Immersive virtual reality training method and system

Abstract

The present invention is applicable to the field of immersive virtual reality application technology, and provides an immersive virtual reality training method and system, which obtains force feedback data and motion trajectory information of operating the initial practical handle and updating the practical handle in the initial IVR environment, and combines the feedback force change and motion trajectory deviation between the two to scientifically adjust the resistance value of the simulated handle in the IVR system, so that the handle feedback in the virtual reality training is closer to the operating feel of the actual device. Through this data-driven adjustment method, the adaptation speed of the operator after the equipment is replaced or updated can be effectively improved, the adaptation period can be shortened, and the accuracy and fluency of the operation can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of immersive virtual reality application, and in particular relates to an immersive virtual reality training method and system. Background Art

[0002] Industrial manipulators are widely used in automated production lines. The accuracy and stability of their operations directly affect production efficiency and product quality. Operators usually control the manipulators through handles to complete tasks such as grabbing, handling, and assembly. In order to improve operating skills, virtual reality (IVR) technology is used for training, allowing operators to perform simulation exercises in a virtual environment. However, with the continuous upgrading of equipment and handles, the new handles are often different from the old handles in design and force feedback characteristics, resulting in differences in the operating feel.

[0003] Existing IVR training methods are usually set based on the characteristics of the initial handle, including the strength of force feedback and the operating feel. When the handle is upgraded, the operating characteristics of the new handle do not match the resistance value of the simulated handle in the original IVR environment. This difference affects the operator's experience in training and prolongs the adaptation period to the new handle. Especially in scenarios that require high-precision control, such as grasping or precise assembly of tiny parts, inconsistent feel will increase the risk of misoperation.

[0004] After the handle is replaced, the current technology requires manual adjustment of the simulated handle resistance value in the IVR environment to match the operating characteristics of the new handle. This manual adjustment process is time-consuming and inefficient. Operators often need to judge the adjustment range of the resistance based on subjective experience, which makes it difficult to complete the adjustment quickly and accurately. In addition, the existing IVR system lacks automated analysis of the force feedback changes between the old and new handles, and is unable to generate adjustment parameters to adapt to the new handle. This results in a difference between the feel felt by the operator in virtual training and the actual operating feel of the new handle, which increases the adaptation time. Especially in high-precision operations, the mismatch in feel may lead to misoperation, which in turn affects production efficiency and quality. Summary of the invention

[0005] The purpose of the present invention is to provide an immersive virtual reality training method and system, aiming to solve the problems raised in the background technology.

[0006] The present invention is implemented as follows: an immersive virtual reality training method, the method comprising:

[0007] Acquire first force feedback data and second force feedback data of simulating handle operation of an initial practical handle and an updated practical handle in an initial IVR environment, and control a manipulator device to grasp a target object and complete a preset grasping stroke;

[0008] According to the first force feedback data and the second force feedback data, calculating the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle, and determining the first adjustment parameter value;

[0009] Determine the motion trajectory of the target object when the simulated handle operates the initial practical handle, and the motion trajectory of the target object when the practical handle is updated, calculate the average deviation between the two, and determine the second adjustment parameter value;

[0010] The resistance value of the simulated handle in the initial IVR environment is obtained, and is adjusted in combination with the first adjustment parameter value and the second adjustment parameter value to obtain a final resistance adjustment value.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the step of calculating, according to the first force feedback data and the second force feedback data, the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle, and determining the first adjustment parameter value includes:

[0012] According to the preset grasping stroke, several key grasping points are selected, and according to the first force feedback data and the second force feedback data, force feedback values ​​at these key grasping points are obtained when the simulated handle operates the initial practical handle and updates the practical handle;

[0013] Compare the difference in feedback force values ​​of each key grasping point when operating the updated practical handle and operating the initial practical handle, and calculate the change value of each key grasping point;

[0014] The average value of the change values ​​of all key grasping points is calculated to obtain the average feedback force change degree of the simulated handle in the entire preset grasping stroke, and the average feedback force change degree is set as the first adjustment parameter value.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in the preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the steps of determining the motion trajectory of the target object when the simulation handle operates the initial actual handle and the motion trajectory of the target object when the actual handle is updated, calculating the average deviation between the two, and determining the second adjustment parameter value include:

[0017] Use visual technology to capture and record the motion trajectory of the target object in the entire preset grasping stroke when the simulated handle operates the initial actual handle and updates the actual handle, including position changes and path curves;

