on the geometric relationships of point clusters satisfying preset parameters in the point cloud data, the point cloud region corresponding to the reflective strip is identified, and the outline of the reflective strip is determined.

[0031] Step 220: Identify the distance between the laser reflection data and each reflective strip.

[0032] The distance information in the laser reflection data determines the spacing between the sensor and each reflective strip, as well as the relative pose of the reference point determined by the sensor and the reflective strip. The reference point is confirmed based on the geometric relationship (midpoint, connecting direction) of relevant points on the reflective strip. In one embodiment of the invention, the center position and normal orientation (i.e., the direction of the front of the charging pile) are determined based on the reference point.

[0033] Step 230: Drive the body to adjust its posture according to the spacing difference with each reflector to eliminate the spacing difference.

[0034] The robot body uses the difference in distance between the lidar and each reflective strip as feedback data. According to the general control feedback process, the robot body is driven to adjust its own posture by displacement and rotation based on the feedback data until the normal of the charging interface surface of the robot body is relatively aligned with the normal of the charging pile (e.g., angle deviation ≤ ±5°), thus establishing an alignment state with the charging pile and ensuring that the robot is in an initial state where it can safely approach the charging pile.

[0035] like Figure 1 As shown, in one embodiment of the present invention, step 300 includes: Step 310: Drive the main body at low speed to approach the charging pile along the normal direction of the charging pile.

[0036] Maintain the robot body at a low and constant speed (e.g., 3 cm / s) to obtain stable reflection data and avoid mechanical vibration from affecting the feedback data.

[0037] Step 320: During the approach, fine-tune the body's course according to the difference in distance between the body and each reflective strip to ensure that it is always centered between the two reflective strips and that the direction of travel always points towards the charging pile.

[0038] If the reflection data is used to perform simple geometric and trigonometric function calculations to determine that the difference in lateral distance between the left and right reflective strips and the robot exceeds a threshold (e.g., > 2 cm), then a fine-tuning steering is triggered to keep the robot body centered between the two reflective strips; at the same time, the heading angle is monitored to ensure that the forward direction always points towards the charging station (e.g., deflection angle ≤ ±3°).

[0039] Step 330: Determine the approximation position based on the distance signal, motor current signal, and wheel speed consistency signal.

[0040] When all of the following conditions are met and the connection is maintained for a preset time (e.g., 0.3–0.5 seconds), the connection is considered "complete": Distance stability: The closest distance to the charging station measured by the lidar is ≤ 5 cm, and the distance change rate is < 1 mm / s; Sudden current surge: A significant increase in the current of the drive wheel motor (e.g., exceeding 30% of the no-load current) indicates that the vehicle body has come into contact with the charging pile and is generating resistance. No slippage: The encoder speeds of the left and right wheels of the main body are consistent, eliminating the possibility of idling.

[0041] Step 340: When the device is close to the designated position, the lifting mechanism of the main body is triggered to drive the charging electrode to move in a directional manner and make contact with the metal contacts of the charging pile to form a charging process.

[0042] In one embodiment of the present invention, it specifically includes: The lifting mechanism drives the charging plates at the bottom of the robot to move downwards (e.g., a stroke of about 1–2 cm). After the charging electrode forms elastic contact with the metal contact of the charging pile, the system detects the charging voltage, reports that the charging circuit is closed, the recharge is complete, and charging begins. The left and right wheel drive motors release the motor driving force ("unloading"), and rely on springs or gravity to maintain appropriate contact pressure to avoid overpressure damage.

[0043] like Figure 1 As shown, in one embodiment of the present invention, step 400 includes: Step 410: When an abnormality occurs in the loss of reflective strip information during the approach process, adjust the angle of the lidar base to ensure that the lidar can scan the reflective strip.

[0044] Step 420: When an abnormal surge in motor current signal occurs during the approach process, navigate the main body to a nearby area and approach the charging station again.

[0045] Step 430: When a contact abnormality occurs, the drive body adjusts the orientation of the charging part according to the difference in the spacing of the reflective strips, and triggers the body lifting mechanism again to drive the charging electrode to move in the correct direction (for example, repeat 3 times).

