Multi-humanoid robot distributed collaborative carrying method based on model predictive control

Abstract

The invention belongs to the technical field of automation, and discloses an improved multi-humanoid robot distributed load sharing cooperative carrying control method based on model predictive control, and the method comprises the steps: inputting and torque inverse solution; carrying out MPC modeling and degree-of-freedom setting; optimizing a target and a constraint condition; stable contact between the robot and the object in the carrying process is ensured, and special carrying constraints are defined; a comprehensive cost function is formulated, and optimization of the carrying process is achieved. Through the improved MPC method and the torque distribution strategy, the efficient and accurate humanoid robot cooperative carrying control method is provided, coordination and cooperation among the robots can be achieved, accurate control over the torque and stability of the postures of the robots in the carrying process can be guaranteed, and therefore the overall performance of multi-robot cooperative carrying is improved.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of automation technology, and in particular relates to an improved multi-human robot distributed load sharing collaborative handling control method and system based on model predictive control. Background Art

[0002] With the rapid development of industrial automation and robotics, the application of robots in tasks such as handling and assembly has gradually increased. Especially in tasks that require high precision and high efficiency, the advantages of robots have become more and more obvious. As a type of robot with high flexibility and adaptability, humanoid robots have shown great potential in many fields. However, when facing complex handling tasks, a single robot is often restricted by multiple factors such as load, task environment and movement space, which limits its efficiency and stability in performing tasks.

[0003] In order to overcome the limitations of a single robot, multi-robot systems (MRS) have gradually become an important research direction in robotics in recent years. Multi-robot collaboration can increase the overall flexibility, stability and efficiency of the system by sharing tasks. Especially when handling large or heavy objects, multi-robot collaboration can effectively improve the accuracy and speed of task completion. Through reasonable task allocation and coordination, multiple robots can achieve more complex and efficient handling operations in collaborative operations than a single robot. Among them, humanoid robots have broad research space and application prospects in collaborative handling tasks due to their humanoid characteristics, environmental adaptability and excellent terrain passability.

[0004] In multi-robot collaborative handling, task allocation and motion coordination are two important research topics. Although multi-robot systems have broad application potential in the field of handling, the following aspects are still understudied when it comes to collaborative handling of humanoid robots:

[0005] Unreasonable distribution of upper-level handling tasks: In collaborative handling tasks, how to reasonably distribute tasks and ensure load balance among robots in different tasks is still an issue that has not been fully addressed. If tasks are unevenly distributed, individual robots will be overloaded, causing dynamic imbalance or reduced work efficiency; in addition, since the multi-robot collaborative handling system is a multi-input system, if there is no reasonable algorithm to coordinate the loads between the robots, it will cause excessive internal force on the objects being handled, resulting in waste of resources and even damage to the objects being handled;

[0006] The lower-level humanoid robots are not coordinated: Unlike traditional planar motion robots, since the handling system is composed of multiple humanoid robots, they achieve movement through the periodic reciprocating swing of the leg joints. Therefore, if there is no reasonable multi-human robot gait synchronization algorithm to make the leg movements of the humanoid robots tend to be synchronized, it will have a great impact on the stability of the handling system, causing it to vibrate up and down, and in severe cases, damage the objects being handled, and then damage the humanoid robots;

[0007] The shortcomings of the existing technology: there is no upper-level collaborative handling algorithm for effective load task allocation; there is no humanoid robot model predictive control algorithm for dynamic trajectory tracking collaborative handling problems; there is no multi-human robot gait synchronization algorithm with enhanced coordination ability. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides an improved multi-human robot distributed load sharing collaborative handling control method based on model predictive control.

[0009] The present invention is implemented as follows: an improved multi-person robot distributed load sharing collaborative handling control method based on model predictive control, the method comprising:

[0010] S1: Collaborative handling upper-level algorithm for trajectory tracking and load sharing;

[0011] S2: Model predictive control (MPC) and whole body control (WBC) lower-level control algorithms for collaborative handling;

[0012] S3: Algorithms for gait synchronization of multi-human robots.

[0013] Further, the S1 specifically includes:

[0014] First, a dynamic model of the object being transported is established. To increase the universality of the task, the parameters of the dynamic model are unknown.

[0015] Based on the desired motion, a control scheme for the transported object is established. The controller is designed as a basic model reference adaptive controller to handle trajectory tracking and adaptive estimation of unknown parameters.