[0018] Compare the two motion trajectories, measure the distance difference between them in space at the key grasping points, and calculate the deviation value of the motion trajectory at each key grasping point;

[0019] The deviation values ​​of all key grasping points are averaged to obtain the average deviation degree of the overall trajectory, and this result is converted into the second adjustment parameter value.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining the simulated handle resistance value in the initial IVR environment and adjusting it in combination with the first adjustment parameter value and the second adjustment parameter value to obtain the final resistance adjustment value include:

[0021] Get the simulated handle resistance value in the initial IVR environment;

[0022] Assigning appropriate weight coefficients to each first adjustment parameter value and second adjustment parameter value;

[0023] The weighted linear combination method is adopted to linearly calculate the simulated handle resistance value in the initial IVR environment and the weighted result of the first adjustment parameter value and the second adjustment parameter to obtain the final resistance adjustment value.

[0024] An immersive virtual reality training system, the system comprising: a data acquisition module, a first adjustment parameter value determination module, a second adjustment parameter value determination module and a final resistance adjustment value calculation module, wherein:

[0025] A data acquisition module is used to acquire first force feedback data and second force feedback data of simulating handle operation of an initial practical handle and updating the practical handle in an initial IVR environment, and controlling a manipulator device to grasp a target object and complete a preset grasping stroke;

[0026] A first adjustment parameter value determination module is used to calculate, based on the first force feedback data and the second force feedback data, the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle, and determine the first adjustment parameter value;

[0027] A second adjustment parameter value determination module is used to determine the motion trajectory of the target object when the simulated handle operates the initial actual handle and the motion trajectory of the target object when the actual handle is updated, calculate the average deviation between the two, and determine the second adjustment parameter value;

[0028] The final resistance adjustment value calculation module is used to obtain the simulated handle resistance value in the initial IVR environment, and adjust it in combination with the first adjustment parameter value and the second adjustment parameter value to obtain the final resistance adjustment value.

[0029] As a further limitation of the technical solution of the embodiment of the present invention, the first adjustment parameter value determination module specifically includes:

[0030] A force feedback value acquisition unit, used to select a number of key grasping points according to a preset grasping stroke, and acquire force feedback values ​​at these key grasping points when the simulated handle operates the initial actual handle and updates the actual handle according to the first force feedback data and the second force feedback data;

[0031] A change value calculation unit, used to compare the difference in feedback force values ​​of each key grasping point when operating the updated practical handle and operating the initial practical handle, and calculate the change value of each key grasping point;

[0032] The average value calculation unit is used to obtain the average value of the change values ​​of all key grasping points, obtain the average feedback force change degree of the simulated handle in the entire preset grasping stroke, and set it as the first adjustment parameter value.

[0033] As a further limitation of the technical solution of the embodiment of the present invention, the key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in the preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the second adjustment parameter value determination module specifically includes:

[0035] A motion trajectory acquisition unit, used to capture and record the motion trajectory of the target object in the entire preset grasping stroke when the simulated handle operates the initial actual handle and updates the actual handle by using visual technology, including position changes and path curves;

[0036] A deviation value calculation unit, used for comparing two motion trajectories, measuring the distance difference between them in space at key grasping points, and calculating the deviation value of the motion trajectory at each key grasping point;

[0037] The second adjustment parameter value determination unit is used to average the deviation values ​​of all key grasping points to obtain an average deviation degree of the overall trajectory, and convert the result into a second adjustment parameter value.

[0038] As a further limitation of the technical solution of the embodiment of the present invention, the final resistance adjustment value calculation module specifically includes:

[0039] A simulated handle resistance value obtaining unit, used to obtain a simulated handle resistance value in an initial IVR environment;

[0040] A weight coefficient allocation unit, used to allocate an appropriate weight coefficient to each first adjustment parameter value and second adjustment parameter value;

[0041] The final resistance adjustment value calculation unit is used to use a weighted linear combination method to linearly calculate the simulated handle resistance value in the initial IVR environment and the weighted result of the first adjustment parameter value and the second adjustment parameter to obtain the final resistance adjustment value.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By obtaining the force feedback data and motion trajectory information of the initial operation handle and the updated operation handle in the initial IVR environment, and combining the feedback force changes and motion trajectory deviations between the two, the resistance value of the simulated handle in the IVR system is scientifically adjusted, so that the handle feedback in virtual reality training is closer to the operating feel of the actual device. This data-driven adjustment method can effectively improve the adaptation speed of operators after equipment replacement or update, shorten the adaptation period, and improve the accuracy and fluency of operation.

