Material carrying regulation and control method based on cooperation of mobile transport vehicle and floor elevator

Abstract

The invention discloses a material carrying regulation and control method based on cooperation of a mobile transport vehicle and a floor elevator, and relates to the technical field of intelligent logistics control, and the method comprises the steps: calculating the total completion time of pairing of each to-be-distributed task and each reachable elevator, generating an initial distribution scheme, and carrying out the matching of each to-be-distributed task and each reachable elevator; optimizing the allocation scheme by iteratively executing task exchange operation and evaluating the change of total completion time after exchange to obtain an optimal allocation scheme; and packaging the optimal allocation scheme into a navigation task instruction, issuing the navigation task instruction to the mobile transport vehicle, packaging the navigation task instruction into a material transport task reservation instruction, sending the material transport task reservation instruction to a corresponding elevator controller, cooperatively controlling the mobile transport vehicle and the elevator to complete cross-layer transport and unloading operation, and recovering an idle state after the task is completed. In the task distribution stage, the time for the mobile transport vehicle to arrive at the elevator interaction area, the current task queue of the elevator and the available time are comprehensively considered.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics control technology, and in particular to a material handling control method based on the collaboration between a mobile transport vehicle and a floor hoist. Background Technology

[0002] With the rapid development of intelligent manufacturing, smart logistics and large public buildings (such as hospitals, airports, data centers, and automated warehouses), the demand for high-frequency and automated material handling in multi-story spaces is becoming increasingly prominent. Unmanned material handling systems with automated mobile robots (AMRs) or mobile transport vehicles as the core have been widely used in production workshops, warehousing logistics and internal building logistics scenarios.

[0003] Existing technologies still have significant shortcomings in practical applications. At the task allocation level, most scheduling strategies adopt a simple "first-come, first-served" or fixed rule-based assignment method, which fails to fully consider the dynamic matching relationship between the AMR's arrival time at the elevator entrance and the elevator's own operating status (such as current position and task queue). This results in the AMR waiting at the elevator entrance for a long time or the elevator running empty, which seriously reduces the overall throughput and equipment utilization of the system. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a material handling control method based on the collaboration of mobile transport vehicles and floor hoists. This solves the problem that at the task allocation level, most scheduling strategies adopt simple "first-come, first-served" or fixed rule-based assignment methods, which fail to fully consider the dynamic matching relationship between the AMR's arrival time at the elevator entrance and the elevator's own operating status (such as current position and task queue). This results in the AMR waiting at the elevator entrance for a long time or the elevator running empty, which seriously reduces the overall throughput and equipment utilization of the system.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a material handling control method based on the coordination of a mobile transport vehicle and a floor hoist, comprising,

[0008] Collect and preprocess operational data and multi-source data, load floor maps to construct a global control coordinate system, calculate the position and pose of transport vehicles through point cloud registration algorithms, receive material handling instructions, parse the starting position information and target position information, and classify tasks into same-floor handling tasks or cross-floor handling tasks.

[0009] Based on same-layer transport tasks, and combined with pose planning for collision-free paths, the mobile transport vehicle arrives at the final unloading point to perform unloading operations. Based on cross-layer transport tasks, the total completion time of each task to be assigned and each reachable hoist is calculated to generate an initial allocation scheme. By iteratively executing task exchange operations and evaluating the change in total completion time after the exchange, the allocation scheme is optimized to obtain the optimal allocation scheme.

[0010] The optimal allocation scheme is encapsulated as a navigation task instruction and sent to the mobile transport vehicle, and encapsulated as a material transport task reservation instruction and sent to the corresponding hoist controller. The mobile transport vehicle and the hoist are coordinated to complete the cross-level transport and unloading operations. After the task is completed, the vehicle returns to the idle state.

[0011] As a preferred embodiment of the material handling control method based on the collaboration between a mobile transport vehicle and a floor hoist as described in this invention, the step of loading a floor map to construct a global control coordinate system and calculating the position and pose of the transport vehicle through a point cloud registration algorithm includes:

[0012] The calibrated and saved floor map is read from the local storage unit. The point cloud data is registered with the reference point cloud of the floor map using a point cloud registration algorithm. The current pose transformation matrix is ​​solved to obtain the pose of the mobile transport vehicle in the map coordinate system.

[0013] As a preferred embodiment of the material handling control method based on the collaboration of a mobile transport vehicle and a floor hoist as described in this invention, the step of receiving material handling instructions, parsing location information, and classifying tasks includes:

[0014] The central dispatch server receives material handling instructions, parses the instructions, and extracts the starting location information, target location information, and mobile transport vehicle task sequence.

[0015] The starting and target location information respectively includes starting and target floor identifiers and planar coordinates;

[0016] The starting floor is compared with the target floor. If the starting floor equals the target floor, it is determined to be a same-floor transport task. If the starting floor does not equal the target floor, it is determined to be a cross-floor transport task.

