Control method and system of intelligent security robot

Abstract

The invention relates to the field of intelligent robot control, and discloses an intelligent security robot control method comprising the following steps: building a robot motion and task model: building a dynamic relationship among the position, speed and acceleration of an intelligent security robot, and defining a multi-task target executed by the robot, wherein the execution time, the energy consumption and the priority of each task are expressed through a target function; the invention also discloses a control system of the intelligent security robot, and the system comprises a control decision module which is used for operating the multi-objective optimization function and carrying out the scheduling and path planning of multiple tasks executed by the robot, and an environment perception module which is used for obtaining the state information of the environment where the robot is located. According to the method, a dynamic optimization control strategy of the HJB equation is introduced, so that the robot can adjust paths and task scheduling in real time, dynamic environment changes are effectively coped with, and efficient and flexible task execution and real-time optimization in a complex environment are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent robot control, and specifically to a control method and system for an intelligent security robot. Background Art

[0002] With the progress of technology, intelligent robots are increasingly widely used in various industries; the application of robots has greatly improved efficiency and accuracy. With the diversification of task types and the complexity of the environment, the challenges faced by robots in performing tasks are also increasing continuously; the real environment is often dynamically changing, and robots need to make real-time adjustments under constantly changing task requirements and environmental conditions.

[0003] The existing technologies mainly rely on static path planning and task scheduling methods, which achieve the execution of robot tasks by presetting task sequences and paths. In some fixed environments, such technologies can effectively complete the specified tasks; however, in dynamic environments, the limitations of these solutions are fully exposed. Although some advanced optimization algorithms, such as Model Predictive Control (MPC), can optimize path selection by predicting future states, these methods mainly rely on static models and are not able to adapt to environmental changes in real time. The advantages of the existing technologies are that they can provide relatively stable performance, but they lack flexibility and real-time performance. Especially when facing dynamic changes and emergencies, their performance is usually not satisfactory.

[0004] The deficiencies of the existing technologies are mainly reflected in two aspects; firstly, the traditional path planning methods cannot effectively cope with the real-time changes of the environment. Robots often need to bypass sudden obstacles or adjust paths according to new task requirements during actual execution, but static control strategies usually cannot react in time, resulting in low task execution efficiency. Secondly, the existing multi-robot cooperation schemes often rely on predetermined tasks and paths and lack the ability of dynamic adjustment; when the task priorities change or the environmental conditions suddenly change, the adjustment ability of the existing technologies is limited, and path conflicts or unreasonable task allocations are likely to occur. These deficiencies directly affect the task completion quality and efficiency of the robots. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the present invention provides a control method and system for an intelligent security robot, which solves the problem that static path planning and task scheduling in the existing technologies cannot cope with dynamic environmental changes.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A control method for an intelligent security robot, comprising the following steps: Establish the robot motion and task model: Construct the dynamic relationship among the position, velocity, and acceleration of the intelligent security robot, and define the multi-task objectives executed by the robot. The execution time, energy consumption, and priority of each task are expressed through the objective function; Define the constraint conditions: According to the physical, energy, and path limitations faced by the robot when executing multi-task objectives, define the dynamic constraints, energy constraints, and path constraints; Construct the multi-objective optimization function: After defining the constraint conditions, combine the factors of the execution time, energy consumption, and smoothness of the control input of the multi-task objectives to construct a comprehensive optimization objective function; Through this function, optimize the task execution time and energy consumption; Apply the variational method to derive the optimal control strategy: Based on the multi-objective optimization function, use the variational method to derive the optimal control strategy; By solving the Lagrangian equation, determine the optimal control input and motion trajectory of the robot when executing tasks; Handle the constrained optimization problem: After obtaining the optimal control strategy, for the constrained optimization problem, apply the KKT conditions to solve the optimization problem containing inequality constraints; Use the HJB equation to optimize the control strategy in a dynamic environment: In a dynamic environment, use the HJB equation to describe the dynamic optimality of the optimal control strategy, and adjust the robot task scheduling and path planning according to environmental changes.

[0007] Preferably, the multi-task objectives include: Execute the area patrol task, and the robot needs to cover the specified area and return to the initial position within the specified time; Execute the abnormal behavior monitoring task, and the robot needs to collect video or image information during the patrol and analyze in real time whether there are abnormal events; Execute the emergency response task. When receiving external alarm information, the robot needs to interrupt the current task and go to the specified position within the specified time to handle the emergency; Each task is scheduled according to the preset priority. The task with a higher priority is executed first in case of resource conflicts, and there are correlations and dependencies among tasks.

[0008] Preferably, the dynamic constraints include: The change of the robot's position over time is limited by its maximum speed and maximum acceleration; The change of the robot's speed should meet the smoothness requirements; There is a physical coupling relationship between the control input and the state variables; During the path planning process, it is necessary to ensure that the robot can operate stably.

[0009] Preferably, the energy constraints include: The total energy consumption of the robot during task execution does not exceed the preset maximum energy threshold; The energy budget for each task is allocated by the system during task scheduling; During task execution, the energy consumption is monitored in real time. If it is estimated that the energy upper limit will be exceeded, compensation is made by adjusting the task order or aborting low-priority tasks; The energy constraint is dynamically coupled with the robot's motion control strategy, enabling the control method to dynamically adjust the task execution plan based on the remaining energy.

[0010] Preferably, the path constraint includes: The motion path of the robot needs to be kept within the preset spatial boundary and must not cross the boundary or enter the restricted area; In complex scenarios, obstacles need to be avoided. The obstacle information can be obtained through sensors and fed back to the path planning module in real time; The path constraint requires the robot to maintain a set safety distance when performing tasks in a specific area to prevent collisions with people or objects; When performing multiple tasks, the path planning needs to simultaneously meet the access order and location requirements of multiple task points.