[0044] By calculating the average feedback force change when operating the initial real handle and updating the real handle, the system can automatically generate the first adjustment parameter value to accurately reflect the difference in force feedback between the two. At the same time, by comparing the motion trajectories of the new and old handles during the grasping stroke, the system further quantifies the trajectory deviation between the two and determines the second adjustment parameter value. The introduction of these adjustment parameters ensures that the resistance feeling of the simulated handle in the virtual environment is closer to the real operation, thereby improving the realism and accuracy of the operation experience.

[0045] Compared with the prior art, the present invention avoids the subjectivity and inefficiency of manually adjusting the operating parameters, and optimizes the simulated handle resistance in an automated manner by comprehensively analyzing the changes in feedback force and motion trajectory. This method not only improves the training effect of operators in a virtual environment, but also reduces the risk of misoperation caused by operational differences after equipment updates, and has obvious practical value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0047] Figure 2 A flow chart of determining a first adjustment parameter value in the method provided in an embodiment of the present invention;

[0048] Figure 3 A flow chart of determining a second adjustment parameter value in the method provided in an embodiment of the present invention;

[0049] Figure 4 A flow chart of determining a final resistance adjustment value in a method provided in an embodiment of the present invention;

[0050] Figure 5 An application architecture diagram of a system provided by an embodiment of the present invention;

[0051] Figure 6 A structural block diagram of a first adjustment parameter value determination module in a system provided by an embodiment of the present invention;

[0052] Figure 7 A structural block diagram of a second adjustment parameter value determination module in a system provided by an embodiment of the present invention;

[0053] Figure 8 This is a structural block diagram of a final resistance adjustment value calculation module in a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] 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 accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0056] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0057] Specifically, an immersive virtual reality training method comprises the following steps:

[0058] Step S100, obtaining first force feedback data and second force feedback data of simulating handle operation of an initial practical handle and an updated practical handle in an initial IVR environment, and controlling a manipulator device to grasp a target object and complete a preset grasping stroke.

[0059] In the embodiment of the present invention, the initial IVR environment refers to the virtual reality training scene and system settings provided to the operator before the equipment is updated. This environment simulates various conditions and scenes in real operation, including the operation interface, force feedback mechanism, equipment movement, etc., so that the operator can be familiar with the operation mode of the manipulator equipment in the virtual environment. In this environment, the operator wears VR equipment (such as a head-mounted display and force feedback gloves) and uses a virtual simulation handle to control two actual handle models (including the old initial actual handle and the new updated actual handle).

[0060] "Using the simulation handle to control the initial operation handle and the updated operation handle, and controlling the manipulator to grab the target object and complete the preset grabbing stroke" means that in the IVR environment, the operator uses the virtual simulation handle to control the two handle models respectively to operate the manipulator. In this way, the operator can control the manipulator in the virtual environment, perform a series of actions such as grabbing, carrying and placing the target object, and complete the preset grabbing stroke.

[0061] During this process, the system will collect real-time operation data when the operator uses the simulated handle to control the initial actual handle and update the actual handle. These data mainly include force feedback values. The force feedback data specifically records the resistance applied by the simulated handle during operation, the change in feel, the reaction force of the manipulator during the grasping process, etc. These data can reflect the difference in force feedback felt by the operator when using the new and old handle models, including force changes, feel strength, operation precision, etc., providing an accurate data basis for subsequent force feedback adjustment and optimization.

[0062] The force feedback value is obtained through the cooperation of the sensors in the force feedback glove and the physics engine of the IVR system. The sensors record the pressure and direction changes applied by the hand during operation, and the physics engine simulates the physical interaction between the manipulator and the target object to calculate the resistance and reaction force during grasping. The calculated force feedback value is transmitted to the glove through a signal, allowing the operator to feel the real force. At the same time, this data is recorded by the system to form detailed force feedback data during the operation.

[0063] Furthermore, the immersive virtual reality training method further comprises the following steps:

[0064] Step S200, when the simulated handle is operated to update the actual handle, the average feedback force change degree of the actual handle in the entire preset grasping stroke is calculated based on the first force feedback data and the second force feedback data, and the first adjustment parameter value is determined.

[0065] Specifically, Figure 2 A flow chart for determining a first adjustment parameter value is shown.