[0046] Step 440: When the same anomaly occurs repeatedly, the control unit enters a fault state and reports to the maintenance department.

[0047] To avoid potential risks and to prevent energy-saving failures, the system includes returning to a nearby area, entering energy-saving mode, generating a fault identifier, and uploading a fault identifier.

[0048] An embodiment of the present invention provides an automated robot docking system based on lidar and highly reflective markings, comprising: The memory is used to store the program code in the process of the above-mentioned automatic robot docking method based on lidar and highly reflective markers; The processor is used to execute the program code in the above-mentioned automatic robot docking method based on lidar and highly reflective markers.

[0049] The processor can be a DSP (Digital Signal Processor), an FPGA (Field-Programmable Gate Array), an MCU (Microcontroller Unit) system board, a SoC (System on a Chip) system board, or a PLC (Programmable Logic Controller) minimum system including I / O.

[0050] An embodiment of the present invention provides an automated robot docking system based on lidar and highly reflective markings, such as... Figure 2 As shown. In Figure 2 In this embodiment, the following are included: The initial navigation device 10 is used to obtain the location of the charging pile and the robot body according to the SLAM map, and to guide the robot body into the vicinity of the charging pile. The pose alignment device 20 is used to acquire laser reflection data of the reflective strip group on the charging pile in the adjacent area, quantify the relative pose of the body and the charging pile according to the reflection data, adjust the pose of the body according to the relative pose, and establish an alignment state with the charging pile. The adapter contact device 30 is used to drive the main body to maintain alignment and approach the charging pile. It judges the proximity based on multi-modal signals and triggers the flexible mechanical connection to form a charging state based on the judgment result.

[0051] like Figure 2 As shown, in one embodiment of the present invention, it further includes: Anomaly removal device 40 is used for anomaly handling processes during the approach and charging connection process.

[0052] like Figure 2 As shown, in one embodiment of the present invention, the initial navigation device 10 includes: The objective initialization module 11 is used to perform path planning based on the SLAM map and autonomously navigate the main body to the preset nearby area in front of the charging pile.

[0053] like Figure 2 As shown, in one embodiment of the present invention, the pose alignment device 20 includes: Target recognition module 21 is used to identify reflective stripes based on laser reflection data; The difference quantization module 22 is used to identify the distance between each reflective strip based on the laser reflection data; The quantization feedback module 23 is used to drive the body to adjust its posture based on the distance difference with each reflective strip, thereby eliminating the distance difference.

[0054] like Figure 2 As shown, in one embodiment of the present invention, the adapter contact device 30 includes: Drive control module 31 is used to drive the body to approach the charging pile at low speed along the normal direction of the charging pile. The drive adjustment module 32 is used to fine-tune the body's heading during the approach process according to the difference in the distance between the body and each reflective strip, so as to ensure that it is always centered between the two reflective strips and that the direction of movement always points towards the charging pile. The positioning judgment module 33 is used to judge the proximity to the target position based on the distance signal, the motor current signal and the wheel speed consistency signal. The contact establishment module 34 is used to trigger the body lifting mechanism to drive the charging electrode to move in a specific direction and make contact with the metal contact of the charging pile when it is close to the position to form a charging.

[0055] like Figure 2 As shown, in one embodiment of the present invention, the anomaly removal device 40 includes: The information loss processing module 41 is used to adjust the angle of the lidar base when an abnormal loss of reflective strip information occurs during the approach process, so as to ensure that the lidar can scan the reflective strip. The current anomaly processing module 42 is used to navigate the main body to a nearby area and re-approach the charging pile when an abnormal surge in motor current signal occurs during the approach process. The fault handling module 43 is used to drive the main body to adjust the orientation of the charging part according to the difference in the spacing of the reflective strips when a contact fault occurs, and to trigger the main body lifting mechanism again to drive the charging electrode to move in the correct direction. The safety protection processing module 44 is used to control the main body to enter a fault state and report to the operation and maintenance when the same abnormality occurs repeatedly.

