Automatic carrying method and system for intelligent stereoscopic warehouse

Abstract

The invention provides an automatic carrying method and system for an intelligent three-dimensional warehouse. The method comprises the steps that S10, data in the environment of the intelligent three-dimensional warehouse are collected in real time through a sensor; s11, motion state data of the robot are collected in real time through a sensor system in the robot; step S12, inputting the acquired environment data and the robot motion state data into a multi-dimensional disturbance dynamics model to obtain behavior data of each robot in a complex environment; and S13, constructing corresponding constraint conditions of the path optimization objective function, performing calculation by using a Lagrange multiplier method, and optimizing the running path of each robot through a dynamic planning strategy. According to the method, path planning and task scheduling are optimized through the multi-dimensional disturbance dynamics model, so that the automation level and the operation efficiency of the intelligent three-dimensional warehouse are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing and logistics, and in particular to an automatic handling method and system for an intelligent vertical warehouse. Background Art

[0002] With the continuous advancement of automation technology, intelligent vertical warehouses have been widely used in warehouse management, logistics distribution, and cargo handling. Intelligent vertical warehouse systems use automated equipment and robots to perform cargo storage, retrieval, and handling tasks, greatly improving the efficiency and accuracy of warehouse operations. However, as the logistics industry's demand for warehousing efficiency and flexibility continues to increase, traditional automated handling methods are no longer able to meet the complex demands of modern warehouse environments.

[0003] Specifically, intelligent warehouses often involve multiple robots operating simultaneously in a complex and dynamic environment, including dynamic obstacles, collaboration and conflicts between robots, and other factors. These factors require highly intelligent and adaptable path planning and task scheduling. Furthermore, the dynamic interactions between robots and tasks must be considered in real time to ensure flexible system response and efficient execution.

[0004] However, the existing solutions have the following shortcomings:

[0005] Existing path optimization algorithms often fail to fully consider the interaction between robots and tasks, which may lead to inefficiencies such as path conflicts and excessive energy consumption when executing tasks; existing scheduling algorithms for multi-robot collaborative operations have deficiencies and lack real-time task adjustment capabilities, and cannot ensure the timely processing of high-priority tasks; existing technologies do not adequately model the dynamic impact between robots and tasks, making it difficult to quickly adjust the path and task execution order according to the actual environment, resulting in poor responsiveness and adaptability of the system when facing complex tasks.

[0006] Therefore, how to achieve accurate and real-time path planning and task scheduling in a dynamic environment has become a key issue in improving the efficiency of the intelligent vertical warehouse automatic handling system. Summary of the Invention

[0007] The technical problem to be solved by the present invention is that the present invention provides an automatic handling method and system for an intelligent vertical warehouse, which optimizes path planning and task scheduling through a multi-dimensional perturbation dynamics model, thereby improving the automation level and operating efficiency of the intelligent vertical warehouse.

[0008] As one aspect of the present invention, there is provided an automatic handling method for an intelligent vertical warehouse, which comprises the following steps:

[0009] Step S10, using sensors to collect data in the smart warehouse environment in real time, including the location information of surrounding obstacles, shelves, other robots, and task targets;

[0010] Step S11, collecting the robot's position, speed, and acceleration motion state data in real time through the robot's internal sensor system;

[0011] Step S12, inputting the collected environmental data and robot motion state data into a multidimensional perturbation dynamics model to obtain the behavior data of each robot in a complex environment;

[0012] Step S13, construct the corresponding constraint conditions of the path optimization objective function, use the Lagrange multiplier method for calculation, optimize the operation path of each robot through the dynamic programming strategy, and control each robot to move along the optimized operation path to ensure that the robot avoids collision when performing the task and completes the task on time.

[0013] Preferably, the step S12 further includes:

[0014] The multi-dimensional perturbation dynamics model is used to obtain the acceleration of each robot at time t using the following formula:

[0015]

[0016] in, It's a robot R i The acceleration at time t represents the rate of change of the robot's motion, R i is the position vector of the ith robot at time t, which is composed of the position components on the three spatial coordinate axes (x i ,y i ,z i ) composition; F i (t) is the external control force acting on the i-th robot; is the interaction force between the i-th robot and the j-th robot, which represents the interaction between robots; M is the number of robots; is the disturbance force between the i-th robot and the k-th task, representing the impact of the task on the robot's action; K is the number of tasks.