[0066] The method of calculating the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle according to the first force feedback data and the second force feedback data, and determining the first adjustment parameter value specifically includes the following steps:

[0067] Step S201, selecting a number of key grasping points according to a preset grasping stroke, and obtaining force feedback values ​​at these key grasping points when the simulated handle operates the initial actual handle and updates the actual handle according to the first force feedback data and the second force feedback data;

[0068] Step S202, comparing the difference in feedback force values ​​of each key grasping point when operating the updated practical operating handle and operating the initial practical operating handle, and calculating the change value of each key grasping point;

[0069] Step S203, calculating the average value of the change values ​​of all key grasping points, obtaining the average feedback force change degree of the simulated handle in the entire preset grasping stroke, and setting it as the first adjustment parameter value.

[0070] In an embodiment of the present invention, the key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in a preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation.

[0071] The significance of selecting key grasping points is that they are the moments when the force sense changes most significantly when the manipulator device applies force to the target object in the preset grasping stroke. Selecting these points can more accurately capture the changes in force feedback during the operation, thereby providing efficient data support for subsequent analysis. These points usually include the initial contact point of grasping, the maximum force point of clamping during grasping, the balance point during movement, and the release point when placing the object. By focusing on these key moments, the differences between the new and old practical handles during the operation process can be more effectively reflected, redundant data interference can be avoided, and calculation efficiency and accuracy can be improved.

[0072] For the subsequent calculation of the first adjustment parameter value, the role of the key grasping points is to measure the overall trend of the force change when operating the updated practical handle and the initial practical handle through the force feedback difference of these points. The specific implementation process includes first obtaining the numerical difference in force feedback between the new and old handles at each key grasping point. Then, these differences are calculated and the change value of each key point is recorded. Finally, these change values ​​are averaged to obtain the average feedback force change degree during the entire grasping process. This average value is the first adjustment parameter value.

[0073] For example, in the preset grasping stroke, we select three key grasping points: the initial contact point, the clamping point in the middle of the grasping, and the placement point. At the initial contact point, the force feedback values ​​of the new and old handles are 5N and 6N respectively, with a difference of 1N; at the clamping point, the feedback values ​​of the new and old handles are 10N and 8N respectively, with a difference of 2N; at the placement point, the values ​​are 4N and 3N, with a difference of 1N. Averaging these difference values ​​(1N, 2N, 1N), the average value is 1.33N, which represents the average feedback force change of the simulated handle relative to the old handle when operating the new handle in the entire preset grasping stroke, and only its value is set as the first adjustment parameter value.

[0074] The first adjustment parameter value set in this way is based on objective data of feedback force changes in actual operation. By calculating the difference in force feedback between the new and old handles at multiple key moments and taking their average value, a comprehensive indicator can be provided to quantify the degree of force change of the new handle relative to the old handle during operation. Its significance lies in making subsequent resistance adjustments more targeted, ensuring that the simulated handle in IVR training can fit the real operating feel of the new handle, and helping operators experience force changes that are closer to reality in a virtual environment, thereby improving the effectiveness and efficiency of training.

[0075] Furthermore, the immersive virtual reality training method further comprises the following steps:

[0076] Step S300, determining the motion trajectory of the target object when the simulated handle operates the initial practical handle and the motion trajectory of the target object when the practical handle is updated, calculating the average deviation between the two, and determining the second adjustment parameter value.

[0077] Specifically, Figure 3 A flow chart for determining a second adjustment parameter value is shown.

[0078] The steps of determining the motion trajectory of the target object when the simulated handle operates the initial practical handle and the motion trajectory of the target object when the practical handle is updated, calculating the average deviation between the two, and determining the second adjustment parameter value specifically include the following steps:

[0079] Step S301, using visual technology to capture and record the motion trajectory of the target object in the entire preset grasping stroke when the simulated handle operates the initial actual handle and updates the actual handle, including position changes and path curves;

[0080] Step S302, comparing the two motion trajectories, measuring the distance difference between them in space at the key grasping points, and calculating the deviation value of the motion trajectory at each key grasping point;

[0081] Step S303, averaging the deviation values ​​of all key grasping points to obtain an average deviation degree of the entire trajectory, and converting the result into a second adjustment parameter value.

[0082] In an embodiment of the present invention, in the initial IVR environment, a camera array or a 3D vision sensor (such as a depth camera or a laser scanner) is used to capture in real time the motion process of the operator controlling the manipulator device to grasp the target object through a simulated handle. The process includes recording the spatial position of the manipulator and the target object, posture changes, and the path curve formed by the motion trajectory. These data will be recorded and two motion trajectories will be generated when the initial actual handle and the updated actual handle respectively control the manipulator device.