[0016] In the unknown parameter adaptive estimation part, a distributed communication network is designed and a distributed consistency term is designed in the estimator to achieve active load sharing.

[0017] Through the above steps, the control signal that each robot needs to apply to the transported object can be obtained.

[0018] The above steps constitute the upper-level algorithm of multi-human robot collaborative handling.

[0019] The S2 specifically includes:

[0020] A dynamic model of a humanoid robot in a handling task is established. The model is based on the traditional humanoid robot dynamic model and adds a reaction force term to characterize the coupling effect between the humanoid robot and the object being handled.

[0021] Establish an MPC model, including loss functions and constraints. The objective function aims to minimize motion tracking error and control cost. The influence of the two is measured by the weight matrix. Constraints include dynamic constraints, input constraints, contact constraints (hand, foot end) and friction constraints.

[0022] MPC solution and WBC implementation. After MPC solution is performed based on the open source library, the MPC output results of the foot-end reaction force and joint motion state of each robot corresponding to the control input of S1 are passed to the WBC module to obtain the control input of each motor.

[0023] Through the above steps, the motor control instructions of the humanoid robot for realizing collaborative handling can be obtained.

[0024] The S3 specifically includes:

[0025] A gait synchronization controller is established based on the distributed MPC method. The optimization variables are phase and step frequency. The loss function is the step frequency difference and phase difference of adjacent robots in the communication graph. The constraints include initial and terminal constraints and limit constraints.

[0026] After solving the MPC based on the open source library, the optimized step frequency result is obtained, which is used as the input to the leg trajectory planner to output the complete leg trajectory.

[0027] Through the above steps, the synchronized gait trajectory of the multi-human robot can be obtained.

[0028] Another object of the present invention is to provide an improved multi-humanoid robot distributed load sharing collaborative handling control system based on model predictive control based on the multi-humanoid robot distributed load sharing collaborative handling control method based on model predictive control, the system specifically comprising:

[0029] The upper layer distributed load sharing collaborative handling module is used to calculate the input force when handling objects with unknown dynamic models without generating internal forces;

[0030] The lower-level model predictive control and whole-body control modules are connected to the upper-level distributed load-sharing collaborative handling modules, used to calculate the control input and state of the humanoid robot that implements the upper-level modules, and finally calculate the joint motor input signals;

[0031] Distributed multi-human robot gait synchronization module: used to achieve gait synchronization of multi-human robots, making the multi-human robot handling process more stable and reliable.

[0032] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the improved multi-human robot distributed load sharing collaborative handling control method based on model predictive control.

[0033] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0034] First, the present invention aims at the shortcomings of the existing multi-robot collaborative handling technology and proposes a multi-robot distributed load sharing collaborative handling control method based on model predictive control, which has the following significant advantages:

[0035] 1. Realize distributed load sharing, thereby avoiding excessive internal forces and waste of resources caused by multiple input systems.

[0036] In the existing multi-robot collaborative handling, most of the work is to use a load distribution algorithm based on geometric angles when considering load task allocation, and use a weighted pseudo-inverse matrix to achieve the robot's internal force-free handling of the object being handled. However, this requires the preset geometric parameters of all robots relative to the object being handled, or the use of a fixed weighted pseudo-inverse matrix, which cannot actively share the load according to the load differences between robots.

[0037] Advantages of this technology: The present invention completely studies the generation mechanism of internal force in multi-robot collaborative handling, solves it from the perspective of control theory, takes into account the individual load-bearing differences between robots, and proposes a parameter estimation algorithm for distributed load sharing, which ensures the internal force-free characteristics of multi-robot collaborative handling in the steady-state stage, avoids waste of resources and protects the transported system.

[0038] 2. Realize the tracking of dynamic trajectory of transported objects.

[0039] In the existing leg-based multi-robot collaborative handling, most of the work is to achieve collaborative handling at the kinematic level, rather than considering trajectory tracking at the dynamic level, which is necessary but complicated. In addition, some data-driven kinematic layer solutions cannot achieve real-time control due to the limitations of the algorithm itself.

[0040] Advantages of this technology: The present invention divides the collaborative handling task into upper-level collaborative handling and lower-level multi-person robot implementation, which can well achieve the tracking of dynamic trajectories at the dynamic level, and generalizes the transported objects with the unknown dynamics modeling control scheme to improve the adaptability of the task.