[0017] As a preferred embodiment of the material handling control method based on the collaboration between a mobile transport vehicle and a floor hoist as described in this invention, the step of performing an unloading operation by the mobile transport vehicle upon reaching the final unloading point, based on the same-floor transport task and combined with pose planning for a collision-free path, includes:

[0018] Based on same-floor handling tasks, the current floor load status is selected as follows: Mobile transport vehicles, among which It is in an unloaded state;

[0019] Select the shortest Euclidean distance between the mobile transport vehicle and the target location information in the planar coordinates as the vehicle to perform the task;

[0020] Based on the current pose of the mobile transport vehicle, the A* heuristic search algorithm is used to plan a collision-free path on the current floor grid map;

[0021] The mobile transport vehicle arrives at the final unloading point and performs the unloading operation through the mechanical interface.

[0022] As a preferred embodiment of the material handling control method based on the collaboration of mobile transport vehicles and floor hoists described in this invention, the following steps are taken: Based on cross-floor transport tasks, the total completion time of each task to be assigned paired with each reachable hoist is calculated to generate an initial allocation scheme. The allocation scheme is then optimized by iteratively executing task exchange operations and evaluating the change in total completion time after the exchange, resulting in an optimal allocation scheme.

[0023] Based on the cross-floor transport task, select the mobile transport vehicles whose task sequence is empty on the current floor, use the A* heuristic search algorithm to plan a collision-free path on the current floor grid map, and calculate the time for the mobile transport vehicle to reach the end of the path.

[0024] For each cross-level task and each hoist, calculate the base time and the hoist's vertical transport time;

[0025] Add the vertical transport time to the base time to get the total completion time;

[0026] Sort the results by total completion time in ascending order to obtain an ordered list;

[0027] Starting from the head of the ordered list, iterate through each pair in turn, repeating the iteration until all tasks are assigned, to obtain the initial assignment scheme;

[0028] The traversal includes skipping the current pairing and continuing to the next one if the task has been marked as assigned; skipping the current pairing if the current assigned count of the elevator is equal to the maximum concurrent capacity; and performing an assignment operation if the traversal conditions are not met.

[0029] Randomly select two tasks assigned to different hoists from the initial allocation scheme, swap the hoist assignments of the tasks, construct a temporary swap scheme, and calculate the total completion time of the temporary swap scheme;

[0030] The total completion time of the temporary exchange plan is compared with the current total completion time. If the total completion time of the temporary exchange plan is less than the current total completion time, the exchange is accepted and a new allocation plan is generated; otherwise, the exchange is rejected.

[0031] Set a maximum number of iterations, and stop iterating when the maximum number of iterations is reached to obtain the optimal allocation scheme.

[0032] As a preferred embodiment of the material handling control method based on the collaboration between a mobile transport vehicle and a floor hoist as described in this invention, the step of encapsulating the optimal allocation scheme into a navigation task instruction and sending it to the mobile transport vehicle, and encapsulating it into a material transport task reservation instruction and sending it to the corresponding hoist controller, and coordinating the control of the mobile transport vehicle and the hoist to complete cross-floor transport and unloading operations, includes:

[0033] The optimal allocation scheme is encapsulated as a navigation task instruction and sent to the on-board controller of the mobile transport vehicle via a wireless network. The arrival time of the destination of the optimal allocation scheme is encapsulated as a material transport task reservation instruction and sent to the controller of each hoist via an industrial communication protocol.

[0034] The mobile transport vehicle's onboard navigation takes the docking position specified in the interaction area as the endpoint, combines the pose and floor grid map, and uses the A* heuristic search algorithm to generate the driving path. The mobile transport vehicle stops at the docking position specified in the interaction area and sends a "ready" message, waiting to enter the hoist controller. The hoist controller sends a signal to the mobile transport vehicle to allow entry. The mobile transport vehicle drives to the preset parking position in the car, and the hoist moves the mobile transport vehicle to the target floor.

[0035] After the hoist reaches the target floor and opens the door, the mobile transport vehicle starts from the center point of the hoist's exit and ends at the coordinates of the final unloading point of the material. It uses the A* heuristic search algorithm to plan a collision-free path on the grid map of the target floor and executes the journey. Once the mobile transport vehicle reaches the final unloading point, it performs the unloading operation through the mechanical interface.

[0036] As a preferred embodiment of the material handling control method based on the coordination of a mobile transport vehicle and a floor hoist described in this invention, wherein: the restoration of the idle state after the task is completed includes:

[0037] After unloading is complete, update your status to: ;

[0038] After the mobile transport vehicle leaves and the car door is closed, the hoist returns to idle and standby status.

[0039] As a preferred embodiment of the material handling control method based on the collaboration of a mobile transport vehicle and a floor hoist as described in this invention, the step of collecting operational data and multi-source data and performing preprocessing includes:

[0040] The OPC UA client-server communication protocol is used to collect the operating data of the hoist. After the mobile transport vehicle is turned on, smart sensors are used to collect multi-source data of the mobile transport vehicle and perform preprocessing.