[0017] Preferably, in step S12,

[0018] The interaction force between the robots is calculated using the following formula:

[0019]

[0020] in, It is an exponential decay function that controls the strength of the mutual influence between robots. The farther the distance, the smaller the influence. is a positive attenuation coefficient used to control the attenuation rate of the mutual influence between robots due to the distance between them, obtained through experiments;

[0021] The following formula is used to calculate the disturbance force of each task on the robot:

[0022]

[0023] Among them, α k is the priority coefficient of the kth task. The higher the task priority, the greater the impact on the robot. ik (t) is the relative position vector between the i-th robot and the target position of the k-th task completed at time t; μ k is the attenuation coefficient of the task's impact on the robot. The farther the distance between the task target and the robot, the smaller the impact. k is the urgency coefficient of the kth task.

[0024] Preferably, step S13 at least includes:

[0025] Construct the following path optimization objective function:

[0026]

[0027] Among them, E total is the path optimization objective function, which represents the total energy consumption; γ1, γ2, and γ3 are weighting coefficients, which control the contribution of acceleration, the relative position between robots, and the relative position between robots and tasks to the total energy, respectively; T is the total time period for calculating the path optimization; |r ij (t)| represents the minimum distance between two robots; |r ik (t)| is the relative position between the robot and the corresponding task;

[0028] Determine the following constraints for the path optimization objective function:

[0029] Safety distance constraint between robots: The minimum distance between each robot and other robots needs to maintain the minimum safety distance D between robots. min Above, avoid collision:

[0030]

[0031] Task completion constraints: Each task must be completed within the specified time:

[0032]

[0033] in, is the time when the kth task starts executing; is the duration required for the kth task; is the end time of the kth task.

[0034] Preferably, the step S13 further includes:

[0035] The following Lagrangian function is used to introduce Lagrangian multipliers λ1, λ2, and combined with the dynamic programming algorithm to solve the optimal path:

[0036]

[0037] in, is the total energy consumption function, which represents the energy consumed by the robot in the process of performing the handling task; λ1 and λ2 are Lagrange multipliers, which are used to adjust the weights of the collision constraint and the task completion time constraint respectively;

[0038] The optimal path solved by the dynamic programming strategy is used to optimize the path of each robot in each time step, and the robot's acceleration, speed and task sequence are gradually adjusted.

[0039] Accordingly, as another aspect of the present invention, an automatic handling system for an intelligent vertical warehouse is provided, comprising:

[0040] The environmental perception module is used to use sensors to collect real-time data in the intelligent warehouse environment, including the location information of surrounding obstacles, shelves, other robots, and mission targets;

[0041] The motion state monitoring module is used to collect the robot's position, speed and acceleration motion state data in real time through the robot's internal sensor system;

[0042] The dynamic model processing module is used to input the collected environmental data and robot motion state data into the multi-dimensional perturbation dynamic model to obtain the behavior data of each robot in a complex environment;

[0043] The path optimization module is used to construct the corresponding constraints of the path optimization objective function, use the Lagrange multiplier method for calculation, optimize the operation path of each robot through a dynamic programming strategy, and control each robot to move along the optimized operation path to ensure that the robot avoids collisions when performing tasks and completes the tasks on time.

[0044] Preferably, in the dynamic model processing module, it is further configured to use the multidimensional perturbation dynamic model to obtain the acceleration of each robot at time t through the following formula:

[0045]

[0046] in, It's a robot R i The acceleration at time t represents the rate of change of the robot's motion, R i is the position vector of the ith robot at time t, which is composed of the position components on the three spatial coordinate axes (x i ,y i ,z i ) composition; F i (t) is the external control force acting on the i-th robot; is the interaction force between the i-th robot and the j-th robot, which represents the interaction between robots; M is the number of robots; is the disturbance force between the i-th robot and the k-th task, representing the impact of the task on the robot's action; K is the number of tasks.

