Intelligent charging control method and system of mobile charging robot

Abstract

The application relates to the field of electric vehicle charging, and particularly discloses an intelligent charging control method and system for a mobile charging robot, which comprises the following steps: acquiring the position and state of charge of a vehicle to be charged, and the position, residual power and task state of the robot; calculating the comprehensive emergency degree of the vehicle in three dimensions of the state of charge, spatial distance and state of charge change rate, and calculating the availability of the robot in the residual power and task state, thereby establishing a matching mapping between the vehicle and the robot and generating a charging task sequence; planning a driving path for a target robot, controlling the target robot to move to a target vehicle to perform visual and force sensing fusion-based charging docking; monitoring the residual power of the robot during the charging process, triggering a follow-up charging mechanism when the residual power is lower than a threshold, assigning other robots to take over the task, and the robot with insufficient power returning to a power supply autonomously.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging, specifically a smart charging control method and system for a mobile charging robot. Background Technology

[0002] With the widespread adoption of electric vehicles, the limitations of fixed charging stations in terms of quantity, spatial layout, and operational flexibility are becoming increasingly apparent. Mobile charging robots, as a proactive charging solution, can autonomously move to vehicles waiting to be charged in scenarios such as parking lots and service areas, effectively compensating for the insufficient coverage of fixed charging facilities. In such systems, scheduling decisions are the core element determining overall service efficiency. The basic logic is as follows: Obtain the battery status and location information of each vehicle waiting to be charged, and simultaneously obtain the current status of each mobile charging robot. Based on this, establish a task matching relationship between vehicles and robots, generate a charging task sequence, and then have the robots execute it sequentially.

[0003] Currently, scheduling methods in related technologies typically employ relatively fixed processing logic. Some solutions simply sort the vehicles to be charged from low to high charge status, prioritizing the allocation of robots to the vehicles with the lowest charge. Other solutions primarily consider the spatial distance between the vehicles and robots, prioritizing the dispatch of the nearest robot to provide service. Still others simply combine multiple factors and then sort them.

[0004] However, all the above solutions share a core flaw: a lack of comprehensive assessment and coordinated matching of vehicle charging urgency and robot service capabilities. Specifically, on the one hand, a vehicle's charging urgency depends not only on its current state of charge (SBC) but also on the rate of SBC change. Using only the current SBC as a criterion can easily overlook vehicles whose charge is currently low but rapidly decreasing. On the other hand, a robot's availability is not only reflected in its idle status but also constrained by its remaining battery power—assigning a robot with insufficient power to perform a charging task may result in it running out of power before completing the charging process. Existing solutions fail to consider both factors simultaneously within the same decision-making framework, leading to inaccurate scheduling and matching relationships, and vehicles with high urgency often failing to receive the most suitable robot service. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an intelligent charging control method and system for a mobile charging robot, thereby resolving the issues of insufficient scheduling accuracy and execution reliability in the prior art due to the lack of coordinated matching between the urgency of vehicle charging and the availability of the robot.

[0006] This invention provides an intelligent charging control method for a mobile charging robot, comprising:

[0007] The system acquires charging request information reported by vehicles to be charged, including the current location and current state of charge of the vehicles to be charged; and acquires self-status information reported by each mobile charging robot, including the current location, current remaining power, and current task execution status of each mobile charging robot.

[0008] Using the current state of charge as the first dimension, the spatial distance between the current location and each mobile charging robot as the second dimension, and the rate of change of the current state of charge as the third dimension, a comprehensive urgency score is calculated for each vehicle to be charged; using the current remaining power and current task execution status of each mobile charging robot as constraints, an availability score is calculated for each mobile charging robot; a matching mapping relationship is established between the vehicles to be charged and the mobile charging robots based on the comprehensive urgency score and the availability score, and a charging task sequence is generated according to the matching mapping relationship;

[0009] Extract the highest priority charging task from the charging task sequence, mark the vehicle to be charged corresponding to the charging task as the target vehicle, mark the mobile charging robot matched with the charging task as the target robot, and plan a driving path for the target robot from the current position to the location of the target vehicle.

[0010] Control the target robot to move along the travel path to the location of the target vehicle and perform a charging docking operation;

[0011] After successful charging docking, the target vehicle is charged according to the preset charging strategy, and the remaining power of the target robot is continuously monitored during the charging process.

[0012] When the remaining battery power of the target robot is lower than the preset recharge threshold, a connection request is sent to the dispatch center, which then dispatches other mobile charging robots to take over the charging. At the same time, the target robot is controlled to detach from the dock and return to the charging station.

[0013] When the target vehicle's state of charge reaches the charging termination condition or a charging abnormality is detected, the target robot is controlled to detach from the dock and return to the standby position.

[0014] Preferably, the scoring rules for the first dimension are as follows: the state of charge is divided into several continuous numerical intervals, each interval is assigned a base score, the lower the state of charge, the higher the base score, and the base scores between adjacent intervals increase in a stepwise manner; the scoring rules for the second dimension are as follows: the minimum spatial distance between the vehicle to be charged and each mobile charging robot is used as the basis, the closer the distance, the higher the score, and the score decreases non-linearly with increasing distance; the scoring rules for the third dimension are as follows: the rate of change of the state of charge of the vehicle to be charged is calculated based on the change in the state of charge within the previous preset time window, the faster the rate of change, the higher the score; the scores of the above three dimensions are weighted and summed, and then multiplied by the weight coefficient determined by the availability score to obtain the comprehensive urgency score.

[0015] Preferably, planning the travel path for the target robot specifically includes:

[0016] Starting from the current position of the target robot and ending at the current position of the target vehicle, and constrained by the static obstacle information in the environment where the target robot is located, a global reference path is generated using the A* search algorithm.