[0083] Next, the two trajectories are compared and analyzed. The focus of the comparison process is to select several key grasping points, which are the moments when the robot arm undergoes significant motion changes when grasping, moving, adjusting, and placing the target object. Using vision technology, the spatial distance difference between the two motion trajectories is measured at these key grasping points. This distance difference can be achieved by calculating the Euclidean distance between the corresponding points of the two trajectories in three-dimensional space. The distance difference of each key grasping point is recorded as the deviation value of the motion trajectory.

[0084] For example, suppose three key grasping points are selected in a preset grasping stroke: the initial contact point of the grasping, the point where the object moves to the halfway position, and the final placement point. At the initial contact point of the grasping, the position of the target object under the initial handle control is (1, 2, 3), and the position of the target object under the update handle control is (1.2, 2.1, 3.1). By calculating the difference in spatial distance between the two points, we get a deviation value of 0.22 meters. Similarly, when moving to the halfway position, the initial position is (3, 4, 5), the updated position is (3.1, 4.2, 5.1), and the deviation value is 0.26 meters; at the placement point position, the deviation value is 0.18 meters. The deviation values ​​of these key grasping points (0.22 meters, 0.26 meters, and 0.18 meters) are averaged to obtain an average deviation degree of 0.22 meters for the overall trajectory. Afterwards, only the value of the average deviation degree is selected as the second adjustment parameter value, which is used to adjust the force sense and trajectory feedback of the simulated handle in the IVR environment.

[0085] The key grasping points are of great significance for the setting of the second adjustment parameter value, because these points reflect the moments when the motion characteristics of the manipulator change most significantly during operation. By measuring the motion deviation between the old and new handles at these points, the subtle differences in the operation trajectory before and after the equipment is replaced can be captured more accurately, ensuring that the adjustment parameter value can reflect the actual changes in the operating handle. This method allows the IVR system to not only consider the overall movement trend when adjusting the force feedback, but also pay attention to the key change positions in the grasping stroke, making the adjusted operation experience more natural and smooth, further improving the training effect.

[0086] Furthermore, the immersive virtual reality training method further comprises the following steps:

[0087] Step S400: acquiring a resistance value of a simulated handle in an initial IVR environment, and adjusting the resistance value in combination with a first adjustment parameter value and a second adjustment parameter value to obtain a final resistance adjustment value.

[0088] Specifically, Figure 4 A flow chart for determining a final resistance adjustment value is shown.

[0089] The process of obtaining the simulated handle resistance value in the initial IVR environment and adjusting it in combination with the first adjustment parameter value and the second adjustment parameter value to obtain the final resistance adjustment value specifically includes the following steps:

[0090] Step S401, obtaining a simulated handle resistance value in an initial IVR environment;

[0091] Step S402, assigning appropriate weight coefficients to each first adjustment parameter value and second adjustment parameter value;

[0092] Step S403 , using a weighted linear combination method, linearly calculate the simulated handle resistance value in the initial IVR environment and the weighted result of the first adjustment parameter value and the second adjustment parameter to obtain a final resistance adjustment value.

[0093] In the embodiment of the present invention, in the initial IVR environment, the resistance value of the simulated handle refers to the basic resistance felt by the operator when using the handle to control the manipulator device. This resistance reflects the force sense simulated by the handle in the virtual environment, such as the clamping force when grabbing an object, the resistance of the object during movement, etc. This value is set based on the operating experience in the initial device state.

[0094] When making adjustments, we first use the two adjustment parameters obtained previously: the first adjustment parameter value and the second adjustment parameter value. The first adjustment parameter value is calculated from the difference in force feedback between the new and old handles, which quantifies the change in force feeling when using the new handle compared to the old handle. The second adjustment parameter value is derived from the degree of deviation between the new and old handles in the motion trajectory, reflecting the difference in the motion path of the handle when controlling the movement of the manipulator.

[0095] Next, we assign weights to these two parameters. Let's say that in this process, we think that the change in force is more important, so the weight assigned to the first adjustment parameter value is 70%, and the weight assigned to the second adjustment parameter value is 30%. These weights are used to determine the proportion of the impact of these two parameters on the final adjustment value.

[0096] We then combine the weighted results of these two parameters into a percentage change. Assume:

[0097] The first adjustment parameter value is 1.33, which means that the change in force feel between the new and old handles is relatively significant.