[0047] Preferably, in the dynamic model processing module, the interaction force between the robots Calculated using the following formula:

[0048]

[0049] in, It is an exponential decay function that controls the strength of the mutual influence between robots. The farther the distance, the smaller the influence. is a positive attenuation coefficient used to control the attenuation rate of the mutual influence between robots due to the distance between them, obtained through experiments;

[0050] The disturbance force of each task on the robot Calculated using the following formula:

[0051]

[0052] Among them, α k is the priority coefficient of the kth task. The higher the task priority, the greater the impact on the robot. ik (t) is the relative position vector between the i-th robot and the target position of the k-th task completed at time t; μ k is the attenuation coefficient of the task's impact on the robot. The farther the distance between the task target and the robot, the smaller the impact. k is the urgency coefficient of the kth task.

[0053] Preferably, the path optimization module further includes:

[0054] The objective function construction unit is used to construct the following path optimization objective function:

[0055]

[0056] in, total is the path optimization objective function, which represents the total energy consumption; γ1, γ2, and γ3 are weighting coefficients, which control the contribution of acceleration, the relative position between robots, and the relative position between robots and tasks to the total energy, respectively; T is the total time period for calculating the path optimization; |r ij (t)| represents the minimum distance between two robots; |r ik (t)| is the relative position between the robot and the corresponding task;

[0057] The constraint determination unit is used to determine the following constraint conditions of the path optimization objective function:

[0058] Safety distance constraint between robots: The minimum distance between each robot and other robots needs to maintain the minimum safety distance between robots min Above, avoid collision:

[0059]

[0060] Task completion constraints: Each task must be completed within the specified time:

[0061]

[0062] in, is the time when the kth task starts executing; is the duration required for the kth task; is the end time of the kth task.

[0063] Preferably, the path optimization module further includes:

[0064] The optimal path solving unit is used to introduce the Lagrangian multipliers λ1 and λ2 using the following Lagrangian function and combine it with the dynamic programming algorithm to solve the optimal path:

[0065]

[0066] in, is the total energy consumption function, which represents the energy consumed by the robot in the process of performing the handling task; λ1 and λ2 are Lagrange multipliers, which are used to adjust the weights of the collision constraint and the task completion time constraint respectively;

[0067] The path optimization processing unit is used to optimize the path of each robot in each time step according to the solved optimal path through a dynamic programming strategy, and gradually adjust the robot's acceleration, speed and task sequence.

[0068] The implementation of the present invention has the following beneficial effects:

[0069] The present invention provides an automatic handling method and system for intelligent vertical warehouses. By adopting a multidimensional perturbation dynamics model, considering the interaction between robots and tasks, and between robots, and introducing multiple perturbation forces to simulate the robot's motion characteristics in the environment, the system ensures that the robot can flexibly adjust its motion trajectory according to real-time environmental changes through precise calculation of relative position, speed, and task requirements, thereby improving the system's response speed and adaptability.

[0070] In this invention, the energy consumption of the robot during the handling process is minimized through the path optimization objective function, while fully considering the time limit constraints of the task and the collision constraints between robots. By combining the Lagrange multiplier method with dynamic programming, it is possible to optimize the path while ensuring efficient task execution and satisfying all constraints, thereby improving the automation level and efficiency of warehouse operations.

[0071] In the present invention, the introduction of the task priority coefficient enables the urgency and priority of the task to be fully considered during task execution. The robot can adjust its action strategy according to the task requirements, thereby ensuring that high-priority tasks are processed in a timely manner, thereby improving the flexibility and resource utilization of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, without inventive work, other drawings derived from these drawings still fall within the scope of the present invention.

[0073] Figure 1 A schematic diagram of the main process of an embodiment of an automatic handling method for an intelligent vertical warehouse provided by the present invention;

[0074] Figure 2 A schematic structural diagram of an embodiment of an automatic handling system for an intelligent vertical warehouse provided by the present invention;

[0075] Figure 3 for Figure 2 Schematic diagram of the structure of the path optimization module. DETAILED DESCRIPTION

[0076] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0077] like Figure 1FIG. 1 shows a schematic diagram of a main process of an embodiment of an automatic handling method for an intelligent vertical warehouse provided by the present invention, which includes the following steps:

[0078] Step S10, using sensors to collect data in the smart warehouse environment in real time, including the location information of surrounding obstacles, shelves, other robots, and task targets;

[0079] In a specific example, to ensure the accuracy of path planning and task scheduling for automated handling in smart warehouses, sensors are needed to collect real-time data about the surrounding warehouse environment. Using LiDAR, cameras, ultrasonic sensors, and other real-time position detection devices, robots can accurately perceive their physical environment, including the locations of obstacles, shelves, other robots, and task targets.