[0041] The intelligent sensors include 2D lidar, vehicle-mounted weighing, and wheel speed gauges;

[0042] The multi-source data includes point cloud data, analog voltage values, and velocity data;

[0043] The operational data includes the current floor, direction of travel, door status, and movement status;

[0044] The preprocessing includes unifying the timestamps of multi-source data, converting multi-source data to the vehicle center coordinate system through external parameter calibration, filtering out outliers from point cloud data, converting analog voltage quantities into load quality using linear calibration, and converting load quality into load state using a threshold discrimination method.

[0045] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in the first aspect of the present invention.

[0046] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in the first aspect of the present invention.

[0047] The beneficial effects of this invention are as follows: By modeling the horizontal travel time, loading and unloading time of AMRs and the vertical transport time of hoists in a unified time dimension, and combining it with an iterative optimization allocation method based on task exchange, the matching between cross-floor tasks and hoists is transformed from static assignment into an optimizable problem of minimizing time costs. Thus, without increasing the number of hardware, the scheduling accuracy and resource utilization balance in multi-AMR and multi-hoist collaborative operation scenarios are improved. The real-time pose information, load status, and path planning results of AMRs are used for collaborative decision-making with hoist operating status data, so that the task allocation results can reflect the actual operating capacity and current working conditions of the equipment. Compared with scheduling methods based solely on rules or historical experience, this invention enhances the adaptability of scheduling results to dynamic environmental changes and improves the timeliness stability and predictability of cross-floor material handling processes. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the material handling control method based on the collaboration between a mobile transport vehicle and a floor hoist in Example 1.

[0050] Figure 2 This is a schematic diagram illustrating the coordinated execution of cross-layer transport tasks in Example 1.

[0051] Figure 3 This is a diagram illustrating the collaborative scheduling architecture of the mobile transport vehicle and the floor hoist in Example 1. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0055] Example 1, referring to Figures 1 to 3 This is the first embodiment of the present invention, which provides a material handling control method based on the coordination of a mobile transport vehicle and a floor hoist, including the following steps:

[0056] S1. Collect and preprocess operational data and multi-source data, load floor map to build global control coordinate system, calculate the position and pose of transport vehicle through point cloud registration algorithm, receive material handling instructions, parse location information and classify tasks.

[0057] Specifically, the process involves collecting operational data and multi-source data and performing preprocessing, including:

[0058] The AMR vehicle communication module is scheduled to act as an OPC UA client, using the OPC UA (IEC 62541) client-server communication protocol to collect the hoist's operating data. After the mobile transport vehicle (AMR) is powered on, smart sensors are used to collect multi-source data from the mobile transport vehicle and perform preprocessing.

[0059] The intelligent sensors include 2D lidar, vehicle-mounted weighing, and wheel speed gauges;

[0060] The multi-source data includes point cloud data, analog voltage (collected by the vehicle-mounted weighing sensor), and speed data;

[0061] The operational data includes the current floor, direction of travel (enumeration values: up = 1, stationary = 0, down = -1), door status (enumeration values: fully open = 1, fully closed = 0, opening = 2, closing = 3), and motion status (enumeration values: acceleration = 1, constant speed = 2, deceleration = 3, stopped = 4).

[0062] The preprocessing includes unifying the timestamps of multi-source data, converting multi-source data from different installation locations to the vehicle center coordinate system through extrinsic parameter calibration, filtering out outliers from the point cloud data, converting analog voltage quantities to load quality using linear calibration, and converting load quality to load state using a threshold discrimination method. The formula is as follows:

[0063] ,

[0064] ,

[0065] in Here, k and b represent the load mass value, respectively, as the slope and intercept, both obtained from factory calibration. For voltage analog quantity, U represents the load state of the mobile transport vehicle. , as well as The states are no-load, partially-loaded, and fully-loaded. and The no-load and full-load thresholds were defined as follows: historical data were collected under no-load and full-load conditions of the AMR, respectively; the means were calculated using statistical analysis; the no-load threshold was defined as the no-load mean plus three times the standard deviation; the full-load threshold was defined as the full-load threshold minus three times the standard deviation. This was based on the principle of "30% of the load factor" in mathematical statistics. (Sigma) principle.

[0066] OPC UA possesses excellent cross-platform and cross-vendor capabilities, enabling the AMR system to seamlessly connect to hoist equipment of different models and manufacturers. This avoids system lock-in issues caused by traditional proprietary protocols, improves the scalability of the overall logistics system, and allows AMR to determine in advance whether the hoist is in an interactive state by acquiring the hoist's current floor, running direction, door status, and movement status in real time. The introduction of running status enumeration values ​​enables the scheduling system to predict waiting time based on the hoist's dynamic status, thereby reordering tasks in multi-vehicle collaborative scenarios and improving the overall system throughput.