[0017] During the movement of the target robot along the global reference path, environmental point cloud data around the target robot is collected at a preset sampling period, and the position and movement trend of dynamic obstacles are identified based on the environmental point cloud data.

[0018] When a dynamic obstacle is detected that may intrude into the global reference path, local path replanning is initiated. Multiple sets of candidate speed commands are sampled in the speed space, and a comprehensive evaluation of the safety and traffic efficiency of each candidate speed command is performed. The candidate speed command with the best comprehensive evaluation is selected as the current control variable.

[0019] The target robot is controlled to move according to the current control value to bypass the dynamic obstacle, and then returns to the global reference path after bypassing it.

[0020] Preferably, the charging docking operation specifically includes:

[0021] After the target robot moves to a preset range of the location of the target vehicle, the vision sensor mounted on the target robot is activated to collect an image containing the charging interface area of ​​the target vehicle.

[0022] The position and orientation information of the charging interface are extracted from the image, and the spatial orientation deviation between the charging gun and the charging interface on the target robot is calculated based on the position and orientation information.

[0023] The multi-degree-of-freedom robotic arm on the target robot is driven to move according to the spatial pose deviation, so that the charging gun approaches the charging interface;

[0024] As the charging gun approaches the charging interface, feedback signals from the force sensor mounted on the charging gun are acquired in real time. When the feedback signal indicates that the contact force reaches a preset contact force threshold, it is determined that the charging gun has come into contact with the charging interface. The robotic arm is then controlled to stop moving and lock its current position to complete the charging docking.

[0025] Preferably, the preset charging strategy is as follows: during the initial charging stage, the target vehicle is charged with a constant current at a first current value; when the state of charge of the target vehicle rises to a first charge threshold, the charging mode is switched to constant voltage charging mode to maintain a constant charging voltage and make the charging current gradually decrease as the state of charge increases; when the state of charge of the target vehicle rises to a second charge threshold, the charging is terminated; the first charge threshold is lower than the second charge threshold.

[0026] Preferably, the charging anomaly includes any of the following situations: the contact resistance between the charging gun of the target robot and the charging interface of the target vehicle exceeds a preset resistance threshold; the temperature of the energy storage battery of the target robot exceeds a preset temperature threshold; the duration of the communication link interruption between the target robot and the dispatch center exceeds a preset time threshold; or the charging status data frames sent by the target vehicle to the target robot during the charging process are continuously lost or have verification errors.

[0027] The present invention also provides an intelligent charging control system for a mobile charging robot, comprising:

[0028] The scheduling center includes a task scheduling module, a path planning module, and a status monitoring module;

[0029] And multiple mobile charging robots, each of which is communicatively connected to the dispatch center;

[0030] The task scheduling module is used to receive charging request information from vehicles to be charged and the status information of each mobile charging robot, and execute the task scheduling steps in the intelligent charging control method for mobile charging robots described above to generate a charging task sequence.

[0031] The path planning module is used to plan a driving path for the target robot after the task scheduling module assigns the target robot to the target vehicle, and to send the driving path to the target robot.

[0032] The status monitoring module is used to receive status information reported by each mobile charging robot in real time. When it detects that the remaining power of any mobile charging robot is lower than the preset recharge threshold, it sends a power replenishment prompt to the task scheduling module. When it detects that any mobile charging robot has an operational abnormality, it sends a task reassignment request to the task scheduling module.

[0033] Each of the mobile charging robots includes a mobile chassis, an energy storage battery, a charging actuator, a navigation and positioning module, an environmental perception module, and a robot controller. The robot controller is electrically connected to the mobile chassis, the energy storage battery, the charging actuator, the navigation and positioning module, and the environmental perception module, respectively, and is used to control the movement of the mobile chassis in response to the driving path issued by the dispatch center, as well as to perform local control of charging docking and the charging process.

[0034] Preferably, the navigation and positioning module includes a lidar and an inertial measurement unit, and the environmental perception module includes a depth camera;

[0035] The lidar is used to collect three-dimensional point cloud data of the environment in which the mobile charging robot is located. The inertial measurement unit is used to collect the acceleration and angular velocity data of the mobile charging robot. The robot controller performs time synchronization and fusion processing on the three-dimensional point cloud data and the acceleration and angular velocity data to generate a real-time pose estimate of the mobile charging robot in the environmental map.

[0036] The depth camera is used to acquire color images and depth images containing the charging interface of the target vehicle, and the robot controller extracts the three-dimensional position and three-dimensional pose of the charging interface from the color images and depth images.

[0037] Preferably, the charging actuator includes a multi-degree-of-freedom robotic arm mounted on the mobile chassis, a charging gun disposed at the end of the robotic arm, and a six-dimensional force sensor disposed between the charging gun and the end of the robotic arm;

[0038] The six-dimensional force sensor is used to detect the force along the three orthogonal directions and the torque around the three orthogonal directions that the charging gun experiences during the docking process, and feeds back the detection data to the robot controller in real time. The robot controller determines whether the charging gun is correctly connected to the charging interface based on the detection data and adjusts the movement speed of the robotic arm accordingly.

[0039] Preferably, it also includes a charging station, which is provided with multiple charging positions, each of which is connected to an AC power source and is provided with a charging connector.

[0040] The charging station is communicatively connected to the dispatch center and is used to report the occupancy and idle status of each charging position to the dispatch center.