[0011] Preferably, the optimization objective function includes: The sum of task execution times, which is used to measure the efficiency of completing multiple tasks within a specified period; The sum of task energy consumption, which is used to evaluate the overall energy utilization and serve as the basis for energy consumption optimization; The smoothness index of the control input, which is used to constrain the acceleration or jerk change of the robot; The above objectives are weighted and combined with weight coefficients to form a comprehensive optimization objective function. The optimal solution of multi-task scheduling and path control is obtained by optimizing this function.

[0012] Preferably, the variational method includes: By introducing the Lagrangian function, the optimization objective and various constraint conditions are combined to form a unified optimization model; The Euler-Lagrange equation is used to perform variational differentiation on the control input and state variables to obtain the necessary conditions satisfied by the optimal solution; The change trajectory of the control variable is obtained by solving the boundary value problem; Combined with the task objective and constraint conditions, the variational method provides an analytical or numerical solution method for obtaining the optimal control strategy.

[0013] Preferably, the KKT conditions include: When there are inequality constraints, equivalent optimization condition expressions are constructed by introducing Lagrange multipliers; The KKT conditions include four aspects: primal feasibility, Lagrangian differentiability, complementary slackness, and gradient optimality; By solving the KKT system, the optimal control input and state trajectory that satisfy all the constraint conditions can be obtained; This method is applicable to dealing with multiple simultaneously existing inequality constraints.

[0014] Preferably, the HJB equation includes: Based on the principle of dynamic programming, establish the expression of the minimum cost function of the system at each state; By solving the HJB equation, obtain the optimal feedback form of the control strategy varying with time, and achieve adaptive control in a dynamic environment; This method takes into account environmental uncertainty and the real-time nature of state transitions, enabling the robot to optimize the path and task allocation in real time during execution; Combined with the input of actual sensing data, the HJB equation is used to correct the optimal control strategy in real time to adapt to the impact brought by environmental changes.

[0015] The present invention also provides a control system for an intelligent security robot, including: Control decision-making module: used to run a multi-objective optimization function, schedule and path-plan the multiple tasks executed by the robot, and generate optimal control instructions on the premise of satisfying dynamic, energy, and path constraints; the control decision-making module performs task rearrangement and optimal selection in case of task conflicts or resource shortages based on task priorities and resource constraints; Environmental perception module: used to obtain the state information of the environment where the robot is located, and the state information includes obstacle positions, boundary regions, and environmental change information; this module is composed of a visual camera, a lidar, an ultrasonic sensor, and an infrared sensor, and provides continuous and accurate environmental modeling results through a data fusion algorithm; Task management module: used to receive task instructions, manage the task queue, allocate task priorities, and cooperate with the control decision-making module in real time to update the task execution status; support the access and response of external alarm, patrol instructions, and image recognition tasks; Motion execution module: used to drive each execution component of the robot to complete motion instructions according to the optimal control strategy generated by the control decision-making module, and the motion instructions include chassis driving, attitude adjustment, and obstacle avoidance actions; this module can also perform feedback monitoring on the executed actions; Energy management module: used to monitor the energy consumption status of the robot's power system in real time, evaluate the energy consumption of task execution, and issue an energy warning when the energy is insufficient; support task adjustment and scheduling reconstruction based on the energy status to ensure the continuity of the robot's task execution; Communication interaction module: used to implement data communication between the system and external servers and operation terminals; supports wired and wireless communication methods, including communication protocols such as Wi-Fi, Bluetooth, and ZigBee, for task distribution, data feedback, and system status synchronization. Dynamic policy update module: used to correct the control policy in real time according to the feedback results of the HJB equation when the environment changes or the task status changes, so as to realize the adaptive optimization of the control system.

[0016] The present invention provides a control method and system for an intelligent security robot. It has the following beneficial effects: 1. The present invention uses the HJB equation to optimize the control policy in a dynamic environment. By adjusting the robot path and task scheduling in real time, it achieves the technical effects of dynamic adaptation and optimal control in a complex dynamic environment. Compared with the static path planning scheme in the prior art, the present invention solves the problem that the robot cannot respond flexibly under sudden environmental changes, and improves the efficiency and accuracy of task execution.

[0017] 2. The present invention uses the HJB equation to describe the dynamic optimality of the robot control policy, enabling the robot to continuously adjust its action path according to real-time environmental changes. Compared with the method of fixed task execution sequence in the prior art, the present invention can handle dynamic obstacles and environmental changes more precisely, and realizes efficient dynamic path planning and task scheduling.

[0018] 3. Through the solution proposed by the present invention, the robot can achieve adaptive optimization control when facing changing tasks and environments. Compared with the single control model in the prior art, the present invention effectively solves the deficiency that the control policy is difficult to cope with diverse tasks in a complex environment, and provides a more flexible control solution.

[0019] 4. The present invention adopts a dynamic optimization method based on the HJB equation to perform real-time optimization for multi-robot cooperation tasks, greatly improving the cooperation efficiency. Compared with the static optimization strategy in the prior art, the present invention solves the problems of path conflict and uneven resource allocation in multi-robot cooperation, ensuring the smooth completion of tasks. Description of the Drawings

[0020] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the system construction of the present invention. Detailed Embodiments

[0021] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to the attached Figure 1 , the embodiment of the present invention provides a control method for an intelligent security robot, including the following steps: S1. Establish a robot motion and task model: Construct the dynamic relationship between the position, speed, and acceleration of the intelligent security robot, and define the multi-task objectives executed by the robot, where the execution time, energy consumption, and priority of each task are expressed through the objective function; By establishing the motion and task model, the present invention effectively combines the motion state and task execution state of the intelligent security robot. Specifically, when the robot executes multiple tasks, the dynamic relationship between its position, speed, and acceleration will affect the task execution efficiency and resource consumption. At the same time, through the optimization of the objective function, the execution time, energy consumption, and priority of the task are taken into account to achieve the overall optimization of the system. The following is a detailed description and technical implementation method.