[0041] 2. Realize gait synchronization of multi-human robots.

[0042] The existing gait synchronization of multi-human robot / multi-quadruped robot systems is considered in the collaborative control algorithm, but there is no gait synchronization algorithm for collaborative handling. Especially when considering the existence of terrain differences, a reasonable gait synchronization algorithm is more important in ensuring the stability and reliability of multi-human robot collaborative handling.

[0043] Advantages of this technology: This invention proposes a gait synchronization algorithm, which rationally plans the gait of the humanoid robot system based on distributed model predictive control. This part takes the phase and amplitude of the gait as the starting point, and realizes the gait synchronization of multi-humanoid robots by establishing reasonable objective functions and constraints, thereby ensuring the stability and reliability of multi-humanoid robot collaborative handling at the application level.

[0044] Second, the expected benefits and commercial value of the technical solution of the present invention after transformation are as follows: The technical solution of multi-person robot collaborative handling has potential for a wide range of application scenarios and commercial value, especially in the fields of industry, logistics, construction, etc. After this technical solution is transformed into an actual product, the following benefits and commercial value can be expected:

[0045] 1. Improve work efficiency and flexibility

[0046] Expected benefits: Compared with traditional single robots or dedicated mechanical equipment, multiple robots working together can handle more complex and flexible handling tasks. These robots can automatically adjust their posture, path, and handling strategy to adapt to different object sizes, shapes, weights, and environmental conditions. This flexibility makes them suitable for a variety of non-standard and unpredictable handling tasks, significantly improving work efficiency.

[0047] Commercial value: In scenarios where frequent handling is required, such as manufacturing, warehousing and logistics, and construction sites, collaborative handling robots can reduce manual labor, improve handling efficiency, reduce errors and damage, and reduce operating costs.

[0048] 2. Replace human work in dangerous environments

[0049] Expected benefits: In harsh or dangerous working environments (such as mines, chemical plants, nuclear power plants, etc.), multi-person robots can replace humans to complete dangerous or heavy handling tasks, thereby protecting the safety of workers. These robots can work together to carry heavy or dangerous objects, reducing the risks in human operations. Commercial value: The application of this technology in high-risk environments can significantly reduce the risk of industrial accidents, thereby reducing the company's safety costs and insurance expenses. In addition, such a system can improve the company's ability to continue working in harsh environments, thereby improving production efficiency.

[0050] 3. Save labor costs

[0051] Expected benefits: Humanoid robot collaborative handling can replace a large amount of manual labor, especially in labor-intensive industries (such as logistics warehousing and factory handling), reducing the dependence of enterprises on manpower. Robots can work continuously for a long time without rest, thus significantly reducing labor costs.

[0052] Business value: With the rise of global labor costs, the application of robot collaborative handling systems can help companies reduce labor costs, especially in logistics centers, distribution stations, and manufacturing plants, which can bring long-term cost savings and increase corporate profit margins.

[0053] 4. Improve the level of automation and intelligence

[0054] Expected benefits: The multi-robot collaborative system has intelligent perception, planning, decision-making and execution capabilities, and can be seamlessly integrated with other intelligent systems (such as warehouse management systems, supply chain management systems, etc.) to form a highly automated and intelligent handling solution. This system can dynamically adjust the working mode according to the environment and task requirements, optimize the handling path, and coordinate the division of labor and cooperation among multiple robots.

[0055] Commercial value: The improvement of intelligence level not only improves handling efficiency, but also reduces the idle time of equipment and resource waste, helping enterprises optimize operations. For some smart factories and automated warehouses, such technology is one of the core competitiveness.

[0056] 5. Wide range of applications and huge market potential

[0057] Expected benefits: Collaborative handling robots can be used in many different fields. In addition to common manufacturing and logistics, they can also be extended to disaster relief, medical treatment, military and other scenes that require object handling. In disaster relief scenes, robots can collaboratively carry heavy objects or rescue equipment, carry supplies on the battlefield, and even carry medical equipment in the operating room.

[0058] Commercial value: This technology has a wide range of market application potential and can be applied to multiple industries, such as warehousing and logistics (such as automation of Amazon warehouses), construction (heavy object handling), service industry (handling in hotels, shopping malls, and hospitals), rescue, etc. With the wave of intelligent upgrading of logistics and manufacturing industries, the market demand for this technology will continue to grow.