[0041] When generating a charging task sequence, the task scheduling module marks mobile charging robots with remaining power below the preset recharge threshold as robots to be recharged, allocates an idle charging position to each of the charging positions, generates a recharge command containing the target charging position identifier and sends it to the robot to be recharged. The robot to be recharged moves to the target charging position according to the recharge command and docks with the charging connector to achieve autonomous recharge.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By comprehensively evaluating the urgency of each vehicle to be charged from three dimensions—state of charge, spatial distance, and rate of change of state of charge—and combining the remaining power of the mobile charging robot and the task execution status to calculate its availability, an optimal matching mapping relationship between the vehicles to be charged and the mobile charging robot is established. This enables the rational allocation of charging resources and the dynamic generation of charging task sequences, effectively avoiding the problem of high-urgency vehicles being delayed due to a single scheduling basis, and significantly improving the overall scheduling rationality in multi-vehicle and multi-robot scenarios.

[0044] Based on this, at the task execution level, this invention achieves autonomous generation and real-time correction of the driving path by combining global reference path search with local dynamic window replanning, enabling the mobile charging robot to safely avoid dynamic obstacles in complex parking lot environments. At the same time, in the charging docking process, a control strategy that integrates visual guidance and force guidance is adopted. The robotic arm is driven to approach the target by recognizing the spatial pose of the charging interface through binocular vision, and then the contact force is detected by a six-dimensional force sensor to determine whether contact has been made and to complete the lock. This achieves a smooth transition from coarse positioning to fine alignment, effectively improving the success rate and reliability of charging docking.

[0045] Furthermore, this invention continuously monitors the remaining battery power of the mobile charging robot during the charging process. When the battery power falls below a preset recharge threshold, a relay charging mechanism is automatically triggered. The dispatch center then assigns another available robot to take over the current charging task. Simultaneously, the robot with insufficient battery power is controlled to detach from the docking station and autonomously return to the charging station to replenish its energy. This effectively avoids the problem of task interruption or robot failure to return due to the robot running out of power, ensuring the continuity of charging services and the autonomous operation capability of the system. Through the coordinated operation of the above three levels of dispatching decision-making, motion control, and energy management, this invention realizes a fully intelligent charging service for mobile charging robots under unmanned conditions. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the overall process of the intelligent charging control method for a mobile charging robot provided in an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of the continuous charging process provided in an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Figure 1 The overall flow of the intelligent charging control method for a mobile charging robot provided in an embodiment of the present invention is illustrated. For example... Figure 1 As shown, the dispatch center first obtains the charging request information reported by the vehicles to be charged and the self-status information reported by each mobile charging robot. Then, it calculates the comprehensive urgency score of each vehicle based on three dimensions: state of charge, spatial distance, and rate of change of state of charge. At the same time, it calculates the availability score of the robot based on the remaining power and task execution status as constraints. Based on this, a matching mapping relationship between vehicles and robots is established, and a charging task sequence is generated. Then, a driving path is planned and issued for the target robot, and it is controlled to move to the location of the target vehicle to perform charging docking. After successful docking, charging is carried out according to the preset strategy, and the remaining power of the robot is continuously monitored during the charging process. When the robot's power is detected to be lower than the return charging threshold, the reconnection charging process is triggered. When the vehicle reaches the charging termination condition or a charging abnormality occurs, the robot is controlled to disengage and return.

[0050] I. Task Scheduling and Dynamic Priority Ranking

[0051] In one embodiment of the present invention, the task scheduling module of the scheduling center generates a charging task sequence in the following manner.

[0052] (a) Charging urgency rating

[0053] Suppose there are currently a total of Vehicles awaiting charging are denoted as For the first Vehicles waiting to be charged Its current state of charge is denoted as (Value range 0-100%), current position coordinates are .

[0054] The basic urgency score for the first dimension, namely the state of charge dimension, is determined as follows: The state of charge is divided into several continuous numerical intervals. In this embodiment, it is preferably divided into five intervals: 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%, with basic scores of 100, 75, 50, 25, and 0 for each interval, respectively. The lower the state of charge, the higher the basic score. The basic scores between adjacent intervals increase in a stepwise manner, with an increment of 25 points.

[0055] The second dimension, namely the spatial distance dimension, is scored as follows. Assume there are currently M mobile charging robots available, with their location coordinates as follows: For vehicles awaiting charging Calculate the Euclidean distance between it and each mobile charging robot:

[0056]

[0057] Take the minimum value among all distances This serves as the basis for scoring the vehicle's spatial distance. Distance Score Calculated using the following nonlinear decay function:

[0058]

[0059] in, The attenuation coefficient has a value range of 0.01 to 0.05, and is preferably used in this embodiment. When the minimum distance is 0, the distance score is 100 points. As the distance increases, the score decreases exponentially, and the closer the distance, the higher the score.

[0060] The third dimension, namely the rate of change of state of charge, is scored as follows: Record the vehicle to be charged. In the previous preset time window Change in the state of charge within The time window Preferably 5 minutes. Rate of change of state of charge The faster the rate of change, the faster the vehicle's battery level is decreasing, and the higher the urgency of needing to recharge. Rate of Change Rating Calculate as follows:

[0061]

[0062] in, In this embodiment, the preferred maximum reference rate of change is... .

[0063] The initial urgency score of the vehicle Vi to be charged is obtained by weighted summation of the scores from the three dimensions mentioned above:

[0064]

[0065] in, , , The weight coefficients for the three dimensions satisfy the following conditions: In this embodiment, the preferred method is... , , In other words, the state of charge has the most important weight in the urgency assessment.

[0066] (ii) Robot availability score

[0067] For the Taiwan Mobile Charging Robot Its usability score Based on remaining battery power A comprehensive assessment based on the current task execution status.

[0068] Remaining battery rating Calculated in the following segmentation method:

[0069]

[0070] Current task execution status score The determination method is as follows: if the robot is currently in an idle state, then If the robot is currently performing a charging task, the score will be proportionally calculated based on the remaining task time; if the robot is currently waiting to be recharged, then... .

[0071] Multiply the remaining battery power score by the task execution status score and normalize the result to obtain the availability score. The value range is 0 to 100.

