Object grabbing method and system based on robot buffering and robot

Abstract

The invention discloses an object grabbing method and system based on robot buffering and a robot, and the method comprises the steps: monitoring the motion track of an air parabolic object in real time, and estimating and predicting the flight state of the air parabolic object through a dynamic model and a filtering algorithm; according to the flight state of the empty projectile, the empty projectile is grabbed through coordination control among a robot chassis, two arms and an end effector; through combination of a tail end six-dimensional force sensor and an admittance system, spring damping in multiple directions is simulated, and in the process of grabbing the empty projectile, instant impact force generated when the empty projectile makes contact with the robot is absorbed. The flight state of the object is estimated and predicted, the track and the dynamic behavior of the object can be accurately predicted, the robot can conduct timely adjustment before the object reaches the preset grabbing position, and the instantaneous impact force generated when the object makes contact with the robot is effectively relieved by designing spring damping; and the safety of the robot in the grabbing process is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of robot grasping technology, and in particular to an object grasping method, system, robot and computer-readable storage medium based on robot buffering. Background Art

[0002] In modern industry, automated unloading has become one of the key technologies to improve efficiency and reduce labor costs. Traditional industrial unloading operations usually rely on manual or fixed mechanical equipment, but with the development of robotics technology, more and more robots are introduced into unloading operations, especially in environments that require flexible handling of complex goods. In recent years, it is common for objects to move quickly or fall freely, especially in scenes such as warehousing, logistics, and loading and unloading, which poses a huge challenge to robots in capturing and stabilizing objects. Therefore, studying how to enable mobile robots to capture freely thrown objects and perform precise grasping has become a hot topic in the field of intelligent industrial unloading.

[0003] Existing robot unloading has the following disadvantages:

[0004] Lack of real-time and adaptability: In the actual scenario of industrial unloading, the landing point, speed and direction of objects may have great variability, which makes the existing technology unable to quickly adapt to complex and uncertain environments, and does not take into account the large mass and inertia of objects for buffering.

[0005] Fixed platform limitations: Many dual-arm robots or industrial robots rely on fixed platforms, which limits their ability to react and adjust quickly in complex dynamic scenarios. For tasks that require flexible movement, such as grasping falling objects, fixed platforms cannot meet the task requirements and cannot make effective real-time adjustments based on the object's trajectory.

[0006] Lack of multi-task collaboration: Although existing dual-arm robots can perform multi-task collaboration, they often fail to consider the coordination and cooperation between the mobile platform and the dual arms. In scenarios where rapid position adjustment and object capture are required, there is a lack of effective collaborative control between the chassis and the dual arms, which results in the robot being unable to quickly and accurately capture and stabilize objects.

[0007] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0008] The main purpose of the present invention is to provide an object grasping method, system, robot and computer-readable storage medium based on robot buffering, aiming to solve the problems in the prior art that the robot grasping process is not safe, cannot grasp objects smoothly and efficiently, and causes damage to the robot hardware.

[0009] To achieve the above object, the present invention provides an object grasping method based on robot buffering, and the object grasping method based on robot buffering comprises the following steps:

[0010] Monitor the motion trajectory of airborne objects in real time, and estimate and predict the flight status of airborne objects through dynamic models and filtering algorithms;

[0011] According to the flight state of the air-dropped object, the robot chassis, dual arms and end effector are coordinated to grasp the air-dropped object.

[0012] Combined with the end six-dimensional force sensor and admittance system, it simulates spring damping in multiple directions and absorbs the instantaneous impact force generated when the air-dropped object comes into contact with the robot during the process of grasping the air-dropped object.

[0013] Optionally, the object grasping method based on robot buffering, wherein the real-time monitoring of the motion trajectory of the air-dropped object and the estimation and prediction of the flight state of the air-dropped object through a dynamic model and a filtering algorithm are specifically as follows:

[0014] A dynamic capture system is used to capture the reflective markers attached to the airborne object to capture the object information. Combined with the parabolic state equation, the movement speed of the airborne object is estimated and the movement trajectory of the airborne object is predicted.