[0056] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for automatic robot docking based on lidar and highly reflective markings, characterized in that, include: The location of the charging station and the robot body are obtained based on the SLAM map, and the robot body is guided to enter the area near the charging station. The laser reflection data of the reflective strip group on the charging pile is acquired in the vicinity. The relative pose of the body and the charging pile is quantified based on the reflection data. The body pose is adjusted based on the relative pose to establish an alignment state with the charging pile. The drive unit maintains alignment as it approaches the charging pile. It determines the proximity based on multi-modal signals and triggers a flexible mechanical connection to initiate the charging process.

2. The automatic robot docking method as described in claim 1, characterized in that, The navigation unit enters the vicinity of the charging station, including: Based on the map built using SLAM, the location of the vehicle and the charging station is determined. The vehicle then uses a path planning algorithm to avoid obstacles and plan the optimal route, and autonomously navigates to the pre-set nearby area in front of the charging station.

3. The automatic robot docking method as described in claim 1, characterized in that, The alignment status with the charging pile includes: Identify reflective strips based on laser reflection data; The distance between each reflective strip is identified based on the laser reflection data; The body adjusts its position based on the difference in spacing with each reflector strip to eliminate the spacing difference.

4. The automatic robot docking method as described in claim 1, characterized in that, The reflective strip group consists of a pair of reflective strips that are perpendicular to the ground and parallel to each other. The key position of the charging pile is mapped to the reflective strip group. The key position of the charging pile is the mapping line of the charging pile's central axis onto the surface of the charging pile. The mapping line is located between the reflective strips and is parallel to the reflective strips; or it is the point on the surface of the charging pile whose center of gravity is mapped onto the surface of the charging pile. The point is located between the reflective strips and on the line connecting the centers of the reflective strips.

5. The automatic robot docking method as described in claim 1, characterized in that, The drive body maintains an aligned state as it approaches the charging pile. Based on multi-modal signals, it determines when the object has reached its designated position. Based on the determination result, it triggers a flexible mechanical connection to establish a charging state, including: The driving body approaches the charging pile at low speed along the normal direction of the charging pile. During the approach, the main body's course is finely adjusted according to the difference in distance between the main body and each reflective strip to ensure that it is always centered between the two reflective strips and that the direction of travel always points towards the charging pile; Approaching position is determined based on distance signal, motor current signal, and wheel speed consistency signal; When it is close to the position, the body lifting mechanism is triggered to drive the charging electrode to move in a direction and make contact with the metal contacts of the charging pile to start charging.

6. The automatic robot docking method as described in claim 1, characterized in that, Also includes: Anomaly handling process during the formation of proximity and charging connection.

7. The automatic robot docking method as described in claim 6, characterized in that, The exception handling process includes: If an anomaly occurs during the approach process where the reflective strip information is lost, adjust the angle of the lidar base to ensure that the lidar can scan the reflective strip; If an abnormal surge in motor current signal occurs during the approach process, the device will be navigated to a nearby area and approach the charging station again. When a contact abnormality occurs, the drive body adjusts the orientation of the charging part according to the difference in the spacing of the reflective strips, and triggers the body lifting mechanism again to drive the charging electrode to move in a specific direction. When the same anomaly occurs repeatedly, the control unit enters a fault state and reports to the maintenance department.

8. An automated robot docking system based on lidar and highly reflective markings, characterized in that, include: A memory for storing program code during the automatic docking process of the robot as described in any one of claims 1 to 7; The processor is used to execute the program code described above.

9. An automated robot docking system based on lidar and highly reflective markings, characterized in that, include: The initial navigation device is used to obtain the location of the charging station and the robot body based on the SLAM map, and guides the robot body to enter the vicinity of the charging station; The pose alignment device is used to acquire laser reflection data of the reflective strip group on the charging pile in the vicinity, quantify the relative pose of the body and the charging pile according to the reflection data, adjust the pose of the body according to the relative pose, and establish an alignment state with the charging pile. The adapter contact device is used to drive the main body to maintain an aligned state as it approaches the charging pile. It judges the proximity based on multi-modal signals and triggers a flexible mechanical connection to form a charging state based on the judgment result.

10. The automated robot docking system based on lidar and highly reflective markings as described in claim 9, characterized in that, Also includes: An anomaly resolution device is used to handle anomalies during the approach and charging connection process.

Images