[0067] Furthermore, a global control coordinate system is constructed by loading the floor map, and the vehicle's pose is calculated using a point cloud registration algorithm, including:

[0068] The vehicle controller reads the calibrated and saved floor map from the local storage unit. The floor map data includes the grid map of all floors, the coordinates of the vertices of the polygonal electronic fences in the interaction areas of each hoist, the coordinates of the center point of the high reflectivity mark on the ground, and the normal orientation vector. The map data is loaded into the SLAM working coordinate system of the vehicle positioning and navigation module, and it is established as the spatial reference coordinate system for all subsequent positioning, navigation and path planning operations, serving as the spatial reference for subsequent positioning and path planning.

[0069] Point cloud data is registered with the reference point cloud of the floor map using a point cloud registration algorithm (such as ICP) to achieve initial positioning with centimeter-level accuracy. The current pose transformation matrix is ​​then solved (this transformation minimizes the distance error between the transformed current point cloud and the reference point cloud) to obtain the pose of the mobile transport vehicle (AMR) in the map coordinate system.

[0070] The point cloud registration algorithm is executed iteratively until the root mean square error of the registration is lower than the preset stop threshold (for example, the stop threshold = 0.02m, which is determined according to the inherent accuracy of the floor map and the minimum accuracy requirement of the navigation control of the mobile transport vehicle. The point cloud collected each time is registered with the pre-stored reference map, and the final root mean square error after registration convergence is recorded. The mean of all final root mean square errors is calculated as the average accuracy of the map registration system. The average accuracy plus twice the standard deviation of the final root mean square error is set as the stop threshold). After the algorithm converges, the translation component and the rotation angle of the rotation matrix around the vertical axis are extracted from the current pose transformation matrix to obtain the pose of the mobile transport vehicle in the map coordinate system, including the position and heading angle.

[0071] The central dispatch server receives material handling instructions, parses the instructions, and extracts the starting position information, target position information, and mobile transport vehicle task sequence.

[0072] The starting and target location information respectively includes starting and target floor identifiers and planar coordinates;

[0073] The starting floor is compared with the target floor. If the starting floor equals the target floor, it is determined to be a same-floor transport task. If the starting floor does not equal the target floor, it is determined to be a cross-floor transport task.

[0074] By describing the grid map, electronic fence and high reflectivity markers of different floors in the same coordinate system, AMR can maintain consistent spatial cognition in cross-floor tasks, avoiding the system fragmentation problem of "relocalization upon floor switching". By using the stopping threshold as the convergence criterion, the credibility of the pose solution is ensured, providing a reliable starting point for subsequent long-distance navigation and cross-floor movement.

[0075] Furthermore, it receives material handling instructions, parses location information, and classifies tasks, including:

[0076] Based on same-floor handling tasks, the current floor load status is selected as follows: Mobile transport vehicles (AMRs), among which In an unloaded state, based on the principle of proximity, the vehicle with the shortest Euclidean distance between the mobile transport vehicle and the target location information is selected as the task execution vehicle. Based on the current pose of the mobile transport vehicle (with the current position of the AMR as the path start point and the target location information coordinates as the path end point), the A* heuristic search algorithm is used to plan a collision-free path on the current floor grid map. The mobile transport vehicle arrives at the final unloading point and performs the unloading operation through a mechanical interface (such as a lifting platform or roller conveyor).

[0077] Prioritize selecting empty AMRs that are closest to the target to reduce unnecessary empty driving distances, shorten task completion time, and avoid fully loaded vehicles participating in unnecessary scheduling by minimizing the path, effectively reducing energy consumption and extending equipment lifespan.

[0078] S2. Based on the same-layer transport task, combined with pose planning for a collision-free path, the mobile transport vehicle arrives at the final unloading point to perform unloading operations. Based on the cross-layer transport task, the total completion time of each task to be assigned and each reachable hoist is calculated to generate an initial allocation scheme. By iteratively executing task exchange operations and evaluating the change in total completion time after the exchange, the allocation scheme is optimized to obtain the optimal allocation scheme.

[0079] Specifically, based on the same-layer transport task and combined with pose planning for a collision-free path, the mobile transport vehicle arrives at the final unloading point to perform the unloading operation, including:

[0080] Based on the cross-floor transport task, select the mobile transport vehicles whose task sequence is empty on the current floor (after the hoist has completed its current floor task). (The current position of the AMR is the starting point of the path, and the coordinates of the entrance center point of the hoist interaction area on the current floor are the ending point of the path. The coordinates of the entrance center point of the hoist interaction area are provided by the floor map.) Use the A* heuristic search algorithm to plan a collision-free path on the current floor grid map and calculate the time for the mobile transport vehicle to reach the end of the path.