[0015] Optionally, in the object grasping method based on robot buffering, the estimating the motion speed of the air-dropped object and predicting the motion trajectory of the air-dropped object are specifically calculated as follows:

[0016]

[0017] Among them, k represents the current time, Indicates the estimated state of the object at the last moment, F k represents the motion matrix, Indicates the estimated state of the object at the last moment, B k represents the gravity matrix, u k represents gravity, Represents the estimated current object state, K k represents the Kalman estimate, z k represents the observation state, H k represents the observation matrix, P k|k-1 represents the covariance matrix, P k-1|k-1 represents the prior covariance matrix of the previous moment, T represents the transpose, Q k represents the state noise matrix, R k represents the observation noise matrix, t represents the estimated time, x0 represents the initial x-axis position in the world Cartesian coordinate system, y0 represents the initial y-axis position in the world Cartesian coordinate system, z0 represents the initial z-axis position in the world Cartesian coordinate system, υ0x represents the initial velocity of the x-axis in the world Cartesian coordinate system, υ 0y Indicates the initial velocity of the y-axis in the world Cartesian coordinate system, υ 0z Represents the initial velocity of the z-axis in the world Cartesian coordinate system, x(t) represents the x-axis position in the world Cartesian coordinate system, y(t) represents the y-axis position in the world Cartesian coordinate system, z(t) represents the z-axis position in the world Cartesian coordinate system, and g represents the acceleration due to gravity.

[0018] Optionally, the object grasping method based on robot buffering, wherein the grasping of the air-dropped object is carried out according to the flight state of the air-dropped object by utilizing the coordinated control between the robot chassis, dual arms and end effector, specifically comprises:

[0019] Based on the dynamic characteristics of the robot itself, the interaction model between the robot and the air-dropped object is established to calculate the optimal grasping trajectory with the shortest time;

[0020] The whole-body model predictive control technology is used to coordinate the movement of the chassis, robotic arm and end effector, and the robot is controlled to accurately track the motion trajectory of air-dropped objects for precise grasping.

[0021] Optionally, in the object grasping method based on robot buffering, the coordination of the movement of the chassis, the robot arm and the end effector is specifically:

[0022] According to the kinematic formula of the chassis and the kinematic equation of the robot arm, the joint angular velocity and the chassis movement speed are obtained by optimization.

[0023] Optionally, the object grasping method based on robot buffering, wherein the six-dimensional force sensor and the admittance system at the end are combined to simulate spring damping in multiple directions, and in the process of grasping the air-thrown object, the instantaneous impact force generated when the air-thrown object contacts the robot is absorbed, specifically:

[0024] Combined with the end six-dimensional force sensor and admittance system, spring damping in multiple directions is simulated to obtain additional simulated force for buffering. The additional simulated force is used to alleviate the impact force of air-thrown objects on the robot during the grasping process.

[0025] Optionally, in the object grasping method based on robot buffering, the additional simulated force is calculated as follows:

[0026]

[0027] Where M represents the inertia coefficient, represents the end displacement acceleration, B represents the damping coefficient, represents the end displacement velocity, K represents the elastic coefficient, △x represents the end displacement, X represents the end position, and F represents the additional simulated force.

[0028] In addition, to achieve the above-mentioned purpose, the present invention further provides an object grasping system based on robot buffer, wherein the object grasping system based on robot buffer comprises:

[0029] Real-time prediction module, used to monitor the motion trajectory of airborne objects in real time, and estimate and predict the flight status of airborne objects through dynamic models and filtering algorithms;

[0030] The whole body control module is used to grasp the air-dropped object by using the coordinated control between the robot chassis, dual arms and end effector according to the flight state of the air-dropped object;

[0031] The whole-body buffer module is used to combine the end six-dimensional force sensor and the admittance system to simulate spring damping in multiple directions. During the process of grasping air-dropped objects, it absorbs the instantaneous impact force generated when the air-dropped objects come into contact with the robot.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also provides a robot, wherein the robot includes: a memory, a processor, and a robot buffer-based object grasping program stored in the memory and executable on the processor, wherein the robot buffer-based object grasping program, when executed by the processor, implements the steps of the robot buffer-based object grasping method as described above.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a robot buffer-based object grasping program, and when the robot buffer-based object grasping program is executed by a processor, the steps of the robot buffer-based object grasping method as described above are implemented.

