Method and system for controlling the travel of a logistics breakdown trolley

Abstract

The application relates to the control field and particularly discloses a logistics decomposition trolley running control method and system. The method comprises the following steps: after a server receives information sent by wireless communication devices on multiple vehicles, a vehicle cooperation relationship model is constructed; while the vehicle running track is determined, the positioning position sequence of a single vehicle and the cooperation relationship among the vehicles are considered; according to the results of station and operation behavior identification and cooperation relationship analysis, effective positioning positions are screened out as track points to generate a preliminary vehicle running track; according to road network information, the track is optimized, and the logistics decomposition trolley running control is realized based on the generated and optimized track. The server of the application dynamically updates the cooperation relationship model, predicts path intersection or regional occupation conflicts based on the cooperation relationship model, dynamically adjusts the vehicle speed or path, jointly smooths the tracks of multiple vehicles, ensures the consistency of path curvatures of adjacent vehicles, avoids sudden turns or stops, and in this way, the problem of track misjudgment is avoided.

Description

Technical Field

[0001] This invention relates to the field of control, and in particular to a method and system for controlling the movement of a logistics breakdown vehicle. Background Technology

[0002] A logistics breakdown vehicle is a piece of equipment used in logistics and warehousing, primarily for the breakdown, sorting, and distribution of goods. In practical applications of data processing and control for logistics breakdown vehicles, multiple vehicles often work collaboratively, potentially influencing and interacting with each other. For example, one vehicle may need to wait for other vehicles to complete certain operations before continuing its journey. In such cases, simply determining the starting point and trajectory based on the location sequence of a single vehicle may not account for the collaborative relationships between vehicles, leading to misjudgments of vehicle behavior. Besides traveling between different logistics stations, logistics breakdown vehicles may also perform various operations within stations, such as loading, unloading, breakdown, and handling. The vehicle's stopping behavior in these operational scenarios may resemble its delivery behavior at the logistics station, easily leading to misjudgments. For instance, when a vehicle temporarily stops within a station to organize goods, it may be misjudged as a delivery activity at a "virtual station," thus incorrectly determining the starting point and affecting subsequent trajectory generation and analysis. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for controlling the movement of logistics decomposition vehicles, which effectively solves the problems of misjudgment of vehicle behavior due to the inability to consider the collaborative relationship between vehicles, as well as the problem of misjudgment of delivery behavior due to "virtual stations".

[0004] To achieve the above objectives, the present invention provides the following technical solution: This invention discloses a method for controlling the movement of a logistics breakdown cart, comprising the following steps: Data acquisition and preprocessing; The system divides logistics stations and work areas, determines the dwelling behavior of logistics breakdown vehicles, and identifies delivery behavior at logistics stations. Collaborative information exchange: After receiving information from wireless communication devices in multiple vehicles, the server constructs a vehicle collaboration relationship model. While determining the vehicle's trajectory, the location sequence of individual vehicles and the cooperative relationship between vehicles should be considered. Based on the results of site and operation behavior identification and collaborative relationship analysis, valid location points are selected as trajectory points, and these trajectory points are connected in chronological order to generate a preliminary vehicle driving trajectory. The initially generated trajectory is smoothed to remove jagged fluctuations and make it more consistent with the actual driving path of the vehicle. Based on road network information, the trajectory is optimized, and the movement of the logistics decomposition vehicle is controlled based on the generated and optimized trajectory.

[0005] This invention provides a logistics disassembly trolley travel control system, using the logistics disassembly trolley travel control method described above, comprising: A wireless communication device installed on a logistics disassembly cart, the wireless communication device being used for wireless communication of the logistics disassembly cart; A positioning device installed on a logistics disassembly cart, the positioning device being used to periodically collect the location information of the logistics disassembly cart; An onboard sensor assembly installed on a logistics dismantling trolley, the onboard sensor assembly being used to monitor the trolley's speed, direction, and acceleration in real time; An operation status monitoring device installed on a logistics disassembly trolley, the operation status monitoring device being used to record the timing and status changes of cargo loading and unloading, and door opening and closing. And a server, wherein the wireless communication device is connected to the server.

[0006] In another aspect, this invention discloses an electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform one or more steps of the above method.

[0007] This invention provides a method and system for controlling the movement of a logistics breakdown cart, which has the following beneficial effects: This invention achieves at least the following effects: Vehicles are treated as graph nodes, and collaborative relationships are represented as edges, quantifying the degree of collaboration. During trajectory generation, collaborative task points are forcibly inserted according to the collaborative relationship model, multi-vehicle paths are adjusted synchronously, and vehicle dwell time is bound to the collaborative task timestamp to ensure reasonable waiting behavior. Vehicles exchange information in real time, the server dynamically updates the collaborative relationship model, predicts path intersections or area occupancy conflicts, dynamically adjusts vehicle speeds or paths, and jointly smooths multi-vehicle trajectories, ensuring consistent path curvature for adjacent vehicles, avoiding sharp turns or stops, and preventing trajectory misjudgment.

[0008] By combining location data, operational status sensors, and vehicle sensors, the authenticity of dwelling behavior is verified. Operational status data is categorized and labeled to clearly distinguish between delivery and non-delivery activities. The server predefines the geographical scope of logistics stations and operational areas, and determines whether a vehicle is within the valid area based on location data. If a vehicle's movement is small within a short period, it is considered a dwelling. Delivery behavior is only marked if there are loading / unloading signals during the dwelling; otherwise, it is considered a temporary stop. By statistically analyzing the proportion of location points and operational status data, delivery behavior and temporary operations within the station are accurately distinguished, avoiding misjudgments of delivery origins and ensuring the accuracy of trajectory generation.

[0009] The technical solution of the present invention may also achieve the following effects: Based on the generated smooth trajectory, the desired steering angle and acceleration of the vehicle are calculated using preview control. Steering commands are generated through a PID controller, and the control is dynamically adjusted according to the cooperative relationship model to ensure that the vehicle travels stably along the trajectory.