[0072] (III) Establishment of matching mapping relationship

[0073] Vehicles awaiting charging Overall urgency score Availability ratings for each robot Matching will be performed. Specifically, the overall urgency score will be considered. Multiply by the availability score The final matching score is obtained by determining the weighting coefficients. The weighting coefficients determined by the availability score are positively correlated with the availability score. Then, a one-to-one matching mapping relationship between vehicles to be charged and mobile charging robots is established according to the final matching score from high to low, generating a charging task sequence. After each charging task is completed, the system recalculates the comprehensive urgency score of the remaining vehicles to be charged and the availability score of the remaining robots, and updates the order of the charging task sequence.

[0074] II. Path Planning

[0075] In one embodiment of the present invention, the specific process of the path planning module planning the driving path for the target mobile charging robot is as follows.

[0076] (a) Global Path Planning

[0077] Starting from the current position of the target robot and ending at the current position of the target vehicle, and constrained by static obstacle information in the environmental map, the A search algorithm is used to generate a global reference path. The cost function of the A algorithm is:

[0078]

[0079] in, From the starting point to the current node The actual cost, To start from the current node The estimated cost to the destination is calculated using Euclidean distance as a heuristic function. After the search is complete, the generated global path is smoothed using piecewise Bézier curves.

[0080] (ii) Local path replanning

[0081] As the target robot moves along the global reference path, environmental point cloud data around the robot is collected at a preset sampling period (preferably 100ms). The onboard 16-line LiDAR collects environmental point cloud data at a scanning frequency of 10Hz, with a ranging accuracy of ±2cm. The point cloud data is processed in real time using the DBSCAN clustering algorithm to identify the position and movement trends of dynamic obstacles.

[0082] When a dynamic obstacle is detected that may intrude into the global reference path (e.g., a dynamic obstacle with a point cloud cluster diameter greater than 0.5m is detected within a 5-meter range), local path replanning is initiated. Multiple candidate velocity commands are sampled in the velocity space using a dynamic window method.

[0083] The velocity space of the dynamic window method is determined by linear velocity. and angular velocity Its composition is subject to the following constraints:

[0084] Robot kinematic constraints: , ,in, The preferred speed is 1.0 m / s. 0.5 rad / s is preferred;

[0085] Safety constraint: Under the influence of the candidate velocity, the robot can stop before a collision at its current velocity;

[0086] Motor acceleration / deceleration constraints: limited by the actual acceleration / deceleration capabilities of the motor.

[0087] A comprehensive evaluation is performed on each group of candidate speed commands within the speed space. The evaluation function is as follows:

[0088]

[0089] in, The azimuth evaluation term measures the consistency between the direction at the end of the candidate trajectory and the target direction. The safety distance evaluation item measures the distance between the candidate trajectory and the nearest obstacle; For speed evaluation, the robot is encouraged to travel at a higher speed; , , These are the weighting coefficients for each evaluation item; The normalization coefficient is used to unify the dimensions of each evaluation item.

[0090] Select comprehensive evaluation The optimal candidate velocity command is used as the current control variable. The target robot is controlled to move according to the velocity command to avoid dynamic obstacles and return to the global reference path after avoiding them.

[0091] (III) Trajectory Tracking Control

[0092] Two-stage PID control is used to achieve trajectory tracking. The outer position loop generates speed commands based on the positional and heading deviations between the robot's current position and the planned path, while the inner speed loop adjusts the motor speed through encoder feedback to ensure the robot moves stably along the planned path.

[0093] III. Charging Connection

[0094] In one embodiment of the present invention, after the target robot moves to a preset range (preferably within 3 meters) of the location of the target vehicle, it performs a charging docking operation.

[0095] (a) Visual guidance positioning

[0096] The vision sensor mounted on the target robot is activated, preferably a binocular camera (baseline 15cm, resolution 1280×800). The binocular camera acquires color and depth images including the charging interface area of ​​the target vehicle.

[0097] After preprocessing the acquired images, the robot controller uses a deep learning object detection algorithm (preferably YOLOv5 model) to identify the charging interface area in the images. Once identification is complete, a binocular stereo matching algorithm is used to calculate the three-dimensional spatial position and orientation of the charging interface.

[0098] Let the position of the charging port in the camera coordinate system be... The attitude is expressed using a rotation matrix. The transformation relationship between the camera coordinate system and the robotic arm base coordinate system, obtained through hand-eye calibration, is used to convert the charging interface pose to the robotic arm base coordinate system.

[0099]

[0100] in, and These are the rotation matrix and translation vector from the camera coordinate system to the robot arm base coordinate system, respectively.

[0101] The target angles of each joint of the robotic arm are calculated based on the pose of the charging interface in the robotic arm's base coordinate system and the current pose of the charging gun. Taking a six-DOF robotic arm as an example, its kinematic equations are:

[0102]

[0103] in, Let be the homogeneous transformation matrix of the robotic arm's end effector (i.e., the charging gun) relative to the base coordinate system. There are six joint angles. Let be the homogeneous transformation matrix corresponding to the target pose of the charging gun. The target angles of each joint are obtained by solving inverse kinematics, and the robotic arm is driven to bring the charging gun closer to the charging interface.

[0104] (ii) Force-guided compliant connection

[0105] As the charging gun approaches the charging interface, a six-dimensional force sensor positioned between the charging gun and the end effector of the robotic arm detects the contact force in real time, including forces along three orthogonal directions. and torque about three orthogonal directions .