[0098] The second adjustment parameter value is 0.22, indicating a smaller change in trajectory deviation.

[0099] Now, we weight these two parameter values:

[0100] First, multiply the change value of 1.33 by its weight (70%), which gives it an impact value of about 0.931.

[0101] Then, multiplying 0.22 by its weight (30%) gives an impact value of approximately 0.066.

[0102] Adding the two results together gives an overall change, which represents the overall difference between the old and new controllers after taking into account the force and trajectory changes during operation. The result after adding is 0.997, which can be understood as the overall change between the old and new controllers, which is approximately equivalent to a 10% change ratio.

[0103] Next, we apply this change ratio to the initial analog handle resistance value. Assuming the initial analog handle resistance value is 10N, a 10% change ratio means that we need to increase the initial resistance value by 10%. The specific operation is to multiply 10N by (1+10%), that is, 10N×1.10, and finally get the final resistance adjustment value of 11N.

[0104] The key to the entire calculation process is that, through reasonable weight distribution and balance, we can scientifically adjust the resistance value of the simulated handle in the IVR environment according to the actual data of force feedback changes and trajectory deviations. This adjustment value makes the operator feel closer to the actual operation experience of the new handle in the virtual environment when the new handle replaces the old handle, so as to adapt to the new equipment faster and improve training efficiency and operation effect.

[0105] In summary, this immersive virtual reality (IVR) training method uses a data-driven approach to adjust the resistance of the simulated handle in the virtual training environment by accurately analyzing the operational differences between the new and old actual operating handles, ensuring that operators can quickly adapt to the new operating experience after the new equipment is updated. The entire solution includes: obtaining the resistance value of the simulated handle in the initial IVR environment, generating the first adjustment parameter and the second adjustment parameter by capturing the force feedback data and motion trajectory deviation data during the operation, and assigning weights to each parameter, and adjusting the final force feedback of the handle in combination with these weights to enhance the sense of reality in virtual training.

[0106] The advantage of this method is that it uses visual technology and sensor data to capture the real force feedback and motion trajectory during the operation, and quantifies the differences between the old and new handles during the operation. By weighted integration of these data, a comprehensive adjustment parameter is generated, so that the resistance adjustment of the simulated handle in the IVR environment can accurately reflect the changes in the operating feel. Compared with the traditional manual debugging method that relies on the subjective feedback of the operator, this solution is more scientific and objective, which can significantly reduce the debugging time and improve the accuracy of the training effect.

[0107] This technical solution solves the operational adaptability problem caused by equipment updates that are common in existing technologies. Traditional IVR training often lacks the ability to quickly adjust to the operating characteristics of different devices when facing equipment updates. When using new equipment, operators need to re-familiarize themselves with the new force sense and motion characteristics, resulting in a long adaptation period, a high rate of misoperation, and unsatisfactory training results. This solution automatically adjusts the operating parameters in the IVR by using comparative analysis of force feedback data and motion trajectory deviation data, allowing operators to experience a feel that is closer to the actual feeling of the new equipment in a virtual environment. In this way, the adaptation period after equipment updates is significantly shortened, the risk of misoperation due to unfamiliar operation is reduced, and the flexibility and adaptability of the virtual training system in practical applications are improved.

[0108] Furthermore, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0109] In another preferred embodiment of the present invention, an immersive virtual reality training system includes:

[0110] The data acquisition module 100 is used to acquire first force feedback data and second force feedback data of simulating handle operation of an initial practical handle and an updated practical handle in an initial IVR environment, and controlling a manipulator device to grasp a target object and complete a preset grasping stroke.

[0111] In the embodiment of the present invention, the initial IVR environment refers to the virtual reality training scene and system settings provided to the operator before the equipment is updated. This environment simulates various conditions and scenes in real operation, including the operation interface, force feedback mechanism, equipment movement, etc., so that the operator can be familiar with the operation mode of the manipulator equipment in the virtual environment. In this environment, the operator wears VR equipment (such as a head-mounted display and force feedback gloves) and uses a virtual simulation handle to control two actual handle models (including the old initial actual handle and the new updated actual handle).

[0112] "Using the simulation handle to control the initial operation handle and the updated operation handle, and controlling the manipulator to grab the target object and complete the preset grabbing stroke" means that in the IVR environment, the operator uses the virtual simulation handle to control the two handle models respectively to operate the manipulator. In this way, the operator can control the manipulator in the virtual environment, perform a series of actions such as grabbing, carrying and placing the target object, and complete the preset grabbing stroke.