[0034] In the present invention, the motion trajectory of the air-dropped object is monitored in real time, and the flight state of the air-dropped object is estimated and predicted through a dynamic model and a filtering algorithm; according to the flight state of the air-dropped object, the coordinated control between the robot chassis, the arms and the end effector is used to grasp the air-dropped object; the end six-dimensional force sensor and the admittance system are combined to simulate spring damping in multiple directions, and in the process of grasping the air-dropped object, the instantaneous impact force generated when the air-dropped object contacts the robot is absorbed. By estimating and predicting the flight state of the object, the present invention can accurately predict the trajectory and dynamic behavior of the object, so that the robot can make timely adjustments before the object reaches the predetermined grasping position. By designing a spring damping system or energy-absorbing material, the instantaneous impact force generated when the object contacts the robot is effectively alleviated, which greatly improves the safety of the robot in the grasping process, ensures that the robot can grasp the object smoothly and efficiently, and reduces the loss of the robot hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of a preferred embodiment of the object grasping method based on robot buffering of the present invention;

[0036] Figure 2 It is a schematic diagram of three processes of a robot grasping an air-dropped object in a preferred embodiment of the object grasping method based on robot buffering of the present invention;

[0037] Figure 3 It is a schematic diagram of the principle process of grasping an object based on the coordinated control of two arms in a preferred embodiment of the object grasping method based on robot buffering of the present invention;

[0038] Figure 4 It is a structural diagram of a preferred embodiment of the object grasping system based on robot buffering of the present invention;

[0039] Figure 5 4 is a structural diagram of a preferred embodiment of the robot of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. 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.

[0041] The object grasping method based on robot buffering described in the preferred embodiment of the present invention is as follows: Figure 1 , Figure 2 and Figure 3 As shown, the object grasping method based on robot buffering includes the following steps:

[0042] Step S10: monitor the motion trajectory of the air-dropped object in real time, and estimate and predict the flight state of the air-dropped object through a dynamic model and filtering algorithm.

[0043] Specifically, Figure 2 As shown in the figure, the state of the object is estimated and predicted: the dynamic capture system is used to capture the reflective markers attached to the airborne object to capture the object information. The six-bit posture obtained through the object information is used to estimate the movement speed of the airborne object and predict the movement trajectory of the airborne object using a Kalman filter combined with a parabolic state equation; the flight state of the object can be accurately estimated and predicted through the dynamic model and filtering algorithm, and accurate spatiotemporal information can be provided for the subsequent grasping action; the specific calculation is:

[0044]

[0045] Among them, k represents the current time, Indicates the estimated state of the object at the last moment, Fk represents the motion matrix, Indicates the estimated state of the object at the last moment, B k represents the gravity matrix, u k represents gravity, Represents the estimated current object state, K k represents the Kalman estimate, z k represents the observation state, H k represents the observation matrix, P k|k-1 represents the covariance matrix, P k-1|k-1 represents the prior covariance matrix of the previous moment, T represents the transpose, Q k represents the state noise matrix, R k represents the observation noise matrix, t represents the estimated time, x0 represents the initial x-axis position in the world Cartesian coordinate system, y0 represents the initial y-axis position in the world Cartesian coordinate system, z0 represents the initial z-axis position in the world Cartesian coordinate system, υ 0x represents the initial velocity of the x-axis in the world Cartesian coordinate system, υ 0y Indicates the initial velocity of the y-axis in the world Cartesian coordinate system, υ 0z Represents the initial velocity of the z-axis in the world Cartesian coordinate system, x(t) represents the x-axis position in the world Cartesian coordinate system, y(t) represents the y-axis position in the world Cartesian coordinate system, z(t) represents the z-axis position in the world Cartesian coordinate system, and g represents the acceleration due to gravity.