[0059] 6. Enhance the differentiated competitiveness of products and services

[0060] Expected benefits: By applying the multi-person robot collaborative handling system, companies can provide more competitive products or services and respond quickly to customer needs. For example, in scenarios that require flexible and efficient logistics, such as fast delivery and customized production, this collaborative robot can significantly shorten delivery time and improve customer satisfaction.

[0061] Business value: Companies can gain market competitive advantages and enhance brand value by providing differentiated products or services. For example, logistics companies can increase their market share through faster and more accurate handling services, and manufacturing companies can meet customized needs and enhance customer stickiness through more flexible production capabilities.

[0062] 7. Scalability and modular design

[0063] Expected benefits: The multi-person robot collaborative handling system can be designed to be modular, and the number and capabilities of robots can be flexibly configured according to task requirements. Enterprises can start with small-scale deployment and gradually expand the scale of the robot system as demand increases, reducing the initial investment risk.

[0064] Business value: This modularity and scalability enables enterprises to flexibly adjust investment plans according to business needs and avoid one-time large-scale capital expenditures. For small and medium-sized enterprises, such scalability is particularly attractive, helping to reduce early investment pressure and gradually improve the level of automation.

[0065] 8. Long-term development and sustainability

[0066] Expected benefits: Collaborative handling robot technology can improve resource utilization, reduce energy consumption and material loss. For example, by intelligently optimizing paths and task allocation, energy waste during handling can be significantly reduced. In addition, robots can be continuously maintained and upgraded, extending system life and reducing long-term operating costs.

[0067] Commercial value: With the global trend of sustainable development, environmentally friendly and efficient collaborative robot systems meet the needs of enterprises to reduce carbon emissions and save energy. They can help enterprises obtain green certification, enhance the brand's social responsibility image, and attract more customers and investors.

[0068] The multi-person robot collaborative handling technology has significant expected benefits and commercial value, especially in terms of improving handling efficiency, reducing costs, and improving safety and intelligence. With the increasing demand for automation and intelligence in industries such as industry and logistics, this technology is expected to play an important role in multiple industries and become an important force that cannot be ignored in the future market. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of an improved multi-person robot distributed load sharing collaborative handling control method based on model predictive control provided by an embodiment of the present invention;

[0070] Figure 2 is a comprehensive cost function content graph provided by an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram of an upper-layer collaborative transport algorithm provided by an embodiment of the present invention:

[0072] Figure 4 This is a schematic diagram of internal forces provided by an embodiment of the present invention:

[0073] Figure 5 This is a trajectory tracking result diagram provided by an embodiment of the present invention:

[0074] Figure 6 : is a diagram of parameter estimation and parameter estimation error simulation results provided by an embodiment of the present invention:

[0075] Figure 7 This is an internal force result diagram provided by an embodiment of the present invention:

[0076] Figure 8 This is a graph of experimental trajectory tracking results provided by an embodiment of the present invention:

[0077] Fig. 9 This is a diagram of experimental parameter estimation results provided by an embodiment of the present invention: DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0079] like Figure 1 As shown, the present invention provides an improved multi-human robot distributed load sharing collaborative handling control method based on model predictive control.

[0080] The present invention is implemented as follows: an improved multi-human robot distributed load-sharing collaborative handling control method based on model predictive control, firstly, analyzing the environmental characteristics of winter to determine the specific requirements of the handling task; then, considering the dynamic model and load-sharing characteristics of the object being handled, the expected force exerted by the robot on the object is obtained based on the upper-level collaborative handling algorithm for trajectory tracking and load sharing; then, an MPC model of the humanoid robot is established, including the establishment of an objective function and constraints, and the obtained expected force is taken into consideration as part of the MPC model, and the MPC is solved; further, the result obtained by the MPC solution is input into a WBC module, and the WBC problem is solved to obtain optimized input commands for each joint of the humanoid robot; finally, a distributed gait synchronization algorithm is run between the humanoid robots to obtain gait instructions, thereby realizing gait synchronization of the humanoid robot group and improving the overall performance of the handling process.