[0080] Step S11, collecting the robot's position, speed, and acceleration motion state data in real time through the robot's internal sensor system;

[0081] In a specific example, the sensor system inside the robot collects its own motion status data such as position, speed, acceleration, etc. in real time. The data collected by the sensors is combined with the intelligent warehouse management system to ensure that the robot can obtain dynamic information of the task in a timely manner, such as the task priority, urgency and required execution time.

[0082] Step S12, inputting the collected environmental data and robot motion state data into a multidimensional perturbation dynamics model to obtain the behavior data of each robot in a complex environment;

[0083] In this step, a multi-dimensional perturbation dynamics model is used to describe the behavior of the automatic handling robot in a complex environment. This includes calculating the interaction forces between the robots and the perturbation forces generated during task execution, and obtaining the robot's acceleration.

[0084] By fusing data from multiple sensors, a more accurate and comprehensive environmental model is obtained. The sensor data is then input into the perturbation dynamics model, becoming a key input for path generation and optimization, ensuring that the robot can make precise dynamic adjustments in complex environments.

[0085] It is understandable that in an intelligent vertical warehouse, multiple robots may operate simultaneously, and there may be path conflicts and dynamic obstacles. The behavior of automatic handling robots in complex environments is described by a multidimensional perturbation dynamics model. In the multidimensional perturbation dynamics model, the motion of each robot is affected by multiple factors, including external control forces, interaction forces with other robots, and perturbation forces generated during task execution.

[0086] The change in the robot's position over time is not only determined by its own control force, but also by the dynamic influence of other robots and tasks. In order to maximize the efficiency and safety of handling, the interaction between robots and between robots and tasks will be considered at the same time, and adaptive adjustments will be made to optimize the path and task execution sequence in real time.

[0087] In a specific example, the step S12 further includes:

[0088] The multi-dimensional perturbation dynamics model is used to obtain the acceleration of each robot at time t using the following formula:

[0089]

[0090] in, It's a robot R i The acceleration at time t represents the rate of change of the robot's motion, R i is the position vector of the ith robot at time t, which is composed of the position components on the three spatial coordinate axes (x i ,y i ,z i ) composition; F i (t) is the external control force acting on the i-th robot, such as the force caused by the target position and the environment change in the warehouse; is the interaction force between the i-th robot and the j-th robot, which represents the interaction between robots; M is the number of robots; is the perturbation force between the i-th robot and the k-th task, representing the impact of the task on the robot's behavior; K is the number of tasks. By introducing multiple perturbations (such as the interrobot forces and the impact of tasks) to simulate the robot's motion characteristics in complex environments, the robot's acceleration can respond to changes in external and environmental factors in real time.

[0091] In order to describe the interaction between robots, the interaction force is calculated by the relative position and relative velocity between robots; each robot R i With another robot R j The relative position in space is expressed by r ij (t)=(x i –x j ,y i –y j ,z i –z j ) is used to represent the relative displacement of the two robots in three-dimensional space; the relative speed v j (t)=(v i -v j ) describes the relative motion speed of the two robots at time t, vi It's a robot R i According to physical principles, the relative position and speed of robots determine the interaction force between them. Specifically, a distance decay function is introduced to effectively avoid invalid interference between robots at long distances, thereby achieving more accurate path and task scheduling.

[0092] The interaction force between the robots is calculated using the following formula:

[0093]

[0094] in, It is an exponential decay function that controls the strength of the mutual influence between robots. The farther the distance, the smaller the influence. is a positive attenuation coefficient used to control the attenuation rate of the distance between robots on the mutual influence, obtained through experiments; the relative position and speed between robots determine the interaction force between them. The closer the distance and the greater the relative speed, the stronger the influence.