[0023] In this embodiment, the motion model of the intelligent security robot is based on the classical dynamic equation. The state variables of the robot mainly include the position , speed , and acceleration , where the time is the independent variable. Specifically, assuming that the robot moves in a two-dimensional plane, its motion model can be expressed by the following equations: ; ; Among them, is the position of the robot, is the speed of the robot, is the acceleration of the robot, and are the derivatives of the position and speed with respect to time, respectively, representing the change rates of speed and acceleration.

[0024] To describe the constraints of the robot motion, considering the maximum speed and acceleration limitations during the robot operation, the following constraint conditions need to be added to the model: ; ; Among them, is the maximum speed of the robot, is the speed of the robot, is the acceleration of the robot, is the maximum acceleration of the robot.

[0025] These constraints ensure that the robot does not exceed its physical capabilities when performing tasks, avoiding overload or unstable performance.

[0026] During the task execution process, the intelligent security robot needs to execute multiple tasks, each with different requirements, including task execution time, energy consumption, and priority, etc. To effectively manage these tasks, the present invention defines a comprehensive objective function that optimizes the execution effects of multiple tasks.

[0027] In a possible implementation, the task objective function can be expressed in the following form: ; where, is the task objective function, is the total number of tasks, is the execution time of the th task, is the energy consumption of the th task, is the priority of the th task. Tasks with higher priorities have greater weights, are the weight coefficients of the task execution time and energy consumption, reflecting the time and energy requirements of the task; is the adjustment coefficient of the priority, used to adjust the influence of the task priority on the objective function.

[0028] In this formula, the task execution time and energy consumption reflect the resource consumption of the robot for each task, while the task priority is used to represent the urgency of the task. To balance the execution of each task, the optimization algorithm will plan the task execution order and resource allocation according to this objective function.

[0029] During the actual task execution process, factors such as the speed and acceleration of the robot, as well as the task execution time and energy consumption, are mutually influential. Especially under limited resource conditions, how to simultaneously meet the task objectives and motion constraints through reasonable scheduling and control strategies is one of the key issues in the present invention.

[0030] Specifically, in this embodiment, when the robot executes each task, it determines whether the task requirements are met according to the current dynamic state (including position, speed, and acceleration). For example, when the robot needs to complete a certain task in a short time, it may need to increase the speed or adjust the path, thereby affecting the acceleration and energy consumption. Therefore, the optimization of task execution is not only a simple superposition of time and energy, but also involves the coordinated scheduling between tasks and movements.

[0031] To this end, the control system adjusts the task execution strategy according to the following relational expressions: ; where, is the start time of the task, is the end time of the task, is the th energy consumption of the task, is the execution time of task , is the speed that changes during the task execution of the robot, is the mass of the robot, is the acceleration of the robot.

[0032] These formulas calculate the execution time and energy consumption of the task by integrating the speed and acceleration during the task execution. During the task execution, the control system dynamically adjusts the movement strategy of the robot according to factors such as the priority of the task and resource consumption to minimize the overall objective function.

[0033] The collaborative optimization of task scheduling and motion control is the key to achieving multi-task execution. By optimizing the execution time, energy consumption, and priority of each task, the system can efficiently allocate the task execution order and resources. During the process of the robot executing tasks, the control system adjusts the movement path of the robot according to the environmental feedback, so as to maximize the task completion degree and avoid excessive energy consumption at the same time.

[0034] In some embodiments, if the robot encounters an obstacle or needs to adjust the path when executing a high-priority task, the control system will recalculate the path and adjust the speed and acceleration of the robot according to the actual requirements of the task to ensure that the task can be completed on time and efficiently.

[0035] S2. Define constraint conditions: Define dynamic constraints, energy constraints, and path constraints according to the physical, energy, and path limitations faced by the robot when executing multi-task objectives; When an intelligent security robot performs multiple tasks, it needs to follow certain physical, energy, and path constraints. These constraints not only ensure the motion stability of the robot but also effectively avoid performance degradation or task failure caused by overloading. To systematically manage these constraints, dynamic constraints, energy constraints, and path constraints are defined respectively in this embodiment. The design of these constraints is to ensure that the robot can efficiently and safely complete the predetermined tasks in a complex environment while coordinating with the aforementioned motion model and multi-task objectives.

[0036] During the task execution process, the intelligent security robot must meet its own dynamic capabilities. Dynamic constraints mainly cover the maximum speed and maximum acceleration of the robot because the robot's motion is limited by physical properties such as the thrust of the motor and the stability of the chassis. To avoid control failure or collision caused by exceeding dynamic limits, these constraints must be set.

[0037] In this embodiment, it is assumed that the maximum speed of the robot is and its maximum acceleration is , then the following dynamic constraints exist: ; ; where is the speed of the robot and is the acceleration of the robot. To ensure that the robot can perform tasks within a reasonable motion range, the speed and acceleration must always be within the specified maximum value range.

[0038] Generally, the robot needs to respond quickly in multiple tasks. If the task requires a high response speed, the control system will adjust the motion trajectory according to this constraint to ensure that the task is completed on time without violating physical limitations.

[0039] Energy constraints are mainly used to limit the energy consumed by the robot during task execution. Since the energy source of the robot is limited (such as a battery), the control system needs to constantly monitor the remaining battery power to avoid being unable to complete the task due to insufficient energy midway. To calculate the energy consumption, the speed, acceleration, and specific requirements of task execution of the robot need to be considered.