[0081] The present invention can also adopt the following method, which comprises:

[0082] S1: Collaborative handling upper-level algorithm for trajectory tracking and load sharing;

[0083] S2: Model predictive control MPC and whole body control WBC lower level control algorithm for collaborative handling;

[0084] S3: Algorithms for gait synchronization of multi-human robots.

[0085] like Figure 3 The core of the upper-level collaborative handling algorithm lies in distributed load sharing and trajectory tracking. Through the force and displacement sensors between the robot and the object being handled, the system obtains real-time interaction data (such as contact force, object displacement, etc.). Based on the object's dynamic model and task requirements, the upper-level algorithm calculates the force distribution and target trajectory that each robot needs to apply to ensure that the load is evenly distributed during the handling process, while avoiding internal force problems caused by unbalanced forces. This module further plans the handling path to ensure stability and efficiency while the object is being manipulated.

[0086] The lower-level control algorithms include model predictive control (MPC) and whole-body control (WBC). MPC uses physical models and future state predictions to calculate the optimal control input of the robot's joints, ensuring high precision and high responsiveness of the robot's movements. At the same time, the whole-body control module is responsible for coordinating the movements of multiple joints of the robot to achieve whole-body balance and the execution of target tasks. By integrating force feedback signals and trajectory planning results, WBC dynamically adjusts the robot's posture, optimizes the angle, speed, and torque input of the robot's joints, and ultimately drives the joint motors to complete the handling task.

[0087] In multi-robot collaborative handling tasks, each robot needs to maintain gait synchronization to ensure the stability of the handling process. The gait synchronization algorithm coordinates the gait rhythm of all robots through real-time communication based on the position, stride, gait phase and other status data of each robot. During the handling process, when a robot has gait deviation or position offset, the gait synchronization module adjusts the pace of other robots based on the feedback signal to ensure overall coordination. The gait synchronization algorithm also uses a predictive adjustment strategy to correct potential inconsistencies in advance and improve handling stability.

[0088] The entire system collects the interaction data between the robot and the object and the robot's own status information through sensors to form a closed-loop control. The real-time feedback mechanism monitors the actions of each robot. When deviations or interference occur, the system dynamically adjusts the trajectory planning and control parameters. During the control process, MPC combines feedback data to optimize the control input in real time to ensure that the robot can still maintain the stability and accuracy of handling in complex environments. In addition, the system records task data for optimization of model parameters and performance improvement of the next task.

[0089] like Figure 5 The S1 specifically includes:

[0090] First, a dynamic model of the object being transported is established. To increase the universality of the task, the parameters of the dynamic model are unknown.

[0091] like Figure 6 Based on the desired motion, a control scheme for the transported object is established. The controller is designed as a basic model reference adaptive controller to handle trajectory tracking and adaptive estimation of unknown parameters.

[0092] In the unknown parameter adaptive estimation part, a distributed communication network is designed and a distributed consistency term is designed in the estimator to achieve active load sharing.

[0093] Through the above steps, the control signal that each robot needs to apply to the object being transported can be obtained. The above steps constitute the upper-level algorithm of multi-human robot collaborative transport.

[0094] The S2 specifically includes:

[0095] A dynamic model of a humanoid robot in a handling task is established. The model is based on the traditional humanoid robot dynamic model and adds a reaction force term to characterize the coupling effect between the humanoid robot and the object being handled.

[0096] Establish an MPC model, including loss functions and constraints. The objective function aims to minimize motion tracking error and control cost. The influence of the two is measured by the weight matrix. Constraints include dynamic constraints, input constraints, contact constraints (hand, foot end) and friction constraints.

[0097] MPC solution and WBC implementation. After MPC solution is performed based on the open source library, the MPC output results of the foot-end reaction force and joint motion state of each robot corresponding to the control input of S1 are passed to the WBC module to obtain the control input of each motor.

[0098] Through the above steps, the motor control instructions of the humanoid robot for realizing collaborative handling can be obtained.

[0099] The S3 specifically includes:

[0100] A gait synchronization controller is established based on the distributed MPC method. The optimization variables are phase and step frequency. The loss function is the step frequency difference and phase difference of adjacent robots in the communication graph. The constraints include initial and terminal constraints and limit constraints.

[0101] After solving the MPC based on the open source library, the optimized step frequency result is obtained, which is used as the input to the leg trajectory planner to output the complete leg trajectory.