[0095] The impact of a task on the robot can be calculated by the disturbance force, which is generated by the force exerted by the task on the robot and is mainly affected by the task target position, the priority of the task, the urgency of the task, and the relative distance between the task and the robot. Specifically, the higher the priority and urgency of the task, the stronger the robot's response to the task. The relative position and relative speed between the task and the robot will also affect the way the robot performs the task. The closer the distance and the more urgent the task, the stronger the driving force of the robot to perform the task. Therefore, the following formula is used to calculate the disturbance force of each task on the robot

[0096]

[0097] Among them, α R is the priority coefficient of the kth task. The higher the task priority, the greater the impact on the robot. It is set by the task management system according to the nature of the task. For example, the priority of a task may be based on customer requirements or the type of order (e.g., bulk orders are more important); r ik (t) is the relative position vector between the i-th robot and the target position (i.e., the transport target position) of the k-th task completed at time t; μ k is the attenuation coefficient of the task's impact on the robot. The farther the distance between the task target and the robot, the smaller the impact. It is set based on the actual physical requirements of the impact of the distance between the robot and the task on the task; ρ k It is the urgency coefficient of the kth task, which is dynamically calculated based on factors such as the remaining time of the task, the timeliness requirement of the task, and customer requirements.

[0098] Step S13, construct the corresponding constraint conditions of the path optimization objective function, minimize the total energy consumption of the robot, use the Lagrange multiplier method for calculation, optimize the operation path of each robot through the dynamic programming strategy, and control each robot to move along the optimized operation path to ensure that the robot avoids collision when performing the task and completes the task on time.

[0099] In this step, in order to optimize the robot's path in a complex dynamic environment, a path optimization objective function is constructed. The goal is to minimize the robot's energy consumption during the path generation process and take into account the interaction of multiple factors, including the robot's acceleration, the relative position between robots, and the relative position between the robot and the task. The influence of each factor is adjusted through different weighting coefficients to reflect the importance of different elements in path optimization. All factors are combined to form a comprehensive path optimization objective function, which makes it possible to find the most appropriate path while considering the dynamic environment and task constraints, thereby achieving efficient and safe automatic handling operations.

[0100] In a specific example, step S13 at least includes:

[0101] Step S130: Construct the following path optimization objective function:

[0102]

[0103] Among them, E total is the path optimization objective function, which represents the total energy consumption, covering the acceleration, collision risk and energy consumption during the task execution of the robot during movement; γ1, γ2, and γ3 are weighting coefficients, which control the contribution of acceleration, the relative position between robots, and the relative position between robots and tasks to the total energy respectively; T is the total time period for calculating the path optimization; |r ij (t)| represents the minimum distance between two robots; |r kl (t)| is the relative position between the robot and the corresponding task.

[0104] Step S131: determine the following constraints of the path optimization objective function:

[0105] Safety distance constraint between robots: The minimum distance between each robot and other robots needs to maintain the minimum safety distance D between robots. min Above, avoid collision:

[0106]

[0107] Task completion constraints: Each task must be completed within the specified time:

[0108]

[0109] in, is the time when the kth task starts executing; is the duration required for the kth task; is the end time of the kth task.

[0110] It can be understood that the above two constraints ensure that there will be no path conflicts when the robot performs tasks, while ensuring the timely completion of high-priority tasks.

[0111] Step S132: Use the following Lagrangian function to introduce Lagrangian multipliers λ1 and λ2, and combine with the dynamic programming algorithm to solve the optimal path:

[0112]

[0113] in, is the total energy consumption function, which represents the energy consumed by the robot in the process of performing the handling task; λ1 and λ2 are Lagrange multipliers, which are used to adjust the weights of the collision constraint and the task completion time constraint respectively.

[0114] It is understood that in the path optimization problem, the goal is to minimize total energy consumption while simultaneously satisfying collision constraints between robots and task completion time constraints. To introduce the constraints into the objective function, the Lagrange multiplier method is used to handle the constraints. The Lagrange multiplier method, by introducing multiplier variables, combines the constraints and the objective function into a single Lagrangian function, thereby transforming the constrained optimization problem into an unconstrained optimization problem. In the present invention, the constraints are handled by introducing the Lagrange multipliers λ1 and λ2, and the constrained optimization problem is ultimately solved using the aforementioned Lagrangian function.

[0115] Among them, in the Lagrangian function, λ1 and λ2 are the introduced Lagrangian multipliers, which adjust the weights of the collision constraint and the task completion time constraint respectively. ij (t)|-D min This ensures a safe distance between robots to prevent collisions. It ensures that the task can be completed within the specified time and that the execution time and priority of the task are met. By solving the Lagrangian function, the optimal path planning can be obtained while ensuring that all constraints are met.