[0040] In this embodiment, it is assumed that the energy consumption of the robot has a certain relationship with its speed and acceleration. That is, within a unit time, the energy consumption of the robot when performing a certain task can be expressed as: ; where is the energy consumption, is the mass of the robot, is the speed of the robot, is the acceleration of the robot, is the correlation coefficient between speed and energy consumption. To ensure that the robot can complete the task, the control system needs to keep the energy consumption of the robot within the range that the battery can bear.

[0041] In some embodiments, when the energy consumption of the robot is too fast during the task execution, the system will activate the energy management module to reduce the energy consumption by adjusting the task priority or re-planning the path, ensuring that high-priority tasks can be completed smoothly.

[0042] Path constraints refer to the geographical and environmental restrictions that the robot must follow when performing tasks. During the movement of the robot, it not only needs to avoid collisions with obstacles, but also needs to select the optimal path according to the task requirements. In some cases, the robot may need to bypass certain obstacles or move within a specific area, so path constraints need to be set to ensure safety and efficiency.

[0043] In this embodiment, the path constraints of the robot can be described in the following form: ; where, is the position of the robot, is the set of feasible regions of the task execution area. Specifically, the robot must always stay within the specified area and cannot enter the prohibited area during the movement.

[0044] During the path planning process, if a dynamic obstacle is encountered, the control system will correct the path in real time to ensure that the robot avoids collisions or entering dangerous areas while maintaining the task completion rate. The control system can also ensure that the robot can cope with emergencies, such as changing the path or pausing the task, by making real-time dynamic adjustments to the path until the obstacle is avoided or eliminated.

[0045] In practical applications, when the robot performs tasks, the dynamic constraints, energy constraints, and path constraints do not exist in isolation, but are interrelated and interact with each other. For example, in the case of high-speed movement, the robot may need to consume more energy, and the path selection may be subject to more stringent restrictions. The control system needs to comprehensively consider these constraints and adjust the speed, acceleration, and task execution order of the robot to ensure that the task can be completed efficiently and safely.

[0046] In a possible implementation, when the robot executes a task, the control system will first evaluate the time requirements, energy consumption, and path limitations of the current task, and perform task scheduling based on this information. During the task execution, the robot adjusts the path, speed, and acceleration according to the real-time environmental feedback to ensure that it always meets the dynamic and energy constraints, while avoiding entering the restricted area or colliding with obstacles.

[0047] S3. Construct a multi-objective optimization function: After defining the constraint conditions, a comprehensive optimization objective function is constructed by combining factors such as the execution time, energy consumption, and control input smoothness of multi-task objectives; through this function, the task execution time and energy consumption are optimized. During the execution of multi-tasks, the intelligent security robot needs to comprehensively consider multiple objectives, such as factors like task execution time, energy consumption, and control input smoothness, to achieve efficient task execution and optimize the overall performance. Therefore, in order to be able to handle these multiple objectives simultaneously, in this embodiment, a comprehensive optimization objective function is constructed to integrate the task execution time, energy consumption, and the smoothness of the control input within a unified framework. Through this optimization objective function, the robot can optimize the consumption of time and energy simultaneously during task execution, while ensuring the smoothness of the control input, so as to improve the stability and efficiency of task execution.

[0048] Task execution time: The task execution time refers to the time required for the robot to complete a specified task. This time needs to be as short as possible to improve the efficiency of the robot in completing tasks.

[0049] Energy consumption: Energy consumption refers to the energy consumed by the robot when executing tasks. Optimizing energy consumption is an important goal to ensure that the robot can continue to work and complete tasks under limited energy conditions.

[0050] Smoothness of control input: The smoothness of control input ensures that the movement of the robot during task execution does not have sudden changes or excessive fluctuations, thus avoiding system instability or excessive energy consumption caused by violent movements.

[0051] After defining the dynamic constraints, energy constraints, and path constraints as described above, in this embodiment, an optimization objective function is constructed by comprehensively considering the task execution time, energy consumption, and smoothness of control input. This optimization objective function realizes the unified optimization of multiple objectives through weighted combination of these factors.

[0052] In this embodiment, the optimization objective function can be expressed in the following form: ; where is the execution time of task , is the energy consumption, is the acceleration of the robot, is the acceleration at the previous moment, is the optimization objective function, is the start time of the task, is the end time of the task, , and are the weight coefficients of time, energy, and control smoothness in the comprehensive objective, respectively adjusting the relative importance of each index in the objective function.

[0053] Execution time is the objective that the robot needs to minimize as much as possible when performing tasks. By reducing the execution time of tasks, the robot's ability to complete more tasks within a limited time can be improved, enhancing its execution efficiency. During the optimization process, the robot will adjust control parameters such as speed and acceleration to minimize the task execution time as much as possible.

[0054] Generally, the robot needs to complete tasks within the physical limits of speed and acceleration during execution. Therefore, in the optimization objective function, a relatively high weight is assigned to the execution time term , so that the task completion time is as short as possible to achieve the goal of efficient execution.

[0055] Energy consumption is another important indicator when the robot performs tasks. In the case of limited energy, the robot must optimize the use of energy to avoid being unable to complete the task midway due to excessive consumption. Energy consumption is closely related to factors such as the acceleration and speed of the robot during task execution. Therefore, the control system needs to reasonably allocate motion resources to avoid high-energy consumption states.

[0056] In this embodiment, by controlling the acceleration and speed of the robot during task execution, the energy consumption is minimized as much as possible while ensuring the stability of task execution. The energy consumption term The weight in the objective function is adjusted according to the nature of the task. If the task has high energy requirements, the weight of energy consumption will be larger, thus prompting the robot to pay more attention to energy conservation during execution.

[0057] Control input smoothness refers to whether the changes in the robot's motion parameters (such as speed and acceleration) are stable during task execution. To avoid instability or additional energy consumption caused by sudden control inputs, a control input smoothness term is introduced in this embodiment as part of the optimization objective function.