[0102] Through the above steps, the synchronized gait trajectory of the multi-human robot can be obtained.

[0103] like Figure 4 The embodiment of the present invention provides an improved multi-humanoid robot distributed load sharing collaborative handling control system based on model predictive control based on the improved multi-humanoid robot distributed load sharing collaborative handling control method, the system specifically comprising:

[0104] like Figure 7 The upper layer distributed load sharing collaborative handling module is used to calculate the input force when handling objects with unknown dynamic models without generating internal forces;

[0105] The lower-level model predictive control and whole-body control modules are connected to the upper-level distributed load-sharing collaborative handling modules, used to calculate the control input and state of the humanoid robot that implements the upper-level modules, and finally calculate the joint motor input signals;

[0106] Distributed multi-human robot gait synchronization module: used to achieve gait synchronization of multi-human robots, making the multi-human robot handling process more stable and reliable.

[0107] An embodiment of the present invention provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the improved multi-human robot distributed load sharing collaborative handling control method based on model predictive control.

[0108] like Figure 8 The upper-level distributed load-sharing collaborative handling module uses a collaborative handling algorithm based on trajectory tracking and load sharing to obtain the expected force applied by the robot to the object by considering the expected trajectory for the task and the load factor for the humanoid robot for the load capacity. This part considers the model uncertainty of the object being handled to improve the versatility of the algorithm. The expected force obtained by running the algorithm is used as the input basis of the MPC.

[0109] like Fig. 9 The upper module calculates the force and torque that each robot needs to apply based on the expected trajectory signal to achieve uniform load distribution and avoid internal force problems caused by unbalanced force between robots. In this process, the estimated parameters of the unknown dynamic model of the object (such as mass, moment of inertia) are used in combination with the load sharing algorithm to generate uniform robot force and torque commands, and output control instructions to the lower module.

[0110] The lower-level model predictive control module calculates the body motion instructions and foot-end reaction force instructions required by each robot based on the force and torque output by the upper-level module, and transmits them to the whole-body control WBC module as the output of the MPC module. The WBC module calculates the control instructions required for each joint based on the input signal of the MPC module and converts them into the voltage and current signals required by the motor driver. Each robot drives its own joint motor according to these signals to ensure coordinated movements. In order to ensure the stability and anti-interference ability of the signal, filtering and noise suppression are also performed during the signal generation process.

[0111] The distributed multi-person robot gait synchronization module is based on an additional model predictive control module. This module includes a distributed MPC objective function and constraints including initial and terminal constraints and limit constraints, and performs MPC solving based on an open source library. The gait control algorithm can adjust the phase and stride of each robot's gait to ensure that the robots are synchronized, thereby ensuring the stability and accuracy of the handling task.

[0112] During the entire control process, the system records input signals, output signals, and feedback data in real time and stores them in memory. The processor periodically analyzes the data to optimize the control strategy and improve the control efficiency of future tasks. In addition, the stored historical data can also be used for dynamic calibration of the model and adjustment of the initial parameters of the model predictive control for the next task, improving the adaptability and reliability of the system.

[0113] like Figure 2The present invention discloses an improved multi-person humanoid robot distributed load sharing collaborative handling control method based on model predictive control, the method comprising: input and torque inverse solution; MPC modeling and degree of freedom setting; optimization objectives and constraints; ensuring stable contact between the robot and the object during the handling process, defining special handling constraints; formulating a comprehensive cost function to achieve optimization of the handling process. The present invention proposes an efficient and accurate humanoid robot collaborative handling control method through an improved MPC method and torque distribution strategy, which can not only achieve coordination and cooperation between robots, but also ensure accurate torque control and stable robot posture during the handling process, thereby improving the overall performance of multi-robot collaborative handling.

[0114] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0115] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. An improved multi-human robot distributed load sharing collaborative handling control method based on model predictive control, characterized in that: The method includes: S1: Collaborative handling upper-level algorithm for trajectory tracking and load sharing; S2: Model predictive control MPC and whole body control WBC lower level control algorithm for collaborative handling; S3: Algorithms for gait synchronization of multi-human robots.