[0106] The robot controller executes a compliant control strategy based on force feedback signals. Let the desired contact force be... (Preferred in this embodiment) The actual tested contact force was Then the force deviation is:

[0107]

[0108] The controller generates a position correction ΔP based on the force deviation:

[0109]

[0110] in, , , These are the proportional, integral, and derivative coefficients, respectively. When the detected contact force reaches the preset contact force threshold (preferably 5 ± 0.5 N in this embodiment), it is determined that the charging gun has correctly contacted the charging interface. The controller then controls the robotic arm to stop moving and lock its current position, completing the charging docking.

[0111] If an abnormal contact force is detected during the docking process (such as a sudden change in force or exceeding the safe range), the controller immediately stops the robotic arm movement and performs a retraction operation. After re-perceiving visual positioning, the docking is attempted again.

[0112] IV. Charging Strategy

[0113] In one embodiment of the present invention, after the charging docking is completed, the target vehicle is charged according to the following strategy.

[0114] During the initial charging phase, with the first current value In this embodiment, constant current charging of the target vehicle is preferred. ( The rated capacity of the vehicle's battery to be charged, for example, when the battery capacity of the vehicle to be charged is 60Ah. ).

[0115] Real-time monitoring of the target vehicle's State of Charge (SOC). When the SOC rises to the first charge threshold... (Preferred in this embodiment) When the voltage reaches 0.5, switch to constant voltage charging mode. In constant voltage mode, the charging voltage is maintained at 0.5. (Determined based on the target vehicle's battery type, for example, for ternary lithium batteries,) The charging current gradually decreases as the state of charge increases.

[0116] When the state of charge of the target vehicle rises to the second charge threshold (Preferred in this embodiment) Charging will terminate when the first charge threshold is lower than the second charge threshold.

[0117] V. Handling Charging Abnormalities

[0118] In one embodiment of the present invention, the following abnormal conditions are monitored during the charging process:

[0119] (1) The contact resistance between the charging gun and the charging interface exceeds the preset resistance threshold (preferably 10mΩ).

[0120] (2) The temperature of the energy storage battery of the mobile charging robot exceeds the preset temperature threshold (preferably 55°C).

[0121] (3) The communication link between the mobile charging robot and the dispatch center is interrupted for a duration exceeding a preset time threshold (preferably 10 seconds).

[0122] (4) The charging status data frames sent by the target vehicle to the mobile charging robot during the charging process are continuously lost (e.g., more than 5 consecutive frames) or have verification errors.

[0123] When any of the above-mentioned abnormal situations are detected, the robot controller immediately interrupts charging, controls the robotic arm to pull out the charging gun at a constant speed of 0.1 m / s, and reports the abnormal information to the dispatch center. The dispatch center determines whether the charging task needs to be reassigned based on the abnormal information.

[0124] Example 1: This example provides a specific implementation process of an intelligent charging control method for a mobile charging robot.

[0125] The dispatch center receives charging request information reported in real time by each vehicle waiting to be charged via its onboard communication module. This charging request information includes the vehicle's current location (provided by the onboard GPS, accuracy ±1m) and current state of charge (provided by the battery management system (BMS), accuracy ±1%). Simultaneously, the dispatch center receives its own status information reported by each mobile charging robot via its 4G / 5G communication module, including its current location, remaining battery power, and current task execution status.

[0126] There are currently three vehicles waiting to be charged in a parking lot, with their states of charge at 15%, 45%, and 72% respectively, located in zones A, B, and C of the parking lot. Two mobile charging robots are currently available, located near the parking lot entrance and in zone D respectively, with remaining charges of 75% and 55% respectively, both in an idle state.

[0127] The task scheduling module generates a charging task sequence according to the following steps:

[0128] For vehicles with a state of charge of 15% Its SOC is in the range of 0-20%, and its basic urgency score is 100 points. The nearest robot is the one near the entrance. The distance is 25 meters, and the distance score is:

[0129]

[0130] The state of charge decreased by 1.5% in the first 5 minutes, at a rate of 0.3% / min. The rate of change score is:

[0131]

[0132] The initial urgency rating is:

[0133]

[0134] For vehicles with a state of charge of 45% Its SOC is in the range of 40% to 60%, and its basic urgency score is 50 points. The nearest robot is the one in area D. The distance is 18 meters, and the distance score is:

[0135]

[0136] The state of charge decreased by 0.8% in the first 5 minutes, at a rate of change of [missing information]. The rate of change is rated as follows:

[0137]

[0138] The initial urgency rating is:

[0139]

[0140] For vehicles with a state of charge of 72% Its SOC is in the range of 60-80%, and its basic urgency score is 25. The nearest robot is the one near the entrance. The distance is 42 meters, and the distance rating is:

[0141]

[0142] The state of charge decreased by 0.3% in the first 5 minutes, at a rate of change of [missing value]. The rate of change is rated as follows:

[0143]

[0144] The initial urgency rating is:

[0145]

[0146] Sorted from highest to lowest based on overall urgency score: .

[0147] Regarding robot availability, The remaining battery power is 75%, and it is in an idle state. The availability rating is A1=100. The remaining battery level is 55%, and the device is in idle state. Availability rating: .

[0148] The task scheduling module is distribute ,for distribute , No allocation yet (due to insufficient number of available robots). After generating the charging task sequence, the path planning module will proceed as follows: and Plan your driving route.

[0149] Start from near the entrance and head towards Area A. Based on the parking lot environment map, the route planning module uses the A* algorithm to generate a global reference path. During travel along the global path, the lidar detected a moving pedestrian (point cloud cluster diameter approximately 0.6m) 5 meters in the forward direction, determining that the pedestrian might have intruded into the global path. The local path replanning module initiated a dynamic window method, sampling candidate velocity commands in the velocity space and selecting the velocity command with the best comprehensive evaluation (linear velocity 0.3m / s, angular velocity 0.15rad / s) for control. After detouring approximately 1.5 meters to the right, it returns to the global path. From departure to arrival The location took approximately 95 seconds to complete.