[0010] When multiple vehicles are operating in parallel, steering priorities are assigned through a collaborative relationship model, and collision time algorithms are used to predict conflict points. If a collision is predicted, the current vehicle's speed and steering are adjusted first, and cooperating vehicles are notified to adjust synchronously. Based on the collaborative relationship model, paths are reserved for high-priority task vehicles, and other vehicles actively avoid them.

[0011] When collaborative tasks are temporarily changed, the server updates the collaborative relationship model and corrects the trajectory in real time, recalculates vehicle priorities, adjusts the target points of relevant vehicle trajectories, and replans the trajectories of affected vehicles locally or globally to ensure task timeliness. When communication between vehicles is interrupted, the server predicts trajectories based on historical collaborative relationships and individual vehicle positioning data, and vehicles can autonomously adjust their trajectories based on preset collaborative rules. Attached Figure Description

[0012] Figure 1 This is a flowchart of the logistics decomposition trolley movement control method of the present invention; Figure 2 This is a block diagram of the logistics decomposition trolley movement control system of the present invention. Detailed Implementation

[0013] 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.

[0014] To address the existing problems in the movement control of logistics breakdown carts, such as misjudgment of vehicle behavior due to the inability to consider the collaborative relationships between vehicles, and misjudgment of delivery behavior due to "virtual stations," this invention discloses a movement control method for logistics breakdown carts, such as... Figure 1 As shown, it includes the following steps: S1. Data acquisition and preprocessing; S2. Station and operation behavior identification; S3. Trajectory determination considering collaborative relationships; S4. Trajectory generation and optimization, and movement control of the logistics decomposition vehicle based on the generated and optimized trajectory.

[0015] Optionally, S1 data acquisition and preprocessing includes the following steps: S11. Multi-source data acquisition: Install high-precision positioning equipment, vehicle-mounted sensor components (such as accelerometers, gyroscopes, and distance sensors), operational status monitoring equipment (such as cargo loading and unloading sensors and door opening and closing sensors) and positioning equipment on the logistics disassembly cart.

[0016] During implementation, the positioning equipment collects the vehicle's location information periodically (e.g., once per second), the on-board sensor components monitor the vehicle's motion status (speed, direction, acceleration) in real time, and the operation status monitoring equipment records the occurrence time and status changes of cargo loading and unloading, and door opening and closing operations.

[0017] S12. Data fusion and tagging: The positioning data, vehicle sensor data and operation status data are fused and processed, and a timestamp is added to each data point to ensure the time consistency of the data.

[0018] At the same time, the operation status data is marked to clarify the type of operation such as loading, unloading and handling, as well as the start and end times.

[0019] S13. Data preprocessing: Filter the collected data to remove noise and outliers. For example, use a Kalman filter algorithm to optimize positioning data and improve positioning accuracy, and normalize the data from vehicle sensor components to ensure they are analyzed under a unified dimension.

[0020] Optionally, S2 site and operation behavior identification includes the following steps: S21. Logistics station and operation area division: Store the location information of logistics stations in the server, and divide different operation areas (such as loading and unloading area, sorting area, and storage area) according to the layout and function of the stations, and set a unique identifier and scope for each area.

[0021] S22. Dwelling behavior judgment: When the positioning of the trolley changes very little within a certain period of time (e.g., 5 minutes) (e.g., displacement less than 5 meters), it is determined that the trolley is in a dwelling state. At the same time, based on the operation status monitoring data, if there are changes in the sensor signals related to the loading, unloading, disassembly, and handling of goods, it is determined that the trolley is performing operations within the station.

[0022] S23. Logistics Station Delivery Behavior Identification: For locations where the location matches the logistics station location, the percentage of adjacent locations sharing the same location is calculated. If this percentage exceeds a preset threshold (e.g., 80%), and the operation status data shows delivery-related behaviors such as loading, unloading, or handover of goods, then the behavior is identified as a delivery activity at that logistics station. If the percentage does not exceed the threshold, but the operation status data shows brief non-delivery-related behaviors such as goods sorting, then it is marked as a temporary operation within the station to avoid misidentification as a delivery activity.

[0023] Optionally, S3 considers the following steps in determining the trajectory of cooperative relationships: S31. Collaborative information interaction: Each logistics breakdown vehicle is equipped with a wireless communication device, enabling it to communicate in real time with other logistics breakdown vehicles and the server. During operation, the vehicle sends its own operation status, location information, and estimated completion time to the server and related collaborative vehicles through the communication device. After receiving information from multiple vehicles, the server constructs a vehicle collaboration relationship model.

[0024] For example, if vehicle A's task is to transport goods to the location of vehicle B for handover, the server records this collaborative relationship and tracks the location changes of vehicle A and vehicle B.

[0025] During implementation, after receiving information from multiple vehicles, the server constructs a vehicle collaboration relationship model as follows: Each vehicle is considered a node in a graph, and the collaborative relationships between vehicles are considered as edges between nodes, thus constructing a graph model. The weight of the edges can be determined based on the degree of collaboration between vehicles.

[0026] For example, if vehicle A needs to wait for vehicle B to complete loading and unloading operations before handing over, then the edge weight between vehicle A and vehicle B can be set to be higher. If the vehicles only occasionally cross positions, then the edge weight can be set to be lower. When constructing a graph model, an adjacency matrix can be used to represent the structure of the graph. In practice, the collaborative relationship can be quantified by defining indicators to measure the degree of collaboration between vehicles.

[0027] For example, collaboration time, collaboration distance, and number of collaboration tasks. Collaboration time can represent the length of time that vehicles cooperate with each other, collaboration distance can represent the spatial distance between vehicles during the collaboration process, and the number of collaboration tasks can represent the number of tasks that vehicles jointly complete. By comprehensively calculating these indicators, a quantitative collaboration relationship value can be obtained, which is used to represent the degree of collaboration between vehicles.