[0113] During this process, the system will collect real-time operation data when the operator uses the simulated handle to control the initial actual handle and update the actual handle. These data mainly include force feedback values. The force feedback data specifically records the resistance applied by the simulated handle during operation, the change in feel, the reaction force of the manipulator during the grasping process, etc. These data can reflect the difference in force feedback felt by the operator when using the new and old handle models, including force changes, feel strength, operation precision, etc., providing an accurate data basis for subsequent force feedback adjustment and optimization.

[0114] The force feedback value is obtained through the cooperation of the sensors in the force feedback glove and the physics engine of the IVR system. The sensors record the pressure and direction changes applied by the hand during operation, and the physics engine simulates the physical interaction between the manipulator and the target object to calculate the resistance and reaction force during grasping. The calculated force feedback value is transmitted to the glove through a signal, allowing the operator to feel the real force. At the same time, this data is recorded by the system to form detailed force feedback data during the operation.

[0115] Furthermore, the immersive virtual reality training system also includes:

[0116] The first adjustment parameter value determination module 200 is used to calculate the average feedback force change degree of the initial actual operating handle in the entire preset grasping stroke when the simulated handle operation updates the actual operating handle based on the first force feedback data and the second force feedback data, and determine the first adjustment parameter value.

[0117] Specifically, Figure 6 It shows a structural block diagram of the first adjustment parameter value determination module 200 in the system provided by an embodiment of the present invention.

[0118] Among them, in the preferred implementation manner provided by the present invention, the first adjustment parameter value determination module 200 specifically includes:

[0119] The force feedback value acquisition unit 201 is used to select a number of key grasping points according to a preset grasping stroke, and acquire force feedback values ​​at these key grasping points when the simulated handle operates the initial actual handle and updates the actual handle according to the first force feedback data and the second force feedback data;

[0120] The change value calculation unit 202 is used to compare the difference in feedback force values ​​of each key grasping point when operating the updated practical handle and operating the initial practical handle, and calculate the change value of each key grasping point;

[0121] The average value calculation unit 203 is used to obtain the average value of the change values ​​of all key grasping points, obtain the average feedback force change degree of the simulated handle in the entire preset grasping stroke, and set it as the first adjustment parameter value.

[0122] The key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in the preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation.

[0123] In an embodiment of the present invention, the key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in a preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation.

[0124] The significance of selecting key grasping points is that they are the moments when the force sense changes most significantly when the manipulator device applies force to the target object in the preset grasping stroke. Selecting these points can more accurately capture the changes in force feedback during the operation, thereby providing efficient data support for subsequent analysis. These points usually include the initial contact point of grasping, the maximum force point of clamping during grasping, the balance point during movement, and the release point when placing the object. By focusing on these key moments, the differences between the new and old practical handles during the operation process can be more effectively reflected, redundant data interference can be avoided, and calculation efficiency and accuracy can be improved.

[0125] For the subsequent calculation of the first adjustment parameter value, the role of the key grasping points is to measure the overall trend of the force change when operating the updated practical handle and the initial practical handle through the force feedback difference of these points. The specific implementation process includes first obtaining the numerical difference in force feedback between the new and old handles at each key grasping point. Then, these differences are calculated and the change value of each key point is recorded. Finally, these change values ​​are averaged to obtain the average feedback force change degree during the entire grasping process. This average value is the first adjustment parameter value.

[0126] For example, in the preset grasping stroke, we select three key grasping points: the initial contact point, the clamping point in the middle of the grasping, and the placement point. At the initial contact point, the force feedback values ​​of the new and old handles are 5N and 6N respectively, with a difference of 1N; at the clamping point, the feedback values ​​of the new and old handles are 10N and 8N respectively, with a difference of 2N; at the placement point, the values ​​are 4N and 3N, with a difference of 1N. Averaging these difference values ​​(1N, 2N, 1N), the average value is 1.33N, which represents the average feedback force change of the simulated handle relative to the old handle when operating the new handle in the entire preset grasping stroke, and only its value is set as the first adjustment parameter value.

[0127] The first adjustment parameter value set in this way is based on objective data of feedback force changes in actual operation. By calculating the difference in force feedback between the new and old handles at multiple key moments and taking their average value, a comprehensive indicator can be provided to quantify the degree of force change of the new handle relative to the old handle during operation. Its significance lies in making subsequent resistance adjustments more targeted, ensuring that the simulated handle in IVR training can fit the real operating feel of the new handle, and helping operators experience force changes that are closer to reality in a virtual environment, thereby improving the effectiveness and efficiency of training.