2. The improved multi-person robot distributed load sharing collaborative handling control method based on model predictive control according to claim 1 is characterized in that: The S1 specifically includes: First, a dynamic model of the object being transported is established. To increase the universality of the task, the parameters of the dynamic model are unknown. Based on the expected motion, a control scheme for the transported object is established. The controller is designed as a basic model reference adaptive controller to handle trajectory tracking and adaptive estimation of unknown parameters. In the adaptive estimation of unknown parameters, a distributed communication network is designed, and a distributed consistency term is designed in the estimator to achieve active load sharing. Through the above steps, the control signal that each robot needs to apply to the transported object can be obtained.

3. The improved multi-person robot distributed load sharing collaborative handling control method based on model predictive control as claimed in claim 1, characterized in that: The S2 specifically includes: establishing a dynamic model of the humanoid robot in the handling task, in the form of adding a reaction force term on the basis of the traditional humanoid robot dynamic model to characterize the coupling effect between the humanoid robot and the object being handled; An MPC model is established, including loss functions and constraints; the objective function aims to minimize motion tracking error and control cost; the influence of the two is measured through a weight matrix; constraints include dynamic constraints, input constraints, contact constraints, and friction constraints.

4. The improved multi-person robot distributed load sharing collaborative handling control method based on model predictive control as claimed in claim 1, characterized in that: The S3 specifically includes: establishing a gait synchronization controller based on a distributed MPC method, wherein the optimization variables are phase and step frequency, the loss function is the step frequency difference and phase difference of adjacent robots in the communication graph, and the constraints include initial and terminal constraints and limit constraints; After solving the MPC problem based on the open source library, the optimized step frequency result is obtained, which is used as the input to the leg trajectory planner to output the complete leg trajectory; Through the above steps, the synchronized gait trajectory of the multi-human robot can be obtained.

5. The improved multi-person robot distributed load sharing collaborative handling control method based on model predictive control as claimed in claim 1, characterized in that: The following steps are also included: Obtain interaction data between multiple robots and transported objects, including contact force, object displacement, and robot status information; In the upper-level collaborative handling module, the target trajectory and the distributed load sharing force between robots are calculated according to the task requirements and the object dynamics model to avoid the generation of internal forces during the handling process; In the lower control module, the model predictive control (MPC) is used to optimize and predict the robot's motion state, and the whole body control (WBC) is used to coordinate the movement of the robot's joints to achieve accurate tracking of the upper module's targets; The rhythm, stride and phase of multiple robots’ gaits are adjusted in real time through the distributed gait synchronization module to ensure gait consistency and system stability during the handling process.

6. The method according to claim 5, characterized in that The distributed load sharing collaborative handling control system includes: The upper layer distributed load sharing collaborative handling module is used to calculate the force distribution and target trajectory of each robot under the task requirements; The lower-level model predictive control (MPC) module and the whole body control (WBC) module are used to optimize the robot joint control input and posture to achieve dynamic adjustment and precise manipulation; Distributed gait synchronization module, which is used to achieve gait coordination between robots through real-time communication and status feedback, avoiding system instability caused by gait deviation during handling; The data processing module is used to collect and analyze the interaction data between the robot and the object, and provide real-time feedback to adjust the control strategy during the task completion process.

7. The method according to claim 5, characterized in that The model predictive control algorithm of the lower control module includes the following steps: Construct optimization problem models that include robot kinematic and dynamic constraints; Combined with the robot's current state feedback and future goals, the optimal solution for joint input is calculated by predicting the change of the robot's state within the planning time window; The whole body control module is used to dynamically distribute joint torque to ensure the robot's posture balance and track the target trajectory; During the handling process, the model predictive control parameters are dynamically updated in combination with real-time interactive data to adapt to external interference and load changes.

8. An improved collaborative control system based on MPC and torque distribution strategy based on the improved collaborative control method based on MPC and torque distribution strategy as described in claims 1-7, characterized in that: The system specifically includes: The upper layer distributed load sharing collaborative handling module is used to calculate the input force when handling objects with unknown dynamic models without generating internal forces; The lower-level model predictive control and whole-body control modules are connected to the upper-level distributed load-sharing collaborative handling modules, used to calculate the control input and state of the humanoid robot that implements the upper-level modules, and finally calculate the joint motor input signals; Distributed multi-human robot gait synchronization module: used to achieve gait synchronization of multi-human robots, making the multi-human robot handling process more stable and reliable.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the improved multi-human robot distributed load sharing collaborative handling control method based on model predictive control as described in any one of claims 1-7.

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