[0028] During implementation, based on the operational logic between vehicles, the collaborative relationships between vehicles are analyzed, and the task dependency relationship analysis is as follows: Loading and unloading sequence depends on the order in which different vehicles are loaded and unloaded in logistics operations.

[0029] For example, vehicle C needs to unload its goods to a designated area before vehicle D can go to that area to load its goods. The server needs to clarify this loading and unloading sequence dependency based on the task plan, record the task association between vehicle C and vehicle D, and track the unloading progress of vehicle C to ensure that vehicle D goes to the loading and unloading point at the appropriate time.

[0030] Transportation relay dependency exists when the transportation of goods requires multiple vehicles to relay together.

[0031] For example, vehicle E transports goods from location A to a transfer point, and vehicle F then transports the goods from the transfer point to location B. The server needs to record the transportation relay relationship between vehicle E and vehicle F, and plan a suitable departure time for vehicle F based on the estimated arrival time of vehicle E to ensure the continuity of goods transportation.

[0032] The resource sharing relationship analysis is as follows: For shared loading and unloading equipment, logistics stations have a limited number of loading and unloading equipment (such as cranes and forklifts), and multiple vehicles may need to share these devices. The server needs to analyze the vehicle's usage requirements and time schedule for loading and unloading equipment, and coordinate the order of vehicle loading and unloading operations. If vehicle G and vehicle H both need to use the same crane for loading and unloading, the server needs to reasonably allocate the crane's usage time based on the urgency of the vehicle's task and its arrival time to avoid equipment conflicts.

[0033] In logistics warehouses or transit points, vehicles may need to share storage space to store goods. The server needs to analyze the storage needs and storage time of the vehicles and goods, and rationally plan the storage space.

[0034] For example, the goods of vehicles I and J need to be stored in specific areas of the warehouse. The server needs to allocate appropriate storage space to the vehicles based on the type, quantity and storage requirements of the goods to improve space utilization.

[0035] The time synchronization relationship analysis is as follows: Synchronized operation times: Some vehicles need to operate synchronously at specific times to ensure the efficient operation of the logistics process.

[0036] For example, if vehicles K and L need to arrive at a certain location at the same time to hand over goods, the server needs to adjust the vehicles' speed and routes in real time based on their location and speed to ensure that both vehicles arrive at the handover point on time.

[0037] Waiting time coordination: When a vehicle needs to wait for other vehicles to complete their work, the server needs to analyze the rationality of the waiting time based on the collaboration relationship. If vehicle M needs to wait for vehicle N to complete the loading and unloading operation before it can continue to drive, the server needs to evaluate the work progress and estimated completion time of vehicle N and provide reasonable waiting suggestions for vehicle M to avoid wasting resources due to long waiting times.

[0038] The path conflict analysis is as follows: When driving paths intersect, vehicles may travel on logistics parks or roads. The server needs to analyze the vehicle driving paths to predict the possibility of path intersections in advance and take corresponding measures to avoid conflicts.

[0039] For example, if the travel paths of vehicle O and vehicle P intersect at an intersection, the server needs to adjust the travel order or speed of the vehicles based on their travel speed and arrival time to ensure that the vehicles pass through the intersection safely.

[0040] Area occupancy conflicts may occur when vehicles occupy the work area of ​​a logistics station. The server needs to monitor the location and work status of vehicles in real time and analyze the occupancy of work areas by vehicles. If vehicles Q and R both plan to enter the same loading and unloading area for work, the server needs to coordinate the entry order of vehicles according to their task priority and work time to avoid area occupancy conflicts.

[0041] For example, if vehicle A's task is to transport goods to the location of vehicle B for handover, the server records this collaborative relationship and tracks the location changes of vehicle A and vehicle B.

[0042] S32. Trajectory Determination and Correction: When determining the vehicle's driving trajectory, the server considers the location sequence of individual vehicles and the collaborative relationship between vehicles. If a vehicle stops while waiting for other vehicles to work, the server judges the rationality of its stop based on the collaborative relationship and accurately records the waiting time and location information when generating the trajectory. For trajectory points affected by collaboration, the server corrects them according to the actual situation to ensure that the trajectory can truly reflect the vehicle's operation process and driving path.

[0043] In implementation, when determining the vehicle's trajectory, the positioning sequence of individual vehicles and the cooperative relationship between vehicles are considered simultaneously. The specific implementation steps are as follows: S321. Trajectory point filtering driven by collaborative relationships and task priority association: Based on task dependencies (such as loading and unloading order, transportation relay) in the collaborative relationship model, key nodes in the single vehicle positioning sequence are filtered. For example, if vehicle A needs to wait for vehicle B to finish unloading before it can load, the unloading completion time of vehicle B is matched with the positioning data of vehicle A to ensure that the trajectory of vehicle A includes the waiting position and time. If vehicle C and vehicle D need to arrive at the handover point simultaneously, the planned meeting time of the two is extracted through the collaborative relationship model to correct the speed and path in the single vehicle trajectory.

[0044] In implementation, resource sharing conflict handling combines resource sharing information in the collaborative relationship (such as equipment occupancy and storage space allocation) to filter invalid location points. For example, when multiple vehicles share the same loading and unloading equipment, the server marks the time the vehicle stays in front of the equipment as a valid trajectory point according to the equipment usage plan in the collaborative model, avoiding misjudgment as temporary parking. If vehicle E waits outside the warehouse area due to insufficient storage space, the server identifies the rationality of the waiting behavior through the collaborative relationship and retains the waiting position in the trajectory.

[0045] S322. Trajectory generation under cooperative relationship constraints, dynamic path constraints, based on the single vehicle positioning sequence, superimposed with path constraints in the cooperative relationship as follows: Time synchronization constraint: If vehicles F and G need to arrive at the same area at a specified time, the server adjusts the trajectory speed of the two vehicles according to the cooperative relationship model to ensure synchronization.

[0046] Area occupancy constraint: If vehicle H enters a certain area and that area is occupied by vehicle I, the server will replan the path of vehicle H according to the collaborative relationship model to avoid the conflict area.