[0116] In step S133, the path of each robot in each time step is optimized by using the optimal path solved by the dynamic programming strategy, and the acceleration, speed and task sequence of the robot are gradually adjusted. The dynamic programming strategy can refer to the dynamic programming method in the prior art and will not be elaborated on here.

[0117] like Figure 2 FIG. 1 shows a schematic diagram of the structure of an embodiment of an automatic handling system for an intelligent vertical warehouse provided by the present invention. Figure 3 As shown, in this embodiment, the automatic handling system 1 for the intelligent vertical warehouse includes at least:

[0118] The environment perception module 10 is used to use sensors to collect data in the intelligent warehouse environment in real time, including the location information of surrounding obstacles, shelves, other robots and task targets;

[0119] The motion state monitoring module 11 is used to collect the robot's position, speed and acceleration motion state data in real time through the robot's internal sensor system;

[0120] The dynamic model processing module 12 is used to input the collected environmental data and robot motion state data into the multi-dimensional perturbation dynamic model to obtain the behavior data of each robot in a complex environment;

[0121] The path optimization module 13 is used to construct the corresponding constraint conditions of the path optimization objective function, use the Lagrange multiplier method for calculation, optimize the operation path of each robot through a dynamic programming strategy, and control each robot to move along the optimized operation path to ensure that the robot avoids collisions when performing tasks and completes the tasks on time.

[0122] More specifically, in the dynamic model processing module 12, it is further configured to use the multi-dimensional perturbation dynamic model to obtain the acceleration of each robot at time t through the following formula:

[0123]

[0124] in, It's a robot R i The acceleration at time t represents the rate of change of the robot's motion, R i is the position vector of the ith robot at time t, which is composed of the position components on the three spatial coordinate axes (x i ,y i ,z i ) composition; F i (t) is the external control force acting on the i-th robot; is the interaction force between the i-th robot and the j-th robot, which represents the interaction between robots; M is the number of robots; is the disturbance force between the i-th robot and the k-th task, representing the impact of the task on the robot's action; K is the number of tasks.

[0125] Among them, the interaction force between the robots Calculated using the following formula:

[0126]

[0127] in, It is an exponential decay function that controls the strength of the mutual influence between robots. The farther the distance, the smaller the influence. is a positive attenuation coefficient used to control the attenuation rate of the mutual influence between robots due to the distance between them, obtained through experiments;

[0128] The disturbance force of each task on the robot Calculated using the following formula:

[0129]

[0130] Among them, α k is the priority coefficient of the kth task. The higher the task priority, the greater the impact on the robot. ik (t) is the relative position vector between the i-th robot and the target position of the k-th task completed at time t; μ k is the attenuation coefficient of the task's impact on the robot. The farther the distance between the task target and the robot, the smaller the impact. k is the urgency coefficient of the kth task.

[0131] like Figure 3 As shown, in a specific example, the path optimization module 13 further includes:

[0132] The objective function construction unit 130 is used to construct the following path optimization objective function:

[0133]

[0134] Among them, E total is the path optimization objective function, which represents the total energy consumption; γ1, γ2, and γ3 are weighting coefficients, which control the contribution of acceleration, the relative position between robots, and the relative position between robots and tasks to the total energy, respectively; T is the total time period for calculating the path optimization; |r j (t)| represents the minimum distance between two robots; |r ik (t)| is the relative position between the robot and the corresponding task;

[0135] The constraint condition determination unit 131 is used to determine the following constraint conditions of the path optimization objective function:

[0136] Safety distance constraint between robots: The minimum distance between each robot and other robots needs to maintain the minimum safety distance D between robots. min Above, avoid collision:

[0137]

[0138] Task completion constraints: Each task must be completed within the specified time:

[0139]

[0140] in, is the time when the kth task starts executing; is the duration required for the kth task; is the end time of the kth task;

[0141] The optimal path solving unit 132 is used to use the following Lagrangian function to introduce Lagrangian multipliers λ1, λ2 and combine it with the dynamic programming algorithm to solve the optimal path:

[0142]

[0143] in, is the total energy consumption function, which represents the energy consumed by the robot in the process of performing the handling task; λ1 and λ2 are Lagrange multipliers, which are used to adjust the weights of the collision constraint and the task completion time constraint respectively;

[0144] The path optimization processing unit 133 is used to optimize the path of each robot in each time step according to the solved optimal path through a dynamic programming strategy, and gradually adjust the acceleration, speed and task sequence of the robot.