[0150] arrive Once the location is within 3 meters, switch to precise positioning mode. The binocular camera captures images of the charging interface, the YOLOv5 model identifies the charging interface's position, and binocular stereo matching calculates the three-dimensional coordinates of the charging interface in the robotic arm's base coordinate system. The attitude angle is The robotic arm uses inverse kinematics to solve for the target angles of each joint, driving the arm to bring the charging gun closer to the charging interface. During the approach, the six-dimensional force sensor detects a gradual increase in the contact force along the Z-axis. When the contact force reaches 5.2N, the controller determines that the charging gun has correctly contacted the charging interface, locks the robotic arm's pose, and completes the docking. The entire docking process takes approximately 18 seconds.

[0151] After charging begins, apply a 1C current (60A) to the circuit. Initiate constant current charging. After 25 minutes of charging, The SOC increased from 15% to 80%, and the system switched to constant voltage charging mode. After charging for another 12 minutes, The SOC reached 95%, and charging was terminated. The entire charging process took 37 minutes and charged 48Ah.

[0152] After charging is complete, Disconnect from docking, pull out the charging gun at a constant speed of 0.1 m / s, and return to the standby position.

[0153] Figure 2 The continuous charging process provided by an embodiment of the present invention is illustrated. For example... Figure 2 As shown, the robot performing the charging task continuously monitors its remaining power during the charging process. When the remaining power is detected to be lower than the preset recharge threshold, it sends a takeover request to the dispatch center. The dispatch center selects one of the available robots to take over based on the availability score of each robot. After confirming that the takeover robot is in place, the current robot stops charging, disconnects from the dock, and returns to the charging station. The takeover robot then performs docking and continues charging until the vehicle reaches the charging termination condition.

[0154] Example 2: This example is an independent operating scenario from Example 1, providing a specific implementation method for a mobile charging robot to trigger continuous charging when its own power is insufficient during the charging process.

[0155] Mobile charging robot in a parking lot Vehicles waiting to be charged Charging services are provided. The energy storage battery initially had 65% remaining charge. The initial state of charge is 10%.

[0156] After charging begins, With a current of 1C Constant current charging is performed. The robot controller continuously monitors the charging process. The remaining battery power. At the 30-minute charging stage... The remaining battery power dropped to 22%. Its state of charge rose to 58%.

[0157] at this time The remaining battery power is approaching the preset recharge threshold of 20%. The robot controller sends a reconnection request to the dispatch center, which includes the following information: current task identifier ( ), Charged amount (approximately 30Ah) Current state of charge (58%) Current position coordinates Current remaining battery level (22%).

[0158] After receiving the takeover request, the dispatch center checks the status of currently available robots. At this time, another robot is in the parking lot. The previous charging task has been completed, and the device is currently idle with 80% battery remaining. It is located at a distance of Approximately 30 meters away. The task scheduling module calculates... Availability score (Remaining battery level ≥ 60% and in idle state), determine by Take over charging.

[0159] Dispatch Center to Issue a charging replacement instruction, including The location coordinates and current state of charge information. Travel according to the path planning method described in the foregoing embodiments. Location, and simultaneously to Send a confirmation signal to take over.

[0160] Upon receiving the confirmation signal, charging stops, and the robotic arm is controlled to pull out the charging gun at a constant speed of 0.1 m / s, disconnecting from the dock. Then, it returns to the charging station according to the recharge command issued by the dispatch center. After moving to an available charging spot at the charging station, it connects to the charging connector and begins to replenish its power autonomously.

[0161] arrive After positioning, perform the charging docking operation to continue charging. Because... Its state of charge has reached 58%. Continue charging directly in constant voltage mode (skipping the constant current stage) until... Charging stops once the state of charge reaches 95%.

[0162] Throughout the entire process, The charging interruption time is approximately from break away from In this embodiment, the time required to complete the docking is approximately 85 seconds. Because charging resumes rapidly after an interruption, [the following is observed]: The overall charging completion time is minimally affected.

[0163] Example 3: This example provides a specific architecture and workflow of an intelligent charging control system for a mobile charging robot.

[0164] The system includes a dispatch center and multiple mobile charging robots. The dispatch center and each mobile charging robot are connected via a 5G communication network with a communication latency of less than 20ms.

[0165] The dispatch center is deployed on a cloud server and includes a task scheduling module, a path planning module, a status monitoring module, and a database module. These modules work collaboratively as follows:

[0166] The task scheduling module continuously listens for charging requests from vehicles waiting to be charged. When a new charging request is received, the task scheduling module reads the current status data (including location, remaining battery power, and task status) and environmental map data of each robot from the database module, calculates the comprehensive urgency score of each vehicle and the availability score of each robot according to the method described in the above implementation, establishes a matching mapping relationship, and generates a charging task sequence.

[0167] After the task scheduling module completes the matching, the path planning module plans a travel path for the target robot according to the method described in the above implementation. The path planning module sends the generated path to the robot controller of the target robot in the form of a waypoint sequence (with a 1m interval between adjacent waypoints).

[0168] The status monitoring module receives status information reported by each mobile charging robot at a frequency of 1Hz. When the remaining power of any robot is detected to be lower than 20% of the preset recharge threshold, the status monitoring module sends a power replenishment prompt to the task scheduling module; when any robot is detected to have an operational abnormality (such as communication interruption for more than 10 seconds, motor overheating, etc.), the module sends a task reassignment request to the task scheduling module.

[0169] Each mobile charging robot includes a mobile chassis, an energy storage battery, a charging actuator, a navigation and positioning module, an environmental perception module, and a robot controller.