[0047] For multi-vehicle collaborative trajectory fusion, the overall trajectory is generated using the following method for collaborative operation fleets (such as relay transportation): The endpoint trajectory of vehicle J is automatically connected to the starting trajectory of vehicle K to form a continuous transportation path.

[0048] The Bezier curve algorithm is used to jointly smooth the trajectories of multiple vehicles, ensuring that the path curvature of adjacent vehicles is consistent and avoiding sharp turns or stops. The Bezier curve algorithm is an existing technology and will not be described in detail here.

[0049] S323. Trajectory correction based on cooperative relationship and trajectory compensation for waiting behavior: When it is detected that a vehicle is stopped due to cooperative relationship (e.g., vehicle L is waiting for vehicle M to unload), the following correction is performed: Insert a virtual stop point, insert a location point for the waiting position in the trajectory, and mark it as "cooperative waiting" state.

[0050] Bind the waiting time to the timestamp of the collaborative task to ensure that the trajectory is consistent with the actual work process.

[0051] S324. Real-time adjustment of path conflicts: If the collaborative relationship model predicts multiple vehicle path intersections, the server dynamically corrects the trajectory of a single vehicle. Based on the urgency of the task, routes are reserved for high-priority vehicles, while the speed of low-priority vehicles is reduced or they are rerouted.

[0052] Based on road network information, alternative routes are generated for affected vehicles, and the estimated arrival time in the collaborative relationship model is updated.

[0053] S325. Based on the task status (such as loading / unloading completion rate, equipment occupancy status) in the collaborative relationship model, verify the rationality of the trajectory: Check whether the dwell time in the vehicle trajectory is consistent with the planned time of the collaborative task. If the deviation exceeds the threshold, trigger an alarm.

[0054] By using data from operational status sensors, it can be confirmed whether the equipment operation points of the vehicle on the trajectory are consistent with the resource allocation in the collaborative model.

[0055] S326. Based on the collaborative relationship model, the global optimization of multi-vehicle trajectories is performed as follows: For vehicles traveling in the same direction (such as platooning), their trajectories are merged into a shared path to reduce redundant calculations.

[0056] Adjust vehicle speed and start-stop strategies according to the sequence of collaborative tasks to reduce overall energy consumption (e.g., reduce frequent acceleration and deceleration).

[0057] S327. When inter-vehicle communication is interrupted, the server predicts the trajectory based on historical collaboration relationships and single-vehicle positioning data as follows: Temporary trajectory constraints are generated based on the collaborative information from the last valid communication.

[0058] Vehicles are allowed to autonomously adjust their trajectories based on preset coordination rules (such as prioritizing the completion of the current task) before communication is restored.

[0059] S328. If the collaborative task is temporarily changed (e.g., an urgent order is added), the server will update the collaborative relationship model in real time and correct the trajectory as follows: Recalculate vehicle priorities and adjust the trajectory target points of relevant vehicles.

[0060] Based on the new collaborative relationship, the trajectories of affected vehicles are replanned locally or globally to ensure mission timeliness.

[0061] Optionally, S4 trajectory generation and optimization includes the following steps: S41. Based on the results of site and operation behavior identification and collaborative relationship analysis, select effective positioning locations as trajectory points.

[0062] In implementation, the effective trajectory point selection rules include: 1. Only retain location points whose positions are within the logistics station or work area. Combine operational status data (such as cargo loading and unloading sensor signals) to filter out location points directly related to delivery activities (such as loading, unloading, and handover) or necessary in-station operations (such as sorting and organizing). Exclude location points resulting from unnecessary stops such as equipment malfunctions or brief detours.

[0063] 2. For collaborative tasks (such as relay transportation and equipment sharing), retain the location points that are synchronized with or intersect with the collaborative vehicles. If a vehicle stops due to a collaborative relationship (such as waiting for other vehicles to complete their work), its location point must be marked as a collaborative waiting state and associated with the corresponding collaborative task ID. Filter out redundant location points unrelated to the collaborative task (such as random movement of vehicles when they are empty).

[0064] 3. Check the continuity of timestamps for location points and delete isolated points with intervals exceeding a preset threshold (e.g., 30 seconds). For densely populated location points (e.g., vehicles frequently changing positions within a station), use a sliding window algorithm to merge adjacent points to reduce trajectory redundancy.

[0065] During implementation, the process of generating a preliminary vehicle trajectory includes: Input the preprocessed positioning data, job status markers, and collaborative task list.

[0066] The filtering process filters out location points that are not in the site / non-operation area, retains location points that match the collaborative task timestamp, and deletes isolated points with an interval exceeding 30 seconds.

[0067] Connect and mark: Connect the filtered points in chronological order to generate the basic trajectory, insert mandatory nodes for collaborative tasks (such as handover points), and mark key nodes (sites, regions, collaborative tasks).

[0068] Output structured trajectory data (including node type, timestamp, and associated task ID).

[0069] Through the above refinement, the trajectory point selection and connection process can more accurately reflect the actual operation process of the logistics breakdown vehicle, while ensuring deep coupling with the collaborative relationship model and operation behavior recognition results, improving the accuracy of subsequent trajectory generation and control, and connecting these trajectory points in chronological order to generate a preliminary vehicle driving trajectory.

[0070] S42. The initially generated trajectory is smoothed using the Bezier curve algorithm to remove jagged fluctuations in the trajectory, making it more consistent with the actual driving path of the vehicle.

[0071] During implementation, the key nodes of the trajectory (such as the location of logistics stations and the boundaries of the work area) are kept unchanged during the smoothing process.

[0072] S43. Trajectory optimization and verification: Optimize the trajectory by combining road network information.

[0073] For example, the speed planning of the trajectory is adjusted according to factors such as road speed limits and congestion. The trajectory is checked for violations of traffic rules or logistics operation procedures (such as driving against traffic or entering prohibited areas). If any violations are found, they are corrected. The optimized trajectory is verified through actual operation data to ensure its accuracy and reliability.