[0058] Control input smoothness is measured by integrating the square of the acceleration change. Specifically, the smoothness term calculates the degree of acceleration fluctuation of the robot during task execution. Larger acceleration fluctuations may cause the robot to generate large impact forces or vibrations during motion, thereby affecting the stability of the system and energy consumption.

[0059] During the optimization process, the control system tends to select smooth control inputs, making the acceleration change more gently and avoiding drastic velocity and acceleration fluctuations. By reasonably adjusting the weight of the smoothness term , the robot can maintain a relatively stable motion during task execution, avoiding unnecessary vibrations or control mutations.

[0060] By constructing the above comprehensive optimization objective function, the control system can flexibly adjust the balance among time, energy, and control smoothness according to the actual requirements of the current task during task execution. For example, in some tasks, the execution time may be the most prioritized objective, while in other tasks, energy consumption may be more important. By adjusting , and 's weight coefficients, the system can dynamically adjust the focus of the optimization objective according to the priorities of different tasks and environmental conditions.

[0061] S4. Derive the optimal control strategy using the variational method: Based on the multi-objective optimization function, use the variational method to derive the optimal control strategy; by solving the Lagrangian equation, determine the optimal control input and motion trajectory of the robot during task execution; In the previous steps, the task execution time, energy consumption, and control input smoothness have been integrated into a unified framework through the construction of the comprehensive optimization objective function. However, to further determine the optimal control input and motion trajectory of the intelligent security robot during multi-task execution, it is necessary to optimize and solve the constructed optimization objective function. Generally, the optimal control strategy that meets the optimization objective can be effectively derived by applying the variational method. In this case, by combining the established comprehensive optimization objective function and using the variational method to solve the Lagrangian equation, the optimal control strategy that meets the dynamic, energy, and path constraint conditions can be obtained.

[0062] In this embodiment, the variational method is used to derive the optimal control strategy of the intelligent security robot. Specifically, by performing variational processing on the multi-objective optimization function, the corresponding Lagrangian equation is obtained, and the optimal control input and motion trajectory are determined therefrom.

[0063] In a possible implementation, to better describe the optimization process, the state variable is defined as , the control input is , and the objective function can be expressed as a functional form of a certain control input and the state variable .

[0064] Based on the aforementioned comprehensive optimization objective function, the process of solving using the variational method includes the following steps: First, construct the Lagrangian function , which includes the objective function and system dynamics constraints. Specifically, it takes the following form: ; where is the Lagrangian function, is the optimization objective function, is the Lagrange multiplier, representing the adjoint variable of the system state, represents the time derivative of the system state, is the state change equation of the system, which describes the motion law of the system under the control input .

[0065] In this Lagrangian function, by introducing the adjoint variable , the dynamic constraint conditions can be explicitly incorporated into the optimization problem, thereby ensuring that the finally obtained control strategy conforms to the physical limitations of the system.

[0066] Then, based on the basic principle of the calculus of variations, the Lagrangian function is differentiated with respect to the state variable and the control input respectively to obtain the corresponding Lagrange equations: ; ; where is the Lagrangian function, is the position of the robot, is the derivative of the robot position with respect to time, is the control input, is the total derivative operator with respect to time .

[0067] Through the above equations, the optimal control input and the optimal motion trajectory can be solved. In the actual solution process, the gradient descent method or other numerical optimization methods can be combined to obtain a specific solution.

[0068] As an option, during the solution process, the Lagrange equations can be reformulated by introducing the Hamiltonian function to simplify the calculation process. The definition of the Hamiltonian function is as follows: ; where is the position of the robot, is the control input, is the Lagrangian function, is the Hamiltonian function, is a co-state variable, is the system dynamic function.

[0069] In this case, by solving the optimality conditions of the Hamiltonian function, an optimization problem equivalent to the original problem can be obtained. This method is applicable not only to continuous-time systems but also to discrete-time cases.

[0070] Furthermore, to ensure the smoothness of the control input, a regularization term can be introduced during the optimization process or a filter can be used to smooth the input signal. For example, if the acceleration input is smoothed and optimized, unnecessary mutations can be reduced by adding a quadratic term of the acceleration change to the objective function.

[0071] In some embodiments, to improve the efficiency of the optimization solution, a piecewise optimization strategy can be adopted. Specifically, the time interval of the entire task can be divided into several sub-intervals. Within each sub-interval, the optimal control strategy is solved separately, and then the solutions of each sub-interval are spliced together. This method can not only reduce the computational amount but also improve the accuracy and stability of the optimization.

[0072] In addition, considering the changes in system parameters and the influence of external disturbances in practical applications, an adaptive mechanism can be introduced during the optimization process. By updating the weight parameters of the state variables and control inputs in real time, the system can better adapt to different task requirements and environmental conditions. For multi-task optimization problems executed in a dynamic environment, this method has higher robustness and flexibility.

[0073] S5. Process the constrained optimization problem: After obtaining the optimal control strategy, for the constrained optimization problem, apply the KKT conditions to solve the optimization problem with inequality constraints; The optimal control strategy of the intelligent security robot during the execution of multi-tasks is derived by the variational method, and the corresponding optimal control input and motion trajectory are obtained. However, in practical applications, the robot usually needs to satisfy some additional constraint conditions when performing tasks, such as motion constraints, energy constraints, and path constraints. These constraint conditions involve not only equality constraints but also may include inequality constraints. To further optimize the control strategy under these constraint conditions, it is necessary to apply the KKT (Karush-Kuhn-Tucker) conditions to solve the optimization problem with inequality constraints.

[0074] In this embodiment, after obtaining the optimal control strategy, for the optimization problem with inequality constraints, the KKT conditions are further applied for solution. By introducing Lagrange multipliers and dual variables, the constrained optimization problem can be effectively solved, and it is ensured that the obtained control strategy is not only optimal but also satisfies all constraint conditions.