[0081] For each cross-layer task (corresponding to AMR) and each hoist, calculate the base time (representing the absolute waiting time caused by the asynchrony between the AMR and hoist readiness times, calculated as the absolute difference between the time the mobile transport vehicle arrives at the end of the path and the earliest available time after the hoist finishes processing the current queue), and calculate the time for the mobile transport vehicle to reach the planar coordinates in the target location information. The formula is:

[0082] ,

[0083] in Let e ​​be the time from the current position of the i-th AMR to the target point e (the planar coordinates in the target position information). Let e ​​be the total length of the planned path from the current position of the i-th AMR to the target point e. To determine the cruise speed of the AMR, the average speed during each task execution is calculated by integrating the pulse signals from its wheel speedometer. Based on the equivalent window length principle of the Exponentially Weighted Moving Average (EWMA) model, and considering the typical cycle of equipment performance evaluation in industrial environments, the queue length is set to 20. This queue length is equivalent to averaging the speed over approximately 20 past task cycles (typically covering several hours to a day of operation), allowing for adaptation to slow changes in equipment performance on a daily basis while ensuring stability. It is the total fixed time for material loading and unloading, including the loading time at the starting point and the unloading time at the ending point, which is determined by the median of the system's historical operating data.

[0084] The formula for calculating the vertical transport time of the hoist is:

[0085] ,

[0086] in The pure transportation time required for the j-th hoist to carry the i-th AMR from the source floor (current floor) to the target floor. and Here, H represents the target floor number and the source floor number for the task, and H is the fixed floor height of the building. To achieve the rated operating speed of the hoist, For a fixed single opening and closing time of the hoist, The fixed time required for the AMR to enter and exit the hoist car is obtained from actual testing.

[0087] Add the vertical transport time to the base time to get the total completion time (reflecting the total time cost caused by this allocation, including the waiting time due to the asynchronous movement of the mobile transport vehicle and the hoist + the time consumed by the vertical transport itself).

[0088] By using the A* heuristic search algorithm to plan paths on static or semi-static grid maps, obstacles, electronic fences, and high-risk areas can be explicitly avoided, reducing path oscillations or deadlocks caused by temporary obstacle avoidance. Fast closed-loop processing of tasks at the same level can effectively reduce the computational pressure on cross-level scheduling modules and improve the overall system response speed.

[0089] Furthermore, based on the cross-level transport task, the total completion time of each task to be assigned paired with each reachable hoist is calculated to generate an initial allocation scheme. The allocation scheme is then optimized by iteratively executing task exchange operations and evaluating the change in total completion time after the exchange, resulting in the optimal allocation scheme, including:

[0090] Calculate the total completion time of each cross-level task to be assigned and each elevator that can reach it, and sort them in ascending order of total completion time to obtain an ordered list;

[0091] Starting from the head of the ordered list, iterate through each pair in turn, repeating the iteration until all tasks are assigned, to obtain the initial assignment scheme;

[0092] The traversal includes skipping the current pairing and continuing to the next pairing if the task has been marked as "assigned". If the current assigned count of the hoist is equal to the maximum concurrent capacity of the hoist (the maximum concurrent capacity is determined by the AMR entry and exit capacity that the hoist can actually handle simultaneously, such as 1 or 2, which is obtained by field calibration), the current pairing is skipped. If the traversal conditions are not met (i.e., the task is not assigned and the hoist is not fully loaded), the assignment operation is performed: the task is assigned to the hoist, the task is marked as "assigned", and the assigned count of the hoist is updated.

[0093] Randomly select two tasks assigned to different elevators from the initial allocation scheme, swap the elevator assignments of the tasks, construct a temporary swap scheme, and calculate the total completion time of the temporary swap scheme (when calculating, it is only necessary to reacquire the vertical transport time of the interactive tasks under the newly assigned elevators, subtract their original vertical transport time from the total completion time and add the new vertical transport time).

[0094] The total completion time of the temporary exchange plan is compared with the current total completion time. If the total completion time of the temporary exchange plan is less than the current total completion time, the exchange is accepted and a new allocation plan is generated; otherwise, the exchange is rejected.

[0095] A fixed threshold method is used to set the maximum number of iterations (e.g., 500 times). The scheduling system's decision must be completed within a specified time window to ensure the material flow rhythm. The maximum allowable scheduling calculation time is set. On the server hardware used for deployment, benchmark tests are conducted on the core process of a single iteration of the optimization algorithm (including random selection of task pairs, calculation of new costs, and comparison and judgment). The average execution time is statistically analyzed. To ensure no timeout, only real-time performance is considered. The theoretical maximum number of iterations is set by dividing the maximum scheduling calculation time by the average execution time and rounding down. When the maximum number of iterations is reached, the iteration stops, and the optimal allocation scheme is obtained.