[0074] S44. Finally, based on the generated and optimized trajectory, the movement of the logistics decomposition vehicle is controlled.

[0075] In practice, the specific aspects of controlling the movement of the logistics decomposition vehicle based on the generated and optimized trajectory include: Based on the generated smooth trajectory, look-ahead control is used to calculate the vehicle's desired steering angle and acceleration to ensure that the vehicle travels stably along the trajectory.

[0076] In each control cycle (e.g., 100ms), a pre-aiming point is selected on the trajectory based on the current position and the pre-aiming distance (dynamically adjusted, e.g., 1 meter at low speed and 5 meters at high speed).

[0077] The lateral deviation (ey) and heading deviation (eθ) between the current pose and the aiming point are calculated, and steering commands are generated through a proportional-integral-derivative (PID) controller.

[0078] During the process of calculating the lateral deviation (ey) and heading deviation (eθ) between the current pose and the aiming point, and generating steering commands through a proportional-integral-derivative (PID) controller, the positioning equipment (such as a high-precision GPS or UWB positioning device) installed on the logistics disassembly vehicle periodically collects the vehicle's position information. At the same time, the accelerometer, gyroscope, and other sensors in the vehicle's sensor components can monitor the vehicle's motion status in real time. By fusing these sensor data, the vehicle's current position coordinates (x, y) and current heading angle θ are calculated, thereby determining the vehicle's current pose.

[0079] Within each control cycle (e.g., 100ms), the aiming distance is dynamically adjusted based on the vehicle's current speed (e.g., when the vehicle is traveling at low speed, such as less than 5km / h, the aiming distance is set to 1 meter; when the vehicle is traveling at high speed, such as greater than 15km / h, the aiming distance is set to 5 meters). Then, based on the vehicle's current direction of travel and the aiming distance, an aiming point is selected on the generated smooth trajectory. The coordinates of this aiming point are denoted as (xp, yp), and its corresponding desired heading angle is denoted as θp.

[0080] Calculate the lateral deviation (ey): The lateral deviation ey is the perpendicular distance from the current position of the vehicle to the tangent of the trajectory at the preview point. By calculating the relative positional relationship between the current position coordinates (x, y) and the preview point coordinates (xp, yp), and combining this with the direction of the tangent to the trajectory at the preview point, the specific value of the lateral deviation ey can be obtained. If the current position of the vehicle is to the left of the tangent to the trajectory at the preview point, then ey is a positive value; if it is to the right, then ey is a negative value.

[0081] Calculate the heading deviation (eθ): The heading deviation eθ is the difference between the current heading angle θ of the vehicle and the expected heading angle θp at the aiming point, i.e., eθ = θp - θ. When eθ is positive, it means that the vehicle's current heading needs to be adjusted to the right; when eθ is negative, it means that the vehicle's current heading needs to be adjusted to the left.

[0082] The PID controller generates steering commands: The proportional-integral-derivative (PID) controller generates steering commands based on the calculated lateral deviation ey and heading deviation eθ. The PID controller consists of three parts: proportional (P), integral (I), and derivative (D). Proportional control output: Based on the current values ​​of lateral deviation ey and heading deviation eθ, multiply them by the corresponding proportional coefficient (Kp). The proportional coefficient Kp determines the controller's response speed to deviations. The larger the Kp value, the faster the controller reacts to deviations, but an excessively large Kp value may lead to system instability.

[0083] Integral part: The lateral deviation ey and heading deviation eθ are integrated over a certain time period and multiplied by the integral coefficient (Ki) to obtain the integral control output. The function of the integral part is to eliminate the steady-state error of the system. Even when the deviation is small, the integral term will continue to accumulate until the deviation is zero.

[0084] The differential component calculates the rate of change of lateral deviation ey and heading deviation eθ, multiplies it by the differential coefficient (Kd), and obtains the differential control output. The differential component can predict the trend of deviation changes and adjust the control output in advance, thereby improving the system's response speed and stability. Finally, the outputs of the proportional, integral, and differential components are added together to obtain the final steering command. This command is sent to the electronic power steering (EPS) system via the CAN bus to control the motor angle, enabling the vehicle to travel in the desired direction, reducing lateral and heading deviations, and achieving stable tracking along the trajectory.

[0085] According to the collaborative relationship model, if there are multi-vehicle collaborative tasks (such as arriving at the handover point simultaneously), the aiming distance and PID parameters are adjusted to force trajectory synchronization.

[0086] If there are multi-vehicle collaborative tasks (such as arriving at a handover point simultaneously), adjust the aiming distance, including: Dynamic adjustment based on speed: Vehicle speed is a key factor in adjusting the aiming distance. If the speed is high, the aiming distance needs to be increased to respond promptly to trajectory deviations; conversely, if the speed is low, the aiming distance can be appropriately reduced to improve trajectory tracking accuracy. For example, during normal operation, the aiming distance of logistics breakdown vehicle A is set to 3 meters. When a collaborative task requiring synchronization with vehicle B to reach the handover point is detected, and vehicle A's speed is 12 km / h, based on the high-precision synchronization requirements of the collaborative task, its aiming distance is adjusted to 4.5 meters.

[0087] Relative Position Considerations: The relative positions of the vehicles are equally important. If car A and car B are to arrive at the handover point simultaneously, and car A is some distance ahead of car B, then car A's aiming distance needs to be adjusted based on the distance difference with car B and the expected synchronization time. Assuming car A is 6 meters ahead of car B and expects to arrive simultaneously after 12 seconds, calculations show that car A's aiming distance may need to be shortened to 2.8 meters to better match car B's trajectory.

[0088] In terms of task priority: For high-priority collaborative tasks, the pre-aiming distance is controlled more strictly. When performing emergency medical supply delivery collaborative tasks, to ensure vehicles arrive at the handover point accurately and efficiently, the pre-aiming distance adjustment accuracy of participating vehicles can reach the decimeter level. For example, in this task, car C's pre-aiming distance was precisely adjusted from the original 3.2 meters to 3.05 meters based on task requirements and actual conditions.