[0046] Step S20: According to the flight state of the air-dropped object, the air-dropped object is grasped by utilizing the coordinated control among the robot chassis, dual arms and end effector.

[0047] Specifically, Figure 2 As shown in the figure, grasping optimization: based on the dynamic characteristics of the robot itself, an interaction model between the robot and the air-dropped object is established, and the optimal grasping trajectory with the shortest time is calculated; the whole-body model predictive control technology is used to coordinate the movement of the chassis, robotic arm and end effector, and the robot is controlled to accurately track the motion trajectory of the air-dropped object for precise grasping.

[0048] The robot not only relies on the grasping action of the upper limbs, but also combines the movement of the chassis to achieve the coordinated work of the robot chassis, arms and end effector through coordinated control; after the expected end position (Xref) is input, the robot system will optimize and solve the joint angular velocity and chassis movement speed according to the kinematic formula of the chassis and the kinematic equation of the robotic arm. The weight of the movement is determined by the energy consumption ratio (R matrix). Each joint and chassis direction has an energy weight term. If the weight of the robotic arm joint is high, the chassis will be moved as much as possible, and if the weight of the chassis is high, the robotic arm will be controlled as much as possible.

[0049] In the process of grasping flying objects, the present invention takes into account the dynamic characteristics of the robot itself, establishes an interaction model between the robot and the projectile (considering the end control position, converting the desired position into force to buffer the impulse of the robot to catch the object), and calculates the optimal grasping trajectory with the shortest time (considering the kinematic equation of the end and the kinematic equation of the object, and inputting the landing position and time of the object as the minimum constraints for optimization); adopts whole-body model predictive control technology (considering the whole-body model predictive control, not only considering the current state, but also combining the robot's kinematic equation to predict the next control, so that the system converges faster and more robustly), so that the robot can accurately track the motion trajectory of the object and achieve precise grasping.

[0050] Step S30: Combine the six-dimensional force sensor and the admittance system at the end to simulate spring damping in multiple directions, and absorb the instantaneous impact force generated when the air-dropped object contacts the robot during the process of grasping the air-dropped object.

[0051] Specifically, Figure 2 As shown, impulse buffering: combining the end six-dimensional force sensor and the admittance system, simulating spring damping in multiple directions, and obtaining additional simulated force for buffering. The additional simulated force is used to alleviate the impact force of air-thrown objects on the robot during the grasping process. It can effectively absorb the instantaneous impact force generated when the object contacts the robot, ensuring that the object is smoothly caught by the robot, thereby avoiding damage to the robot caused by collision.

[0052] The calculation of the additional simulated force is:

[0053]

[0054] Where M represents the inertia coefficient, represents the end displacement acceleration, B represents the damping coefficient, represents the end displacement velocity, K represents the elastic coefficient, △x represents the end displacement, X represents the end position, and F represents the additional simulated force.

[0055] The technical solution of the present invention not only realizes the robot's precise grasping of flying objects, but also significantly improves the stability and flexibility of the robot system. By predicting the object's flight trajectory in real time, the robot can accurately capture the flying object and complete the grasping action with the optimal trajectory; by coordinating the movement of the chassis and the robotic arm, the grasping accuracy and efficiency are improved, especially in a dynamic environment, the balance and stability of the system can be ensured; in addition, the buffer effectively reduces the impact force during the grasping process, protects the safety of the robot body, and prolongs the service life of the equipment. The elastic design of the system ensures smooth contact with the object during the grasping process, avoids damage to the grasped object, and further improves the success rate and reliability of the grasping.