[0075] During the optimization process, the constraint conditions may include the dynamic constraints, energy constraints, path constraints, etc. of the robot when performing tasks. These constraint conditions are usually expressed as inequality constraints. For example, the maximum speed, maximum acceleration, upper limit of energy consumption, etc. of the robot can all be regarded as inequality constraints.

[0076] To solve this constrained optimization problem, in this embodiment, the KKT conditions are introduced, combined with the Lagrange multiplier method, to construct the Lagrangian function of the constrained optimization, and the optimal solution is obtained by solving the KKT conditions.

[0077] Specifically, let the objective function be , and the constraint conditions be ≤0 and =0 (where, represents the inequality constraint, represents the equality constraint), the Lagrangian function can be expressed as: ; where, is the Lagrange multiplier of the inequality constraint , is the Lagrange multiplier of the equality constraint , is the Lagrangian function, used to construct the optimization problem including constraints, representing the combination of the objective function and the constraint conditions, is the optimized objective function.

[0078] In this case, the KKT conditions include the following aspects: Primal feasibility condition: The constraint conditions must be satisfied, that is: ; where, is the Lagrange multiplier of the inequality constraint .

[0079] Dual feasibility condition: The Lagrange multipliers must be non - negative, that is: ; where, is the Lagrange multiplier of the inequality constraint .

[0080] Complementary slackness condition: The product of the Lagrange multiplier corresponding to each inequality constraint and the constraint condition must be zero, that is: ; where, is the Lagrange multiplier of the inequality constraint .

[0081] Optimality Conditions: The partial derivatives of the Lagrangian function with respect to the control input and the state variables must be zero, i.e.: ; where is the Lagrangian function, are the state variables, usually representing the current position or state of the robot or system, is the control input variable.

[0082] In this embodiment, first, the Lagrangian function needs to be constructed according to the dynamic model and constraint conditions of the robot system. Then, by optimizing the Lagrangian function, the control input and the state variables are solved while satisfying all constraint conditions.

[0083] To solve the above optimization problem, first, the Lagrangian function is constructed and the KKT conditions are applied for solution. During the solution process, in some embodiments, numerical optimization algorithms such as Newton's method or gradient descent method may be used to more efficiently solve the Lagrange multipliers and the optimal control input.

[0084] Specifically, assume that the state and control input of the robot satisfy the following forms of constraints: Dynamic Constraints: Such as maximum speed and maximum acceleration.

[0085] Energy Constraints: Such as battery consumption and maximum energy consumption limit.

[0086] Path Constraints: Such as the robot cannot cross a specific obstacle area.

[0087] These constraints can be handled by the KKT conditions. When handling, for each constraint , a non - negative Lagrange multiplier is introduced, and it is required that at the optimal solution, the product of the Lagrange multiplier and the corresponding constraint is zero, which ensures that the Lagrange multiplier can only take effect when the constraint is activated.

[0088] Construct the Lagrangian function: Combine the objective function and the constraint conditions, construct the Lagrangian function, and introduce the Lagrange multipliers; Apply the KKT conditions: Take the derivative of the Lagrangian function and apply the KKT conditions to solve for the optimal control input and the state trajectory.

[0089] Numerical optimization: Combine numerical optimization algorithms (such as gradient descent method or Newton's method) to solve the optimization problem and obtain the optimal solution.

[0090] Verification Constraints: Ensure that all constraint conditions are satisfied under the optimal solution, especially the complementary slackness conditions of inequality constraints.

[0091] In some embodiments, the optimization process may require multiple iterations to adjust the Lagrange multipliers and control inputs until the optimality conditions and all constraint conditions are satisfied. Through these steps, the optimal control strategy of the intelligent security robot in complex tasks can be finally obtained.

[0092] S6. Optimize the control strategy in a dynamic environment using the HJB equation: In a dynamic environment, use the HJB equation to describe the dynamic optimality of the optimal control strategy and adjust the robot task scheduling and path planning according to environmental changes; By introducing the KKT conditions and Lagrange multipliers, the optimization problem with inequality constraints has been successfully solved, and the optimal control strategy of the robot in a static environment has been obtained. However, in a dynamic environment, the robot not only needs to consider static constraints but also must adapt to environmental changes in real time and adjust the task scheduling and path planning accordingly. Therefore, the key to optimizing the control strategy in a dynamic environment is to accurately adjust the control strategy by introducing a method for describing dynamic optimality. In this case, the Hamilton-Jacobi-Bellman (HJB) equation becomes an effective tool for describing the dynamic optimality of the optimal control strategy.

[0093] In this embodiment, for the optimal control problem in a dynamic environment, the HJB equation is applied to describe the dynamic optimality of the optimal control strategy. Specifically, the HJB equation can provide a real-time control strategy optimization scheme for the robot by considering the adjustment of the robot's task scheduling and path planning in the environment. Through the dynamic perception of environmental changes, the robot can adjust the optimal path and task arrangement in real time during the execution process to ensure that the task objectives can still be efficiently achieved in a constantly changing environment.

[0094] In a dynamic environment, the robot needs to perceive environmental changes in real time and optimize the control strategy. The HJB equation provides an effective mathematical framework for solving such dynamic optimization problems. Specifically, set the control input of the robot as , the state variable as , and define the objective function as the performance index (such as time, energy, etc.). In a dynamic environment, the robot not only needs to consider real-time environmental feedback during task execution but also must adjust the task scheduling and path planning according to this feedback.