[0089] If there are multi-vehicle collaborative tasks (such as synchronous arrival at the handover point), then adjust the PID parameters: Proportional coefficient (Kp) adjustment: To achieve close synchronization and enable rapid vehicle response to trajectory deviations, the Kp value can be appropriately increased to enhance the controller's responsiveness to current deviations. However, an excessively large Kp value can lead to system instability and requires careful adjustment. For example, under normal driving conditions, the Kp value of vehicle D is 0.6. After detecting a synchronized task with vehicle E, the Kp value is increased to 0.8 after evaluation, enabling vehicle D to react more quickly to deviations from vehicle E's trajectory.

[0090] Integral coefficient (Ki) adjustment: The Ki value is adjusted to account for potential steady-state errors in collaborative tasks. To eliminate steady-state errors more quickly and ensure precise matching of vehicle trajectories with collaborating vehicles, the Ki value can be increased to enhance the cumulative effect of the integral term on errors. However, an excessively large Ki value may cause integral saturation, affecting system performance. In multi-vehicle collaborative long-distance transportation tasks, to ensure that vehicle trajectories remain synchronized during long-term travel, the integral coefficient Ki of vehicle F is increased from 0.12 to 0.22 to better eliminate steady-state errors caused by various factors.

[0091] Differential coefficient (Kd) adjustment: To predict the trend of trajectory deviation changes and make adjustments in advance, the Kd value needs to be adjusted. In cooperative tasks, to ensure vehicles smoothly follow the cooperative trajectory and avoid large fluctuations, the Kd value can be increased to improve sensitivity to the rate of change of deviation. However, an excessively large Kd value may make the system overly sensitive to noise. When multiple vehicles need to reach the handover point synchronously at high speed, to prevent unstable situations such as sudden turns or speed changes when vehicles approach the handover point, the differential coefficient Kd of vehicle G is adjusted from 0.25 to 0.35, allowing the vehicles to adjust their trajectories more smoothly and achieve synchronization.

[0092] Comprehensive Dynamic Adjustment: In actual operation, the PID parameters are not fixed but are dynamically adjusted based on the real-time vehicle operating status, the progress of collaborative tasks, and real-time communication feedback between vehicles. For example, as the vehicle approaches the handover point, the PID parameters are continuously adjusted based on real-time position deviation and speed changes as the distance shortens. When there are still 8 meters to the handover point, based on the current deviation and its trend, the Kp value of the vehicle H is adjusted to 0.85, the Ki value to 0.28, and the Kd value to 0.38, enabling the vehicle to adjust its trajectory more accurately and achieve synchronous arrival with the collaborating vehicles. Therefore, the adjustment of PID parameters in this invention is a comprehensive dynamic adjustment of PID parameters.

[0093] Based on the wheelbase and maximum steering angle of the logistics dismantling vehicle, calculate the maximum permissible radius of curvature of the trajectory to avoid sudden turns that could cause rollover or cargo slippage. First, clarify that the wheelbase of the logistics dismantling vehicle refers to the distance between the front and rear axles, a fixed vehicle parameter obtainable from the vehicle's design specifications or relevant technical documents. The maximum steering angle refers to the maximum angle the wheels can deflect when the steering wheel is turned to its limit, also obtainable from the vehicle's technical parameters. The maximum permissible radius of curvature of the trajectory is calculated as follows: Given the wheelbase of the logistics dismantling vehicle as L and the maximum steering angle as θmax, the maximum permissible radius of curvature Rmax = L / sin(θmax).

[0094] Therefore, this invention is based on the pre-aiming control calculation vehicle, which generates steering commands with a proportional-integral-derivative controller and dynamically controls the vehicle according to the cooperative relationship model.

[0095] The specific aspects of trajectory generation and optimization-based movement control for logistics breakdown vehicles also include: calculating the average speed (e.g., 5 km / h in the sorting area and 15 km / h on the travel route) based on the trajectory's starting point, ending point, and collaborative task time window, and automatically decelerating to a safe speed (e.g., 3 km / h) before entering the work area.

[0096] If a vehicle or obstacle is detected ahead (via cooperative communication or onboard sensors), the speed is smoothly adjusted using an S-shaped acceleration / deceleration curve.

[0097] Based on real-time trajectory tracking using anticipation control, the steering command is calculated from lateral deviation and heading deviation.

[0098] When performing a relay transport task, if the cooperating vehicle has not arrived at the handover point, the steering sensitivity will be automatically reduced to avoid entering the waiting area too early.

[0099] Therefore, this invention uses an S-shaped acceleration / deceleration curve to smoothly adjust the speed, uses pre-aiming control to track the trajectory in real time, and dynamically adjusts the steering sensitivity according to the cooperative vehicle.

[0100] The specific aspects of trajectory generation and optimization-based control of logistics decomposition vehicles also include: when multiple vehicles are operating in parallel, assigning steering priorities through a collaborative relationship model (such as giving priority to vehicles with emergency tasks).

[0101] The location, speed, and trajectory of surrounding vehicles are obtained through vehicle-to-vehicle communication, and the collision time (TTC) algorithm is used to predict the collision point.

[0102] If a collision is predicted (TTC < 2 seconds), prioritize adjusting the speed and steering of the current vehicle (e.g., slowing down or detouring), and notify cooperating vehicles to adjust synchronously.

[0103] Based on the collaborative relationship model, a path is reserved for high-priority task vehicles (such as emergency orders), and other vehicles actively give way.

[0104] Before entering the loading and unloading area, the equipment occupancy status (such as forklift position) is confirmed through the collaborative relationship model, and the parking position is dynamically adjusted.

[0105] When multiple vehicles share loading and unloading equipment, they enter the designated parking spaces in sequence according to task priority, and are guided to precise alignment by steering commands (error ≤ 10cm).

[0106] Based on the sorting order and shelf location, a serpentine or Z-shaped trajectory is generated to reduce the number of times the vehicle needs to reverse.