[0056] The invention not only solves the accuracy and flexibility problems in traditional robot grasping tasks, but also significantly improves the robot's adaptability and work efficiency. Through this collaborative control method, the robot can perform more precise grasping operations in complex environments, especially when facing dynamic objects and complex movements, showing higher flexibility and responsiveness.

[0057] In addition, the present invention also innovatively achieves impact force buffering and ground adaptability. Through the coordinated work of the chassis and upper limbs, combined with full-body buffering, the robot can effectively mitigate the impact force generated when contacting an object. The movement of the chassis can help the robot absorb and disperse the impact force during the grasping process, avoiding excessive impact that causes the robot to be unstable or damaged. This technological innovation enables the robot to remain stable and protect its own structure and function when grasping heavy objects or high-speed objects.

[0058] In addition, the current design mainly focuses on a single dual-arm robot for grasping tasks. In the future, it is possible to consider multiple robots working together to improve the unloading efficiency through a networked multi-robot system. In this solution, multiple robots can be assigned tasks and grasped collaboratively in real time, further improving the efficiency and accuracy of the overall system.

[0059] Furthermore, if Figure 4 As shown, based on the above-mentioned object grasping method based on robot buffer, the present invention also provides an object grasping system based on robot buffer, wherein the object grasping system based on robot buffer includes:

[0060] A real-time prediction module 51 is used to monitor the motion trajectory of the airborne object in real time, and estimate and predict the flight state of the airborne object through a dynamic model and a filtering algorithm;

[0061] The whole body control module 52 is used to grasp the air-dropped object by utilizing the coordinated control among the robot chassis, the dual arms and the end effector according to the flight state of the air-dropped object;

[0062] The whole body buffer module 53 is used to combine the terminal six-dimensional force sensor and the admittance system to simulate spring damping in multiple directions, and absorb the instantaneous impact force generated when the air-dropped object contacts the robot during the process of grasping the air-dropped object.

[0063] In the present invention, the real-time prediction module 51 monitors the motion trajectory of the object in real time, and estimates and predicts the flight state of the object through a dynamic model and a filtering algorithm; the whole-body control module 52 utilizes the coordinated control between the robot chassis, arms and end effector. In the process of grasping air-dropped objects, the robot not only relies on the grasping action of the upper limbs, but also combines the movement of the chassis to ensure the balance and stability of the entire system; the whole-body buffer module 53 is used to alleviate the impact force caused by the object on the robot during the grasping process. By designing a spring damping system, it can effectively absorb the instantaneous impact force generated when the object contacts the robot.

[0064] like Figure 3 As shown in the figure, the robot system first estimates the physical state of the object and predicts its flight trajectory, and then passes the estimated state to the whole body control module; the planner simplifies the robot's joint calculations, uses the trajectory of the object's grasping end, and transmits it to the whole body control module (MPC). The controller then outputs the joint speed of each joint, and these speed signals are converted into torque commands for the underlying motors through the PD controller.

[0065] Technical effects of the present invention:

[0066] (1) Improve capture accuracy and success rate: The present invention uses a real-time object motion prediction module combined with advanced algorithms such as extended Kalman filtering to accurately predict the trajectory and dynamic behavior of an object, allowing the robot to make timely adjustments before the object reaches the predetermined grasping position.

[0067] (2) Expanded motion space and flexibility: The whole-body coordination control scheme (whole-body control module) of the present invention effectively integrates the movement of the robot chassis and arms, so that the robot can make full use of the movement of the chassis to adjust the position and posture of the upper limbs when performing grasping tasks.

[0068] (3) Impact force buffering and shock absorption: The full-body buffer module of the present invention effectively mitigates the instantaneous impact force generated when an object contacts the robot by designing a spring damping system or energy-absorbing material. Through the response of the full-body buffer module, the kinetic energy of the object can be effectively absorbed, avoiding structural damage or unstable operation of the robot due to excessive impact force.