[0095] The basic form of the HJB equation is: ; where, is the control input variable, represents the value function in state , that is, the expected cumulative return of executing the optimal control policy starting from this state; is the instantaneous cost of the system (such as energy consumption, time, etc.); is the dynamic equation of the robot system, which describes the motion of the robot under the control input ; is the gradient of the value function with respect to the state variable , which reflects the impact of state changes on the future cumulative return.

[0096] In general, the control problems faced by robots not only include minimizing the optimization objective, but also include dynamically adjusting task scheduling and path planning under changing environments. To address these changes, the HJB equation provides a description of dynamic optimality, enabling the robot to make optimal adjustments to future task objectives based on the current environmental state and the control input .

[0097] Specifically, in a dynamic environment, environmental conditions (such as obstacles, task priorities, etc.) may change over time. To enable the robot to operate effectively in such an environment, task scheduling and path planning must be adjusted based on real-time feedback. In some embodiments, the robot behavior can be adjusted through the following steps: Environmental perception and state update: The robot uses sensors to perceive environmental changes in real time and updates the state variable . For example, the robot may need to bypass suddenly appearing obstacles or adjust task priorities.

[0098] Dynamic optimization adjustment: By solving the HJB equation, the robot can adjust path planning and task scheduling based on the current environmental state and the optimal control input . For example, when an obstacle appears, the robot can adjust the path in real time to avoid the obstacle and optimize the execution time or energy consumption.

[0099] Update control policy: Based on the solution of the HJB equation, the robot can dynamically adjust the control input at each moment to ensure optimal performance in a changing environment. This includes adjusting the robot's motion trajectory, task execution order, etc.

[0100] Through the above steps, the robot can achieve real-time optimization in a dynamic environment, and its path planning and task scheduling can flexibly adapt to external changes.

[0101] In some embodiments, in addition to path planning and task scheduling, the HJB equation can also be used to optimize control strategies in other dynamic environments. For example, in a much more complex multi-robot collaboration task, each robot can adjust its own action strategy through the HJB equation while taking into account the collaboration requirements with other robots. In this case, the HJB equation not only describes the optimal control of a single robot but can also be extended to the collaborative control of multiple robots, thereby achieving the optimal scheduling of the overall task objective.

[0102] In addition, considering the uncertainties in practical applications, the HJB equation can also be extended in combination with probability models, such as introducing a stochastic process to describe the random changes in the environment. In this case, the solution of the HJB equation will include the expectation of future states, rather than just specific path planning or task scheduling.

[0103] The control system of an intelligent security robot described below can be correspondingly referred to in relation to the control method of an intelligent security robot described above.

[0104] Please refer to the appendix Figure 2 , the present invention also provides a control system of an intelligent security robot, including: Control decision-making module: used to run a multi-objective optimization function, schedule and path plan multiple tasks executed by the robot, and generate optimal control instructions on the premise of satisfying dynamic, energy, and path constraints; the control decision-making module performs task rearrangement and optimal selection when there are task conflicts or resource shortages based on task priorities and resource constraints; Environmental perception module: used to obtain the state information of the environment where the robot is located, and the state information includes obstacle positions, boundary regions, and environmental change information; the module is composed of a visual camera, a lidar, an ultrasonic sensor, and an infrared sensor, and provides continuous and accurate environmental modeling results through a data fusion algorithm; Task management module: used to receive task instructions, manage the task queue, assign task priorities, and cooperate with the control decision-making module in real-time to update the task execution status; support the access and response of external alarm, patrol instructions, and image recognition tasks; Motion execution module: used to drive each execution component of the robot to complete motion instructions according to the optimal control strategy generated by the control decision-making module, and the motion instructions include chassis driving, attitude adjustment, and obstacle avoidance actions; the module can also perform feedback monitoring on the execution actions; Energy management module: used to monitor the energy consumption status of the robot's power system in real-time, evaluate the energy consumption of task execution, and issue an energy warning when the energy is insufficient; support task adjustment and scheduling reconstruction based on the energy status to ensure the continuity of the robot's task execution; Communication interaction module: used to implement data communication between the system and external servers and operation terminals; supports wired or wireless communication methods, including communication protocols such as Wi-Fi, Bluetooth, and ZigBee, and is used for task distribution, data feedback, and system status synchronization; Dynamic policy update module: used to correct the control policy in real time according to the feedback results of the HJB equation when the environment changes or the task status changes, so as to achieve the adaptive optimization of the control system.

[0105] The system of this embodiment can be used to execute the above method embodiment, and its principle and technical effect are similar, so it will not be elaborated here.

[0106] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for an intelligent security robot, characterized in that, It includes the following steps: Establish a robot motion and task model: Construct the dynamic relationships among the position, velocity, and acceleration of an intelligent security robot, and define the multi-task objectives executed by the robot, where the execution time, energy consumption, and priority of each task are expressed through an objective function; Define the constraint conditions: According to the physical, energy, and path limitations faced by the robot when executing multi-task objectives, define dynamic constraints, energy constraints, and path constraints; Construct a multi-objective optimization function: After defining the constraint conditions, combine the factors of the execution time, energy consumption, and smoothness of control input of the multi-task objectives to construct a comprehensive optimization objective function; Through this function, optimize the task execution time and energy consumption; Apply the variational method to derive the optimal control strategy: Based on the multi-objective optimization function, use the variational method to derive the optimal control strategy; By solving the Lagrangian equation, determine the optimal control input and motion trajectory of the robot when executing tasks; Handle the constrained optimization problem: After obtaining the optimal control strategy, for the constrained optimization problem, apply the KKT conditions to solve the optimization problem with inequality constraints; Use the HJB equation to optimize the control strategy in a dynamic environment: In a dynamic environment, use the HJB equation to describe the dynamic optimality of the optimal control strategy, and adjust the robot task scheduling and path planning according to environmental changes.