[0107] Therefore, this invention allocates steering priorities through a collaborative relationship model, uses a collision time algorithm to predict conflict points to adjust vehicle speed and steering, and notifies cooperating vehicles to adjust synchronously; based on the collaborative relationship model and dynamic priority adjustment, a trajectory is generated according to the sorting order of goods and shelf position.

[0108] The specific control of the logistics decomposition vehicle based on the generated and optimized trajectory also includes: in the low-level control, the desired steering angle and acceleration of the trajectory tracking are converted into specific control signals, wherein the steering command is sent to the electronic power steering system (EPS) via the CAN bus to control the motor rotation angle.

[0109] The speed command is then sent to the drive motor controller to adjust the throttle / brake opening (e.g., 0-100% accelerator pedal opening corresponds to 0-15km / h).

[0110] Physical limits are embedded in the control commands, with a maximum steering angle of ±45° (adjusted according to vehicle model) and a maximum acceleration of ±2m / s² (to prevent cargo tipping).

[0111] Emergency braking is triggered when an obstacle is detected at a distance of <0.5 meters or with a lateral deviation >0.3 meters, at which point the brakes are immediately engaged.

[0112] Lateral deviation (ey) is ideally <0.1 meters; heading deviation (eθ) is ideally <5°; collaborative task completion time error is <10 seconds; energy efficiency is <0.5 kWh / km per unit distance.

[0113] Based on the above details, the logistics decomposition vehicle can achieve high-precision path tracking, multi-vehicle collaborative obstacle avoidance, dynamic task response, and safety boundary control based on the generated and optimized trajectory, thus solving problems such as misjudgment in multi-vehicle collaboration and inaccurate in-station operation trajectory in existing technologies.

[0114] In summary, by treating vehicles as nodes in a graph and collaborative relationships (such as task dependencies and resource sharing) as edges, and by quantifying the degree of collaboration (such as weights representing waiting time and task priorities), collaborative task points (such as handover locations) are forcibly inserted during trajectory generation based on task dependencies (such as loading / unloading order and relay transportation) in the collaborative relationship model. Simultaneously, multi-vehicle paths are adjusted, and vehicle dwell time is bound to the timestamps of collaborative tasks to ensure the rationality of waiting behaviors in the trajectory (such as matching the dwell time of vehicle A with the unloading completion time of vehicle B). Vehicles exchange locations, operational statuses, and task progress in real time via wireless communication. The server dynamically updates the collaborative relationship model, predicts path intersections or area occupancy conflicts based on the collaborative relationship model, and dynamically adjusts vehicle speeds or paths (such as reserving paths for high-priority vehicles and detouring for low-priority vehicles). Joint smoothing of multi-vehicle trajectories ensures consistent path curvature between adjacent vehicles, avoiding sharp turns or stops, thereby preventing trajectory misjudgments. Furthermore, this is combined with location data (such as GPS). The system uses UWB (Ultra-Wideband) sensors, operational status sensors (such as cargo loading / unloading and door opening / closing), and vehicle-mounted sensors (such as accelerometers and gyroscopes) to verify the authenticity of dwelling behavior. It categorizes and labels operational status data (such as loading / unloading start / end times and cargo weight changes), clearly distinguishing between delivery behaviors (such as loading / unloading) and non-delivery behaviors (such as sorting cargo). The geographical scope of logistics stations and operational areas (such as loading / unloading areas and sorting areas) is predefined on the server. Location data is used to determine if a vehicle is within a valid area. If a vehicle's displacement is less than 5 meters within 5 minutes, it is considered a dwelling. Only when dwelling is accompanied by operational signals such as loading / unloading or handling (such as cargo weight changes) is it labeled as a delivery behavior; otherwise, it is considered a temporary stop. If the proportion of adjacent positioning points at a certain station location exceeds 80%, and the operational status data shows delivery-related behavior, it is determined to be a delivery behavior. Dwellings with a proportion below the threshold but exhibiting brief sorting behavior are labeled as "temporary work within the station" to avoid misjudging as delivery starting points, thus preventing issues related to misjudging the starting point location and trajectory generation.

[0115] It is evident that one or more steps of the method of the present invention can be implemented by a computer program, which can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer program causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer program can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] Therefore, it can be understood that this invention discloses an electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform one or more steps of the above method.

[0117] like Figure 2 As shown, this application also discloses a logistics disassembly trolley travel control system, using the above-described logistics disassembly trolley travel control method, including: A wireless communication device installed on a logistics disassembly cart; the wireless communication device is used for wireless communication on the logistics disassembly cart. A positioning device installed on a logistics disassembly cart is used to collect the location information of the logistics disassembly cart at regular intervals. The on-board sensor assembly installed on the logistics disassembly trolley is used to monitor the trolley's speed, direction, and acceleration in real time. The on-board sensor assembly includes an accelerometer, a gyroscope, and a distance sensor. The operation status monitoring equipment installed on the logistics disassembly trolley is used to record the time and status changes of cargo loading and unloading, and door opening and closing. The operation status monitoring equipment includes cargo loading and unloading sensors and door opening and closing sensors. And servers, wireless communication devices connect to servers.

[0118] The servers of the systems described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] In summary, the vehicle-mounted sensor components, including accelerometers, gyroscopes, and distance sensors, monitor the vehicle's speed, direction, and acceleration in real time. This monitoring information is then wirelessly transmitted to the server. The positioning device periodically transmits the location information of the logistics breakdown vehicle to the server via wireless communication. The operation status monitoring device records the loading and unloading of goods, door opening and closing times, and status changes, all transmitted wirelessly to the server. Finally, after receiving information from multiple vehicles' wireless communication devices, the server constructs a vehicle cooperation relationship model, determines the vehicle's trajectory, and considers the location sequence of individual vehicles and the cooperation relationships between vehicles. Based on the station and operation behavior identification and cooperation relationship analysis results, valid location points are selected as trajectory points. These trajectory points are connected in chronological order to generate a preliminary vehicle trajectory. The generated trajectory is then smoothed to remove jagged fluctuations, making it more consistent with the vehicle's actual travel path. Finally, road network information is used to optimize the trajectory, and the logistics breakdown vehicle's movement is controlled based on the generated and optimized trajectory.