[0170] The mobile chassis features a four-wheel independent drive structure and a differential steering mechanism, with a maximum driving speed of 1.0 m / s and a minimum turning radius of 0.5 m. The energy storage battery is a lithium iron phosphate battery pack with a rated capacity of 15 kWh and a rated voltage of 48 V.

[0171] The navigation and positioning module includes a 16-line LiDAR (10Hz scanning frequency, ±2cm ranging accuracy) and a six-axis inertial measurement unit (100Hz sampling frequency). The robot controller synchronizes the 3D point cloud data collected by the LiDAR with the acceleration and angular velocity data collected by the inertial measurement unit, and performs fusion processing using a tightly coupled LiDAR-IMU SLAM algorithm to generate a real-time pose estimate of the robot in the environmental map, with a positioning accuracy better than ±5cm.

[0172] The environmental perception module includes a depth camera (1280×800 resolution, depth ranging range 0.5–5.0 m) used to acquire color and depth images of the target vehicle's charging port. The robot controller extracts the 3D position and 3D pose of the charging port from the color and depth images.

[0173] The charging actuator includes a six-degree-of-freedom collaborative robotic arm mounted on a mobile chassis, a charging gun at the end of the robotic arm, and a six-dimensional force sensor positioned between the charging gun and the end of the robotic arm. The six-dimensional force sensor measures force... Torque The measurement accuracy is and The robot controller uses an admittance control algorithm to achieve smooth docking of the charging gun based on force and torque data fed back from the six-dimensional force sensors.

[0174] During peak hours on a certain day, five vehicles simultaneously submitted charging requests in the parking lot, while only three mobile charging robots were available. The task scheduling module sorted the five vehicles according to their overall urgency score, assigning a robot to the three vehicles with the highest scores, and placing the remaining two vehicles in a waiting queue. The status monitoring module continuously monitored the status of the three robots performing charging tasks. When one robot completed its charging task, the status monitoring module sent a task completion notification to the task scheduling module. The task scheduling module then selected the vehicle with the highest overall urgency score from the waiting queue, assigned it the robot that had just completed its task, and began the next round of charging service. Through this dynamic scheduling mechanism, the three robots continuously provided service to the five vehicles during peak hours, with an average waiting time of no more than eight minutes per vehicle.

[0175] The intelligent charging control method and system for mobile charging robots provided by this invention can be widely applied in parking lots, highway service areas, residential communities, and other locations requiring flexible charging services for electric vehicles. Compared with existing technologies, this invention achieves reasonable prioritization of charging tasks and optimized robot allocation through multi-dimensional comprehensive urgency scoring; achieves safe and efficient autonomous navigation through the combination of global path planning and local path replanning; achieves high-success-rate charging docking through the fusion of visual and force-sensory guidance; and ensures the continuity of the charging process through a continuous charging mechanism. It has promising industrial application prospects and commercial value.

[0176] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made by those skilled in the art to the above embodiments within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart charging control method for a mobile charging robot, characterized in that, include: Obtain charging request information reported by the vehicle to be charged, the charging request information including the current location and current state of charge of the vehicle to be charged; And obtain the self-status information reported by each mobile charging robot, including the current location, current remaining power, and current task execution status of each mobile charging robot; Using the current state of charge as the first dimension, the spatial distance between the current location and each mobile charging robot as the second dimension, and the rate of change of the current state of charge as the third dimension, a comprehensive urgency score is calculated for each vehicle to be charged; using the current remaining power and current task execution status of each mobile charging robot as constraints, an availability score is calculated for each mobile charging robot; a matching mapping relationship is established between the vehicles to be charged and the mobile charging robots based on the comprehensive urgency score and the availability score, and a charging task sequence is generated according to the matching mapping relationship; Extract the highest priority charging task from the charging task sequence, mark the vehicle to be charged corresponding to the charging task as the target vehicle, mark the mobile charging robot matched with the charging task as the target robot, and plan a driving path for the target robot from the current position to the location of the target vehicle. Control the target robot to move along the travel path to the location of the target vehicle and perform a charging docking operation; After successful charging docking, the target vehicle is charged according to the preset charging strategy, and the remaining power of the target robot is continuously monitored during the charging process. When the remaining battery power of the target robot is lower than the preset recharge threshold, a connection request is sent to the dispatch center, which then dispatches other mobile charging robots to take over the charging. At the same time, the target robot is controlled to detach from the dock and return to the charging station. When the target vehicle's state of charge reaches the charging termination condition or a charging abnormality is detected, the target robot is controlled to detach from the dock and return to the standby position.

2. The intelligent charging control method for the mobile charging robot according to claim 1, characterized in that, The scoring rules for the first dimension are as follows: the state of charge is divided into several continuous numerical intervals, and each interval is assigned a base score. The lower the state of charge, the higher the base score, and the base scores between adjacent intervals increase in a stepwise manner. The scoring rules for the second dimension are as follows: the minimum spatial distance between the vehicle to be charged and each mobile charging robot is used as the basis. The closer the distance, the higher the score, and the score decreases non-linearly with increasing distance. The scoring rules for the third dimension are as follows: the rate of change of the state of charge of the vehicle to be charged is calculated based on the change in the state of charge within the previous preset time window. The faster the rate of change, the higher the score. The scores of the above three dimensions are weighted and summed, and then multiplied by the weight coefficient determined by the availability score to obtain the comprehensive urgency score.