[0145] For more details, please refer to and combine the above Figure 1 The description is not repeated here.

[0146] The implementation of the embodiments of the present invention has the following beneficial effects:

[0147] The present invention provides an automatic handling method and system for intelligent vertical warehouses. By adopting a multidimensional perturbation dynamics model, considering the interaction between robots and tasks, and between robots, and introducing multiple perturbation forces to simulate the robot's motion characteristics in the environment, the system ensures that the robot can flexibly adjust its motion trajectory according to real-time environmental changes through precise calculation of relative position, speed, and task requirements, thereby improving the system's response speed and adaptability.

[0148] In this invention, the energy consumption of the robot during the handling process is minimized through the path optimization objective function, while fully considering the time limit constraints of the task and the collision constraints between robots. By combining the Lagrange multiplier method with dynamic programming, it is possible to optimize the path while ensuring efficient task execution and satisfying all constraints, thereby improving the automation level and efficiency of warehouse operations.

[0149] In the present invention, the introduction of the task priority coefficient enables the urgency and priority of the task to be fully considered during task execution. The robot can adjust its action strategy according to the task requirements, thereby ensuring that high-priority tasks are processed in a timely manner, thereby improving the flexibility and resource utilization of the overall system.

[0150] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] The above disclosure is only a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. An automatic handling method for an intelligent vertical warehouse, characterized in that: The following steps are involved: Step S10, using sensors to collect data in the smart warehouse environment in real time, including the location information of surrounding obstacles, shelves, other robots, and task targets; Step S11, collecting the robot's motion state data in real time through the robot's internal sensor system, wherein the motion state data includes position, speed, and acceleration; Step S12, inputting the collected environmental data and robot motion state data into a multidimensional perturbation dynamics model to obtain the behavior data of each robot in a complex environment; Step S13: construct corresponding constraint conditions of the path optimization objective function, use the Lagrange multiplier method for calculation, optimize the operation path of each robot through a dynamic programming strategy, and control each robot to move along the optimized operation path.

2. The method according to claim 1, characterized in that The step S12 further comprises: The multi-dimensional perturbation dynamics model is used to obtain the acceleration of each robot at time t using the following formula: in, It's a robot R i The acceleration at time t represents the rate of change of the robot's motion, R i is the position vector of the ith robot at time t, which is composed of the position components on the three spatial coordinate axes (x i ,y i ,z i ) composition; F i (t) is the external control force acting on the i-th robot; is the interaction force between the i-th robot and the j-th robot, which represents the interaction between robots; M is the number of robots; is the disturbance force between the i-th robot and the k-th task, representing the impact of the task on the robot's action; K is the number of tasks.

3. The method according to claim 2, characterized in that In step S12, The interaction force between the robots is calculated using the following formula: in, It is an exponential decay function that controls the strength of the mutual influence between robots. The farther the distance, the smaller the influence. is a positive attenuation coefficient used to control the attenuation rate of the mutual influence between robots due to the distance between them, obtained through experiments; The following formula is used to calculate the disturbance force of each task on the robot: Among them, α k is the priority coefficient of the kth task. The higher the task priority, the greater the impact on the robot. ik (t) is the relative position vector between the i-th robot and the target position of the k-th task completed at time t; μ k is the attenuation coefficient of the task's impact on the robot. The farther the distance between the task target and the robot, the smaller the impact. k is the urgency coefficient of the kth task.

4. The method according to claim 3, characterized in that The step S13 at least includes: Construct the following path optimization objective function: Among them, E total is the path optimization objective function, which represents the total energy consumption; γ1, γ2, and γ3 are weighting coefficients, which control the contribution of acceleration, the relative position between robots, and the relative position between robots and tasks to the total energy, respectively; T is the total time period for calculating the path optimization; |r ij (t)| represents the minimum distance between two robots; |r ik (t)| is the relative position between the robot and the corresponding task; Determine the following constraints for the path optimization objective function: Safety distance constraint between robots: The minimum distance between each robot and other robots needs to maintain the minimum safety distance D between robots. min Above, avoid collision: |r ij (t)|≥D min , Task completion constraints: Each task must be completed within the specified time: in, is the time when the kth task starts executing; is the duration required for the kth task; is the end time of the kth task.