[0069] The present invention greatly improves the safety of the robot during the grasping process, ensures that the robot can grasp objects smoothly and efficiently, and reduces the loss of robot hardware.

[0070] Furthermore, if Figure 5 As shown, based on the above-mentioned robot buffer-based object grasping method and system, the present invention also provides a robot accordingly, and the robot includes a processor 10, a memory 20 and a display 30. Figure 5Only some of the components of the robot are shown, but it should be understood that implementation of all of the components shown is not required, and more or fewer components may be implemented instead.

[0071] In some embodiments, the memory 20 may be an internal storage unit of the robot, such as a hard disk or memory of the robot. In other embodiments, the memory 20 may also be an external storage device of the robot, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the robot. Further, the memory 20 may also include both an internal storage unit of the robot and an external storage device. The memory 20 is used to store application software and various types of data installed on the robot, such as the program code of the installed robot. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores an object grasping program 40 based on robot buffering, and the object grasping program 40 based on robot buffering can be executed by the processor 10, thereby realizing the object grasping method based on robot buffering in the present application.

[0072] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the robot buffer-based object grasping method.

[0073] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the robot and to display a visual user interface. The processor 10, the memory 20, and the display 30 of the robot communicate with each other via a system bus.

[0074] In one embodiment, when the processor 10 executes the robot buffer-based object grasping program 40 in the memory 20 , the steps of the robot buffer-based object grasping method described above are implemented.

[0075] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a robot buffer-based object grasping program, and when the robot buffer-based object grasping program is executed by a processor, the steps of the robot buffer-based object grasping method as described above are implemented.

[0076] In summary, the present invention provides an object grasping method, system, robot and computer-readable storage medium based on robot buffering, the method comprising: real-time monitoring of the motion trajectory of air-dropped objects, and estimating and predicting the flight state of air-dropped objects through dynamic models and filtering algorithms; according to the flight state of air-dropped objects, using the coordinated control between the robot chassis, arms and end effectors to grasp the air-dropped objects; combining the end six-dimensional force sensor and admittance system to simulate spring damping in multiple directions, in the process of grasping air-dropped objects, absorbing the instantaneous impact force generated when the air-dropped objects contact the robot. The present invention can accurately predict the trajectory and dynamic behavior of the object by estimating and predicting the flight state of the object, so that the robot can make timely adjustments before the object reaches the predetermined grasping position, and effectively alleviate the instantaneous impact force generated when the object contacts the robot by designing a spring damping system or energy-absorbing material, greatly improving the safety of the robot grasping process, ensuring that the robot can grasp objects smoothly and efficiently, and reducing the loss of robot hardware.

[0077] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or robot including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or robot. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or robot including the element.

[0078] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0079] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for grasping an object based on robot buffering, characterized in that: The object grasping method based on robot buffering includes: Monitor the motion trajectory of airborne objects in real time, and estimate and predict the flight status of airborne objects through dynamic models and filtering algorithms; According to the flight state of the air-dropped object, the robot chassis, dual arms and end effector are coordinated to grasp the air-dropped object. Combined with the end six-dimensional force sensor and admittance system, it simulates spring damping in multiple directions and absorbs the instantaneous impact force generated when the air-dropped object comes into contact with the robot during the process of grasping the air-dropped object.

2. The object grasping method based on robot buffering according to claim 1 is characterized in that: The real-time monitoring of the motion trajectory of the air-dropped object and the estimation and prediction of the flight state of the air-dropped object through a dynamic model and a filtering algorithm are specifically as follows: A dynamic capture system is used to capture the reflective markers attached to the airborne object to capture the object information. Combined with the parabolic state equation, the movement speed of the airborne object is estimated and the movement trajectory of the airborne object is predicted.