[0096] Traditional scheduling schemes often only consider transportation distance or floor difference, ignoring the implicit waiting time caused by timing mismatch between equipment. By explicitly considering the readiness difference between AMR and hoist, the resource waste problems of "AMR arriving too early but waiting for a long time" or "hoist idle but no vehicle available" can be avoided in the allocation stage. The cruising speed is not based on a fixed empirical value, but is obtained by statistical averaging of recent tasks through wheel speed gauges. It can dynamically reflect the real operating capacity of AMR under different loads and ground conditions, avoiding the use of "floor difference" as a rough indicator. Instead, it explicitly introduces building parameters and equipment performance, which is applicable to building environments with different floor heights and different travel characteristics. This greedy allocation strategy prioritizes the task with the lowest time cost—hoist pairing, providing a near-optimal starting point for subsequent optimization and reducing the iterative search space.

[0097] S3. Encapsulate the optimal allocation scheme as a navigation task instruction and send it to the mobile transport vehicle, and encapsulate it as a material transport task reservation instruction and send it to the corresponding hoist controller. Coordinate the control of the mobile transport vehicle and the hoist to complete the cross-level transport and unloading operations. After the task is completed, restore the idle state.

[0098] Specifically, the optimal allocation scheme is encapsulated as a navigation task instruction and sent to the mobile transport vehicle, and also encapsulated as a material transport task reservation instruction and sent to the corresponding hoist controller. This collaboratively controls the mobile transport vehicle and the hoist to complete cross-level transport and unloading operations, including:

[0099] The optimal allocation scheme is encapsulated into a navigation task instruction (which includes the target hoist identifier and the coordinates of the center point of the target interaction area) and sent to the on-board controller of the mobile transport vehicle via a wireless network (such as Wi-Fi 6 or 5G private network). The arrival time of the optimal allocation scheme is encapsulated into a material transport task reservation instruction (which includes the task sequence number, the identifier of the reserved AMR, the source floor number, the target floor number, the optimal allocation scheme, and the corresponding arrival time of the destination) and sent to the controller of each hoist via an industrial communication protocol (such as OPC UA or MQTT).

[0100] The mobile transport vehicle's onboard navigation takes the docking position (1m from the elevator door) designated in the interaction area as the endpoint. Combining the pose and the loaded floor grid map, it uses the A* heuristic search algorithm to generate a collision-free driving path. The mobile transport vehicle stops at the docking position designated in the interaction area, and uses 2D LiDAR point cloud registration to align its vehicle axis with the center line of the car entrance. It then sends a "ready in position, waiting to enter" signal to the elevator controller (via wireless communication). Upon receiving the "ready in position" signal from the mobile transport vehicle, the elevator controller sends a "allow entry" command to the mobile transport vehicle.

[0101] The method of generating a collision-free driving path using the A* heuristic search algorithm includes using a loaded floor grid map as the search environment. Each grid cell in the map is marked as "passable" (corresponding to ground, passageway) or "impassable" (corresponding to obstacles, walls, fixed equipment areas). The current coordinates of the AMR are converted into corresponding grid indices based on the map resolution, serving as the starting node. The target location (e.g., the center point 1 meter in front of the hoist door) is also mapped to a grid index, serving as the target node.

[0102] Create an open list and a closed list, add the starting node to the open list, and define an evaluation function value f(n) for each node, as follows:

[0103] f(n) = g(n) + h(n),

[0104] Where f(n) is the comprehensive evaluation value of node n, g(n) is the minimum cumulative path cost from the starting node to node n, and h(n) is the heuristic estimated cost from node n to the target node, calculated using Manhattan distance;

[0105] If the open list is empty, the path does not exist and the search fails.

[0106] Otherwise, select the node with the smallest f(n) from the open list as the current node. If the current node is the target node, the search is successful and the path backtracking phase begins.

[0107] Otherwise, move the current node into the closed list and traverse its 4 neighboring (up, down, left, right) adjacent grid nodes (Note: due to the use of Manhattan distance, diagonal movement is prohibited to ensure the acceptability of the heuristic function). If the grid is "impassable" or is already in the closed list, skip it.

[0108] Otherwise, calculate the cost of the new path from the starting node to the adjacent node via the current node. If the adjacent node with the new path cost is not in the open list, update its parent node to the current node; otherwise, add it to the open list.

[0109] Once the target node is visited, starting from the target node, trace back along the parent node pointer to the starting node to obtain a collision-free optimal path composed of grid indexes;

[0110] After receiving the "permission to enter" instruction, the mobile transport vehicle checks the status of the car door, the safety inside the car (no foreign objects), and whether the current location is the source floor. If the conditions are not met, the hoist controller can reject the reservation and notify the central dispatch server for reassignment. The mobile transport vehicle then drives to the preset parking position inside the car (the geometric center of the car). After receiving the "loading ready" signal, the hoist controller closes the car door and moves the mobile transport vehicle to the target floor.

[0111] After the elevator reaches the target floor and opens the door, the mobile transport vehicle starts from the center point of the elevator outlet and ends at the coordinates of the final unloading point of the material. It uses the A* heuristic search algorithm to plan a collision-free path on the grid map of the target floor and executes the journey. When the mobile transport vehicle reaches the final unloading point, it performs the unloading operation through a mechanical interface (such as a lifting platform or roller conveyor).