2. The control method of an intelligent security robot according to claim 1, wherein The multi-task objectives include: Execute the area patrol task, where the robot needs to cover a specified area within a specified time and return to the initial position; Execute the abnormal behavior monitoring task, where the robot needs to collect video or image information during the patrol and analyze in real time whether there are abnormal events; Execute the emergency response task. When receiving external alarm information, the robot needs to interrupt the current task and go to the specified location within a limited time to handle the emergency; Each task is scheduled according to the preset priority. Tasks with higher priority are executed first in case of resource conflicts, and there are associations and dependencies among tasks.

3. The control method of an intelligent security robot according to claim 1, characterized in that, The dynamic constraints include: The position of the robot changing with time is limited by its maximum speed and maximum acceleration; The change in the robot's speed should meet the smoothness requirements; There is a physical coupling relationship between the control input and the state variables; During the path planning process, it is necessary to ensure that the robot can operate stably.

4. The control method of an intelligent security robot according to claim 1, characterized in that, The energy constraints include: The total energy consumption of the robot during the task execution process does not exceed the preset maximum energy threshold; The energy budget for each task is allocated by the system during task scheduling; During the task execution process, monitor the energy consumption in real time. If it is estimated that the energy upper limit will be exceeded, compensate by adjusting the task order or aborting low-priority tasks; The energy constraint is dynamically coupled with the robot's motion control strategy, enabling the control method to dynamically adjust the task execution plan according to the remaining energy.

5. The control method of an intelligent security robot according to claim 1, characterized in that The path constraints include: The motion path of the robot needs to be kept within the preset spatial boundary and not cross the boundary or enter the restricted area; In complex scenarios, it is necessary to avoid obstacles, and the obstacle information can be obtained through sensors and fed back to the path planning module in real time; The path constraint requires the robot to maintain a set safety distance when executing tasks in a specific area to prevent collisions with people or objects. When performing multitasks, path planning needs to simultaneously meet the access order and location requirements of multiple task points.

6. The control method of an intelligent security robot according to claim 1, wherein, The optimization objective function includes: The sum of task execution times, which is used to measure the efficiency of multitasks completed within a specified period; The sum of task energy consumption, which is used to evaluate the overall energy utilization and serve as the basis for energy consumption optimization; The smoothness index of control input, which is used to constrain the acceleration or jerk change of the robot; The above objectives are weighted and combined with weight coefficients to form a comprehensive optimization objective function, and the optimal solution of multitask scheduling and path control is obtained by optimizing this function.

7. The control method of an intelligent security robot according to claim 1, characterized in that The variational method includes: By introducing the Lagrangian function, the optimization objective and various constraint conditions are combined to form a unified optimization model; The Euler-Lagrange equation is used to perform variational differentiation on the control input and state variables to obtain the necessary conditions satisfied by the optimal solution; The change trajectory of the control variable is obtained by solving the boundary value problem; Combined with the task objective and constraint conditions, the variational method provides an analytical or numerical solution method for obtaining the optimal control strategy.

8. A control method for an intelligent security robot according to claim 1, characterized in that, The KKT conditions include: When there are inequality constraints, an equivalent optimization condition expression is constructed by introducing Lagrange multipliers; The KKT conditions include four items: primal feasibility, Lagrangian differentiability, complementary slackness, and gradient optimality; The optimal control input and state trajectory that satisfy all constraint conditions can be obtained by solving the KKT system; This method is applicable to dealing with multiple simultaneous inequality constraints.

9. The control method of an intelligent security robot according to claim 1, characterized in that, The HJB equation includes: Based on the principle of dynamic programming, the expression of the minimum cost function of the system in each state is established; By solving the HJB equation, the optimal feedback form of the control strategy changing with time is obtained to achieve adaptive control in a dynamic environment; This method considers the environmental uncertainty and the real-time nature of state transitions, enabling the robot to optimize the path and task allocation in real time during the execution process; Combined with the actual sensing data input, the HJB equation is used to correct the optimal control strategy in real time to adapt to the impact brought by environmental changes.

10. A control system for an intelligent security robot, according to the control method of an intelligent security robot described in any one of claims 1-9, characterized in that, It includes: Control decision-making module: used to run the multi-objective optimization function, schedule and path plan the multitasks executed by the robot, and generate optimal control instructions on the premise of meeting dynamic, energy, and path constraints; the control decision-making module performs task rearrangement and optimal selection in case of task conflicts or resource shortages based on task priorities and resource constraints; Environmental perception module: used to obtain the state information of the environment where the robot is located, and the state information includes obstacle positions, boundary regions, and environmental change information; the module is composed of a visual camera, a lidar, an ultrasonic sensor, and an infrared sensor, and provides continuous and accurate environmental modeling results through data fusion algorithms; Task management module: used to receive task instructions, manage the task queue, assign task priorities, and cooperate with the control decision-making module in real time to update the task execution status; support the access and response of external alarm, patrol instructions, and image recognition tasks; Motion execution module: It is used to drive each execution component of the robot to complete motion instructions according to the optimal control strategy generated by the control decision-making module. The motion instructions include chassis driving, attitude adjustment, and obstacle avoidance actions. The module can also perform feedback monitoring on the executed actions. Energy management module: It is used to monitor the energy consumption status of the robot power system in real time, evaluate the energy consumption of task execution, and issue an energy warning when the energy is insufficient. It supports task adjustment and scheduling reconstruction based on the energy state to ensure the continuity of robot task execution. Communication and interaction module: It is used to realize data communication between the system and external servers and operation terminals. It supports wired and wireless communication methods, including communication protocols such as Wi-Fi, Bluetooth, and ZigBee, for task distribution, data backhaul, and system status synchronization. Dynamic policy update module: It is used to correct the control strategy in real time according to the feedback results of the HJB equation when the environment changes or the task status changes, so as to realize the adaptive optimization of the control system.

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