3. The object grasping method based on robot buffering according to claim 2 is characterized in that: The estimated velocity of the air-dropped object and the predicted trajectory of the air-dropped object are specifically calculated as follows: Among them, k represents the current time, Indicates the estimated state of the object at the last moment, F k represents the motion matrix, Indicates the estimated state of the object at the last moment, B k represents the gravity matrix, u k represents gravity, Represents the estimated current object state, K k represents the Kalman estimate, z k represents the observation state, H k represents the observation matrix, P k|k-1 represents the covariance matrix, P k-1|k-1 represents the prior covariance matrix of the previous moment, represents transpose, Q k represents the state noise matrix, R k represents the observation noise matrix, t represents the estimated time, x0 represents the initial x-axis position in the world Cartesian coordinate system, y0 represents the initial y-axis position in the world Cartesian coordinate system, z0 represents the initial z-axis position in the world Cartesian coordinate system, v 0x Indicates the initial velocity of the x-axis in the world Cartesian coordinate system, v 0y Indicates the initial velocity of the y-axis in the world Cartesian coordinate system, v 0z Represents the initial velocity of the z-axis in the world Cartesian coordinate system, x(t) represents the x-axis position in the world Cartesian coordinate system, y(t) represents the y-axis position in the world Cartesian coordinate system, z(t) represents the z-axis position in the world Cartesian coordinate system, and g represents the acceleration due to gravity.

4. The object grasping method based on robot buffering according to claim 1 is characterized in that: The method of grabbing the air-dropped object by using the coordinated control among the robot chassis, the dual arms and the end effector according to the flight state of the air-dropped object specifically includes: Based on the dynamic characteristics of the robot itself, the interaction model between the robot and the air-dropped object is established to calculate the optimal grasping trajectory with the shortest time; The whole-body model predictive control technology is used to coordinate the movement of the chassis, robotic arm and end effector, and the robot is controlled to accurately track the motion trajectory of air-dropped objects for precise grasping.

5. The object grasping method based on robot buffering according to claim 4 is characterized in that: The coordination of the movement of the chassis, the robotic arm and the end effector is specifically as follows: According to the kinematic formula of the chassis and the kinematic equation of the robot arm, the joint angular velocity and the chassis movement speed are obtained by optimization.

6. The object grasping method based on robot buffering according to claim 1, characterized in that: The combined end six-dimensional force sensor and admittance system simulates spring damping in multiple directions to absorb the instantaneous impact force generated when the air-thrown object contacts the robot during the process of grabbing the air-thrown object. Specifically: Combined with the end six-dimensional force sensor and admittance system, spring damping in multiple directions is simulated to obtain additional simulated force for buffering. The additional simulated force is used to alleviate the impact force of air-thrown objects on the robot during the grasping process.

7. The object grasping method based on robot buffering according to claim 6 is characterized in that: The additional simulated force is calculated as: Where M represents the inertia coefficient, represents the end displacement acceleration, B represents the damping coefficient, represents the end displacement velocity, K represents the elastic coefficient, Δx represents the end displacement, X represents the end position, and F represents the additional simulated force.

8. An object grasping system based on robot buffer, characterized in that: The object grasping system based on robot buffering includes: Real-time prediction module, used to monitor the motion trajectory of airborne objects in real time, and estimate and predict the flight status of airborne objects through dynamic models and filtering algorithms; The whole body control module is used to grasp the air-dropped object by using the coordinated control between the robot chassis, dual arms and end effector according to the flight state of the air-dropped object; The whole-body buffer module is used to combine the end six-dimensional force sensor and the admittance system to simulate spring damping in multiple directions. During the process of grasping air-dropped objects, it absorbs the instantaneous impact force generated when the air-dropped objects come into contact with the robot.

9. A robot, characterized in that: The robot includes: a memory, a processor, and a robot buffer-based object grasping program stored in the memory and executable on the processor. When the robot buffer-based object grasping program is executed by the processor, the steps of the robot buffer-based object grasping method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a robot buffer-based object grasping program, and when the robot buffer-based object grasping program is executed by a processor, the steps of the robot buffer-based object grasping method according to any one of claims 1 to 7 are implemented.

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