[0112] The central dispatch server only needs to issue targets and constraints without interfering with the underlying motion control of the AMR, making the system structure clear, easy to maintain and expand. By explicitly specifying the coordinates of the target hoist and its interaction area, path ambiguity or resource competition in multi-hoist scenarios is avoided, reducing scheduling conflicts caused by decision ambiguity. Hoists can plan their own running queues in advance based on the reservation time, reducing temporary scheduling and invalid waiting, and improving the utilization rate of vertical transportation resources. Through attitude alignment, the probability of side friction and jamming when the AMR enters the car can be significantly reduced, which is especially suitable for scenarios with compact space or large loads. When the conditions are not met, the hoist controller can refuse the reservation and notify the central dispatch server for reallocation, preventing the spread of error states in the system. After completing vertical transportation, the AMR can seamlessly connect to horizontal transportation without manual intervention, improving the automation level of the overall logistics link. The path is independently planned based on the target floor grid map, which can adapt to the differences in the layout of different floors and avoid navigation errors caused by cross-floor map misuse.

[0113] Furthermore, after the task is completed, the system returns to an idle state, including:

[0114] After unloading is completed, the mobile transport vehicle sends a "task completed" report to the central dispatch server. The report includes the task ID, completion timestamp, and updates its own status. ;

[0115] After the mobile transport vehicle departs and the car door closes, the hoist controller sends a "vertical transport task completed" notification to the central server, and the hoist status returns to "idle and ready".

[0116] The completion of the timestamp and task ID feedback enables the system to have a complete task lifecycle record, which facilitates operation analysis and fault tracing. Through a clear state regression mechanism, the system avoids devices from remaining in an "unknown state" or "occupied state" for a long time, reducing the risk of scheduling deadlock.

[0117] This embodiment also provides a computer device applicable to a material handling control method based on the collaboration of a mobile transport vehicle and a floor hoist, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the material handling control method based on the collaboration of a mobile transport vehicle and a floor hoist as proposed in the above embodiment.

[0118] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0119] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the material handling control method based on the coordination of a mobile transport vehicle and a floor hoist, as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0120] In summary, this invention models the horizontal travel time, loading and unloading time of AMRs, and vertical transport time of hoists using a unified time dimension. Combined with an iterative optimization allocation method based on task exchange, it transforms the matching of cross-floor tasks with hoists from static assignment to an optimizable problem of minimizing time costs. This improves scheduling accuracy and resource utilization balance in multi-AMR, multi-hoist collaborative operation scenarios without increasing the number of hardware components. By collaboratively deciding on AMR real-time pose information, load status, and path planning results with hoist operating status data, the task allocation results reflect the actual operating capacity and current working conditions of the equipment. Compared to scheduling methods based solely on rules or historical experience, this enhances the adaptability of scheduling results to dynamic environmental changes and improves the timeliness, stability, and predictability of cross-floor material handling processes.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A material handling control method based on the coordination of mobile transport vehicles and floor hoists, characterized in that: include, Collect and preprocess operational data and multi-source data, load floor maps to construct a global control coordinate system, calculate the position and pose of transport vehicles through point cloud registration algorithms, receive material handling instructions, parse location information, and classify tasks. Based on same-layer transport tasks, and combined with pose planning for collision-free paths, the mobile transport vehicle arrives at the final unloading point to perform unloading operations. Based on cross-layer transport tasks, the total completion time of each task to be assigned and each reachable hoist is calculated to generate an initial allocation scheme. By iteratively executing task exchange operations and evaluating the change in total completion time after the exchange, the allocation scheme is optimized to obtain the optimal allocation scheme. The optimal allocation scheme is encapsulated as a navigation task instruction and sent to the mobile transport vehicle, and encapsulated as a material transport task reservation instruction and sent to the corresponding hoist controller. The mobile transport vehicle and the hoist are coordinated to complete the cross-level transport and unloading operations. After the task is completed, the vehicle returns to the idle state.

2. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 1, characterized in that: The loaded floor map constructs a global control coordinate system, and the position pose of the transport vehicle is calculated using a point cloud registration algorithm, including: The calibrated and saved floor map is read from the local storage unit. The point cloud data is registered with the reference point cloud of the floor map using a point cloud registration algorithm. The current pose transformation matrix is ​​solved to obtain the pose of the mobile transport vehicle in the map coordinate system.

3. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 2, characterized in that: The process of receiving material handling instructions, parsing location information, and classifying tasks includes: The central dispatch server receives material handling instructions, parses the instructions, and extracts the starting location information, target location information, and mobile transport vehicle task sequence. The starting and target location information respectively includes starting and target floor identifiers and planar coordinates; The starting floor is compared with the target floor. If the starting floor equals the target floor, it is determined to be a same-floor transport task. If the starting floor does not equal the target floor, it is determined to be a cross-floor transport task.

4. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 3, characterized in that: The method based on same-layer transport tasks, combined with pose planning for collision-free path creation, and the mobile transport vehicle reaching the final unloading point to perform unloading operations includes: Based on same-floor handling tasks, the current floor load status is selected as follows: Mobile transport vehicles, among which It is in an unloaded state; Select the shortest Euclidean distance between the mobile transport vehicle and the target location information in the planar coordinates as the vehicle to perform the task; Based on the current pose of the mobile transport vehicle, the A* heuristic search algorithm is used to plan a collision-free path on the current floor grid map; The mobile transport vehicle arrives at the final unloading point and performs the unloading operation through the mechanical interface.

5. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 4, characterized in that: The process involves calculating the total completion time of each task to be assigned and paired with each reachable hoist based on cross-level transport tasks, generating an initial allocation scheme, and optimizing the allocation scheme by iteratively executing task exchange operations and evaluating the change in total completion time after the exchange to obtain the optimal allocation scheme, including: Based on the cross-floor transport task, select the mobile transport vehicles whose task sequence is empty on the current floor, use the A* heuristic search algorithm to plan a collision-free path on the current floor grid map, and calculate the time for the mobile transport vehicle to reach the end of the path. For each cross-level task and each hoist, calculate the base time and the hoist's vertical transport time; Add the vertical transport time to the base time to get the total completion time; Sort the results by total completion time in ascending order to obtain an ordered list; Starting from the head of the ordered list, iterate through each pair in turn, repeating the iteration until all tasks are assigned, to obtain the initial assignment scheme; The traversal includes skipping the current pairing and continuing to the next one if the task has been marked as assigned; skipping the current pairing if the current assigned count of the elevator is equal to the maximum concurrent capacity; and performing an assignment operation if the traversal conditions are not met. Randomly select two tasks assigned to different hoists from the initial allocation scheme, swap the hoist assignments of the tasks, construct a temporary swap scheme, and calculate the total completion time of the temporary swap scheme; The total completion time of the temporary exchange plan is compared with the current total completion time. If the total completion time of the temporary exchange plan is less than the current total completion time, the exchange is accepted and a new allocation plan is generated; otherwise, the exchange is rejected. Set a maximum number of iterations, and stop iterating when the maximum number of iterations is reached to obtain the optimal allocation scheme.

6. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 5, characterized in that: The process of encapsulating the optimal allocation scheme into a navigation task instruction and sending it to the mobile transport vehicle, and encapsulating it into a material transport task reservation instruction and sending it to the corresponding hoist controller, and coordinating the control of the mobile transport vehicle and the hoist to complete cross-level transport and unloading operations, includes: The optimal allocation scheme is encapsulated as a navigation task instruction and sent to the on-board controller of the mobile transport vehicle via a wireless network. The arrival time of the destination of the optimal allocation scheme is encapsulated as a material transport task reservation instruction and sent to the controller of each hoist via an industrial communication protocol. The mobile transport vehicle's onboard navigation takes the docking position specified in the interaction area as the endpoint, combines the pose and floor grid map, and uses the A* heuristic search algorithm to generate the driving path. The mobile transport vehicle stops at the docking position specified in the interaction area and sends a "ready" message, waiting to enter the hoist controller. The hoist controller sends a signal to the mobile transport vehicle to allow entry. The mobile transport vehicle drives to the preset parking position in the car, and the hoist moves the mobile transport vehicle to the target floor. After the hoist reaches the target floor and opens the door, the mobile transport vehicle starts from the center point of the hoist's exit and ends at the coordinates of the final unloading point of the material. It uses the A* heuristic search algorithm to plan a collision-free path on the grid map of the target floor and executes the journey. Once the mobile transport vehicle reaches the final unloading point, it performs the unloading operation through the mechanical interface.

7. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 6, characterized in that: The process of restoring the idle state after the task is completed includes: After unloading is complete, update your status to: ; After the mobile transport vehicle leaves and the car door is closed, the hoist returns to idle and standby status.

8. The material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in claim 1, characterized in that: The process of collecting operational data and multi-source data and preprocessing it includes: The OPC UA client-server communication protocol is used to collect the operating data of the hoist. After the mobile transport vehicle is turned on, smart sensors are used to collect multi-source data of the mobile transport vehicle and perform preprocessing. The intelligent sensors include 2D lidar, vehicle-mounted weighing, and wheel speed gauges; The multi-source data includes point cloud data, analog voltage values, and velocity data; The operational data includes the current floor, direction of travel, door status, and movement status; The preprocessing includes unifying the timestamps of multi-source data, converting multi-source data to the vehicle center coordinate system through external parameter calibration, filtering out outliers from point cloud data, converting analog voltage quantities into load quality using linear calibration, and converting load quality into load state using a threshold discrimination method.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the material handling control method based on the coordination of a mobile transport vehicle and a floor hoist as described in any one of claims 1 to 8.

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