5. The method according to claim 4, wherein The step S13 further comprises: The following Lagrangian function is used to introduce Lagrangian multipliers λ1, λ2, and combined with the dynamic programming algorithm to solve the optimal path: in, is the total energy consumption function, which represents the energy consumed by the robot in the process of performing the handling task; λ1 and λ2 are Lagrange multipliers, which are used to adjust the weights of the collision constraint and the task completion time constraint respectively; The optimal path solved by the dynamic programming strategy is used to optimize the path of each robot in each time step, and the robot's acceleration, speed and task sequence are gradually adjusted.

6. An automatic handling system for intelligent vertical warehouse, characterized in that: include: The environmental perception module is used to use sensors to collect real-time data in the intelligent warehouse environment, including the location information of surrounding obstacles, shelves, other robots, and mission targets; The motion state monitoring module is used to collect the robot's position, speed and acceleration motion state data in real time through the robot's internal sensor system; The dynamic model processing module is used to input the collected environmental data and robot motion state data into the multi-dimensional perturbation dynamic model to obtain the behavior data of each robot in a complex environment; The path optimization module is used to construct the corresponding constraint conditions of the path optimization objective function, use the Lagrange multiplier method for calculation, optimize the operation path of each robot through a dynamic programming strategy, and control each robot to move along the optimized operation path.

7. The system according to claim 6, characterized in that In the dynamic model processing module, it is further used to use the multidimensional perturbation dynamic model to obtain the acceleration of each robot at time t through the following formula: in, It's a robot R i The acceleration at time t represents the rate of change of the robot's motion, R i is the position vector of the ith robot at time t, which is composed of the position components on the three spatial coordinate axes (x i ,y i ,z i ) composition; F i (t) is the external control force acting on the i-th robot; is the interaction force between the i-th robot and the j-th robot, which represents the interaction between robots; M is the number of robots; is the disturbance force between the i-th robot and the k-th task, representing the impact of the task on the robot's action; K is the number of tasks.

8. The system according to claim 7, characterized in that In the dynamic model processing module, the interaction force between the robots Calculated using the following formula: in, It is an exponential decay function that controls the strength of the mutual influence between robots. The farther the distance, the smaller the influence. is a positive attenuation coefficient used to control the attenuation rate of the mutual influence between robots due to the distance between them, obtained through experiments; The disturbance force of each task on the robot Calculated using the following formula: Among them, α k is the priority coefficient of the kth task. The higher the task priority, the greater the impact on the robot. ik (t) is the relative position vector between the i-th robot and the target position of the k-th task completed at time t; μ k is the attenuation coefficient of the task's impact on the robot. The farther the distance between the task target and the robot, the smaller the impact. k is the urgency coefficient of the kth task.

9. The system according to claim 8, characterized in that The path optimization module further includes: The objective function construction unit is used to construct the following path optimization objective function: Among them, E total is the path optimization objective function, which represents the total energy consumption; γ1, γ2, and γ3 are weighting coefficients, which control the contribution of acceleration, the relative position between robots, and the relative position between robots and tasks to the total energy, respectively; T is the total time period for calculating the path optimization; |r j (t)| represents the minimum distance between two robots; |r ik (t)| is the relative position between the robot and the corresponding task; The constraint determination unit is used to determine the following constraint conditions of the path optimization objective function: Safety distance constraint between robots: The minimum distance between each robot and other robots needs to maintain the minimum safety distance D between robots. min Above, avoid collision: |r ij (t)|≥D min , Task completion constraints: Each task must be completed within the specified time: in, is the time when the kth task starts executing; is the duration required for the kth task; is the end time of the kth task.

10. The system according to claim 9, characterized in that The path optimization module further includes: The optimal path solving unit is used to introduce the Lagrangian multipliers λ1 and λ2 using the following Lagrangian function and combine it with the dynamic programming algorithm to solve the optimal path: in, is the total energy consumption function, which represents the energy consumed by the robot in the process of performing the handling task; λ1 and λ2 are Lagrange multipliers, which are used to adjust the weights of the collision constraint and the task completion time constraint respectively; The path optimization processing unit is used to optimize the path of each robot in each time step according to the solved optimal path through a dynamic programming strategy, and gradually adjust the robot's acceleration, speed and task sequence.

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