[0120] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling the movement of a logistics breakdown cart, characterized in that, Includes the following steps: The system divides logistics stations and work areas, determines the dwelling behavior of logistics breakdown vehicles, and identifies delivery behavior at logistics stations. Collaborative information exchange: After receiving information from wireless communication devices in multiple vehicles, the server constructs a vehicle collaboration relationship model. While determining the vehicle's trajectory, the location sequence of individual vehicles and the cooperative relationship between vehicles should be considered. Based on the results of site and operation behavior identification and collaborative relationship analysis, valid location points are selected as trajectory points, and these trajectory points are connected in chronological order to generate a preliminary vehicle driving trajectory. The initially generated trajectory is smoothed to remove jagged fluctuations and make it more consistent with the actual driving path of the vehicle. Based on road network information, the trajectory is optimized, and the movement control of the logistics decomposition vehicle is based on the generated and optimized trajectory. The movement control of the logistics decomposition vehicle based on the generated and optimized trajectory includes: The vehicle is calculated based on the anti-aiming control, and steering commands are generated by a proportional-integral-derivative controller, which is then dynamically controlled according to the cooperative relationship model.

2. The method for controlling the movement of a logistics breakdown cart according to claim 1, characterized in that, It also includes data acquisition and preprocessing steps: Multi-source data acquisition involves installing positioning devices, onboard sensor components, and operational status monitoring equipment on the logistics disassembly cart. The collected data is fused and labeled, and then preprocessed.

3. The method for controlling the movement of a logistics breakdown cart according to claim 2, characterized in that: In the process of data fusion and labeling, positioning data, vehicle sensor component data and operation status data are fused together. In the data preprocessing, the collected data is filtered to remove noise and outliers, and the data from the vehicle sensor components is normalized to ensure that it is analyzed under a unified dimension.

4. The method for controlling the movement of a logistics disassembly trolley according to claim 3, characterized in that: In the collaborative information exchange, the logistics breakdown vehicle communicates in real time with other vehicles and the server through wireless communication devices to complete the collaborative information exchange.

5. The method for controlling the movement of a logistics breakdown cart according to claim 4, characterized in that, The construction of the vehicle cooperative relationship model includes the following steps: Each vehicle is considered as a node in a graph, and the collaborative relationships between vehicles are considered as edges between nodes, thus constructing a graph model; Quantify the collaborative relationship by defining indicators to measure the degree of collaboration between vehicles; Analyze the collaborative relationships between vehicles based on their operational logic.

6. The method for controlling the movement of a logistics breakdown cart according to claim 5, characterized in that, The analysis of the collaborative relationships between vehicles includes: Task dependency analysis, which includes analyzing control loading and unloading sequence dependencies and transportation relay dependencies; Resource sharing relationship analysis, which includes analyzing and controlling the sharing of loading and unloading equipment and the sharing of storage space; Time synchronization relationship analysis, which includes analyzing the synchronization of control operation time and the coordination of waiting time; Path conflict relationship analysis, which includes analyzing and controlling the intersection of driving paths and area occupancy conflicts.

7. The method for controlling the movement of a logistics breakdown cart according to claim 6, characterized in that, Determining the vehicle's trajectory while considering the location sequence of individual vehicles and the cooperative relationship between vehicles includes the following steps: Based on the task dependency analysis in the collaborative relationship model, key nodes in the single-vehicle positioning sequence are selected. By analyzing resource-sharing relationships within collaborative relationships, invalid location points are filtered out. Based on the single-vehicle positioning sequence, path constraints in the cooperative relationship are superimposed; For convoys operating collaboratively, the overall trajectory is generated using segmented splicing and collaborative smoothing methods; When a vehicle is detected to be stopped due to a collaborative relationship, a correction is performed by inserting a virtual stop point and aligning the timestamp. If the collaborative relationship model predicts multiple vehicle paths to intersect, the server dynamically corrects the trajectory of a single vehicle. Verify the rationality of the trajectory based on the task status in the collaborative relationship model; Global optimization of multi-vehicle trajectories is performed based on a collaborative relationship model; When communication between vehicles is interrupted, the server predicts the trajectory based on historical collaboration relationships and individual vehicle positioning data; If the collaborative task is temporarily changed, the server will update the collaborative relationship model and correct the trajectory in real time.

8. The method for controlling the movement of a logistics disassembly trolley according to claim 7, characterized in that, The trajectory-based control of logistics decomposition vehicles also includes: The speed is smoothly adjusted using an S-shaped acceleration / deceleration curve, the trajectory is tracked in real time based on pre-aiming control, and the steering sensitivity is dynamically adjusted according to the cooperative vehicle. Steering priorities are assigned through a collaborative relationship model, collision time algorithms are used to predict conflict points to adjust vehicle speed and steering, and collaborative vehicles are notified to adjust synchronously; based on the collaborative relationship model and dynamic priority adjustment, trajectories are generated according to the sorting order of goods and shelf location.

9. A logistics disassembly trolley travel control system, using the logistics disassembly trolley travel control method as described in claim 8, characterized in that, include: A wireless communication device installed on a logistics disassembly cart, the wireless communication device being used for wireless communication of the logistics disassembly cart; A positioning device installed on a logistics disassembly cart, the positioning device being used to periodically collect the location information of the logistics disassembly cart; An onboard sensor assembly installed on a logistics dismantling trolley, the onboard sensor assembly being used to monitor the trolley's speed, direction, and acceleration in real time; An operation status monitoring device installed on a logistics disassembly trolley, the operation status monitoring device being used to record the timing and status changes of cargo loading and unloading, and door opening and closing. And a server, wherein the wireless communication device is connected to the server.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform one or more steps of the method of claim 8.

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