3. The intelligent charging control method for the mobile charging robot according to claim 1, characterized in that, The specific steps of planning the travel path for the target robot include: Starting from the current position of the target robot and ending at the current position of the target vehicle, and constrained by the static obstacle information in the environment where the target robot is located, a global reference path is generated using the A* search algorithm. During the movement of the target robot along the global reference path, environmental point cloud data around the target robot is collected at a preset sampling period, and the position and movement trend of dynamic obstacles are identified based on the environmental point cloud data. When a dynamic obstacle is detected that may intrude into the global reference path, local path replanning is initiated. Multiple sets of candidate speed commands are sampled in the speed space, and a comprehensive evaluation of the safety and traffic efficiency of each candidate speed command is performed. The candidate speed command with the best comprehensive evaluation is selected as the current control variable. The target robot is controlled to move according to the current control value to bypass the dynamic obstacle, and then returns to the global reference path after bypassing it.

4. The intelligent charging control method for the mobile charging robot according to claim 1, characterized in that, The specific steps of performing the charging docking operation include: After the target robot moves to a preset range of the location of the target vehicle, the vision sensor mounted on the target robot is activated to collect an image containing the charging interface area of ​​the target vehicle. The position and orientation information of the charging interface are extracted from the image, and the spatial orientation deviation between the charging gun and the charging interface on the target robot is calculated based on the position and orientation information. The multi-degree-of-freedom robotic arm on the target robot is driven to move according to the spatial pose deviation, so that the charging gun approaches the charging interface; As the charging gun approaches the charging interface, feedback signals from the force sensor mounted on the charging gun are acquired in real time. When the feedback signal indicates that the contact force reaches a preset contact force threshold, it is determined that the charging gun has come into contact with the charging interface. The robotic arm is then controlled to stop moving and lock its current position to complete the charging docking.

5. The intelligent charging control method for the mobile charging robot according to claim 1, characterized in that, The preset charging strategy is as follows: during the initial charging stage, the target vehicle is charged with a constant current at a first current value; when the state of charge of the target vehicle rises to a first charge threshold, the charging mode is switched to constant voltage charging mode to maintain a constant charging voltage and make the charging current gradually decrease as the state of charge increases; when the state of charge of the target vehicle rises to a second charge threshold, the charging is terminated; the first charge threshold is lower than the second charge threshold.

6. The intelligent charging control method for the mobile charging robot according to claim 1, characterized in that, The charging anomaly includes any of the following situations: the contact resistance between the charging gun of the target robot and the charging interface of the target vehicle exceeds a preset resistance threshold; the temperature of the energy storage battery of the target robot exceeds a preset temperature threshold; the duration of the communication link interruption between the target robot and the dispatch center exceeds a preset time threshold; or the charging status data frames sent by the target vehicle to the target robot during the charging process are continuously lost or have verification errors.

7. An intelligent charging control system for a mobile charging robot, characterized in that, include: The scheduling center includes a task scheduling module, a path planning module, and a status monitoring module; And multiple mobile charging robots, each of which is communicatively connected to the dispatch center; The task scheduling module is used to receive charging request information from vehicles to be charged and the status information of each mobile charging robot, and execute the task scheduling steps in the intelligent charging control method for mobile charging robots as described in any one of claims 1 to 6 to generate a charging task sequence. The path planning module is used to plan a driving path for the target robot after the task scheduling module assigns the target robot to the target vehicle, and to send the driving path to the target robot. The status monitoring module is used to receive status information reported by each mobile charging robot in real time. When it detects that the remaining power of any mobile charging robot is lower than the preset recharge threshold, it sends a power replenishment prompt to the task scheduling module. When it detects that any mobile charging robot has an operational abnormality, it sends a task reassignment request to the task scheduling module. Each of the mobile charging robots includes a mobile chassis, an energy storage battery, a charging actuator, a navigation and positioning module, an environmental perception module, and a robot controller. The robot controller is electrically connected to the mobile chassis, the energy storage battery, the charging actuator, the navigation and positioning module, and the environmental perception module, respectively, and is used to control the movement of the mobile chassis in response to the driving path issued by the dispatch center, as well as to perform local control of charging docking and the charging process.

8. The intelligent charging control system for the mobile charging robot according to claim 7, characterized in that, The navigation and positioning module includes a lidar and an inertial measurement unit, and the environmental perception module includes a depth camera; The lidar is used to collect three-dimensional point cloud data of the environment in which the mobile charging robot is located. The inertial measurement unit is used to collect the acceleration and angular velocity data of the mobile charging robot. The robot controller performs time synchronization and fusion processing on the three-dimensional point cloud data and the acceleration and angular velocity data to generate a real-time pose estimate of the mobile charging robot in the environmental map. The depth camera is used to acquire color images and depth images containing the charging interface of the target vehicle, and the robot controller extracts the three-dimensional position and three-dimensional pose of the charging interface from the color images and depth images.

9. The intelligent charging control system for the mobile charging robot according to claim 7, characterized in that, The charging actuator includes a multi-degree-of-freedom robotic arm mounted on the mobile chassis, a charging gun disposed at the end of the robotic arm, and a six-dimensional force sensor disposed between the charging gun and the end of the robotic arm. The six-dimensional force sensor is used to detect the force along the three orthogonal directions and the torque around the three orthogonal directions that the charging gun experiences during the docking process, and feeds back the detection data to the robot controller in real time. The robot controller determines whether the charging gun is correctly connected to the charging interface based on the detection data and adjusts the movement speed of the robotic arm accordingly.

10. The intelligent charging control system for the mobile charging robot according to claim 7, characterized in that, It also includes a charging station, which is provided with multiple charging positions, each of which is connected to an AC power source and is provided with a charging connector. The charging station is communicatively connected to the dispatch center and is used to report the occupancy and idle status of each charging position to the dispatch center. When generating a charging task sequence, the task scheduling module marks mobile charging robots with remaining power below the preset recharge threshold as robots to be recharged, allocates an idle charging position to each of the charging positions, generates a recharge command containing the target charging position identifier and sends it to the robot to be recharged. The robot to be recharged moves to the target charging position according to the recharge command and docks with the charging connector to achieve autonomous recharge.

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