[0128] Furthermore, the immersive virtual reality training system also includes:

[0129] The second adjustment parameter value determination module 300 is used to determine the target object motion trajectory when the simulated handle operates the initial actual handle and the target object motion trajectory when the actual handle is updated, calculate the average deviation between the two, and determine the second adjustment parameter value.

[0130] Specifically, Figure 7 It shows a structural block diagram of the second adjustment parameter value determination module 300 in the system provided by an embodiment of the present invention.

[0131] Among them, in the preferred implementation manner provided by the present invention, the second adjustment parameter value determination module 300 specifically includes:

[0132] The motion trajectory acquisition unit 301 is used to capture and record the motion trajectory of the target object in the entire preset grasping stroke when the simulated handle operates the initial actual handle and updates the actual handle by using visual technology, including position changes and path curves;

[0133] A deviation value calculation unit 302 is used to compare two motion trajectories, measure the distance difference between them in space at key grasping points, and calculate the deviation value of the motion trajectory at each key grasping point;

[0134] The second adjustment parameter value determining unit 303 is used to average the deviation values ​​of all key grasping points to obtain an average deviation degree of the entire trajectory, and convert the result into a second adjustment parameter value.

[0135] In an embodiment of the present invention, in the initial IVR environment, a camera array or a 3D vision sensor (such as a depth camera or a laser scanner) is used to capture in real time the motion process of the operator controlling the manipulator device to grasp the target object through a simulated handle. The process includes recording the spatial position of the manipulator and the target object, posture changes, and the path curve formed by the motion trajectory. These data will be recorded and two motion trajectories will be generated when the initial actual handle and the updated actual handle respectively control the manipulator device.

[0136] Next, the two trajectories are compared and analyzed. The focus of the comparison process is to select several key grasping points, which are the moments when the robot arm undergoes significant motion changes when grasping, moving, adjusting, and placing the target object. Using vision technology, the spatial distance difference between the two motion trajectories is measured at these key grasping points. This distance difference can be achieved by calculating the Euclidean distance between the corresponding points of the two trajectories in three-dimensional space. The distance difference of each key grasping point is recorded as the deviation value of the motion trajectory.

[0137] For example, suppose three key grasping points are selected in a preset grasping stroke: the initial contact point of the grasping, the point where the object moves to the halfway position, and the final placement point. At the initial contact point of the grasping, the position of the target object under the initial handle control is (1, 2, 3), and the position of the target object under the update handle control is (1.2, 2.1, 3.1). By calculating the difference in spatial distance between the two points, we get a deviation value of 0.22 meters. Similarly, when moving to the halfway position, the initial position is (3, 4, 5), the updated position is (3.1, 4.2, 5.1), and the deviation value is 0.26 meters; at the placement point position, the deviation value is 0.18 meters. The deviation values ​​of these key grasping points (0.22 meters, 0.26 meters, and 0.18 meters) are averaged to obtain an average deviation degree of 0.22 meters for the overall trajectory. Afterwards, only the value of the average deviation degree is selected as the second adjustment parameter value, which is used to adjust the force sense and trajectory feedback of the simulated handle in the IVR environment.

[0138] The key grasping points are of great significance for the setting of the second adjustment parameter value, because these points reflect the moments when the motion characteristics of the manipulator change most significantly during operation. By measuring the motion deviation between the old and new handles at these points, the subtle differences in the operation trajectory before and after the equipment is replaced can be captured more accurately, ensuring that the adjustment parameter value can reflect the actual changes in the operating handle. This method allows the IVR system to not only consider the overall movement trend when adjusting the force feedback, but also pay attention to the key change positions in the grasping stroke, making the adjusted operation experience more natural and smooth, further improving the training effect.

[0139] Furthermore, the immersive virtual reality training system also includes:

[0140] The final resistance adjustment value calculation module 400 is used to obtain the resistance value of the simulated handle in the initial IVR environment, and adjust it in combination with the first adjustment parameter value and the second adjustment parameter value to obtain the final resistance adjustment value.

[0141] Specifically, Figure 8 It shows a structural block diagram of the final resistance adjustment value calculation module 400 in the system provided by an embodiment of the present invention.

[0142] Among them, in the preferred embodiment provided by the present invention, the final resistance adjustment value calculation module 400 specifically includes:

[0143] The simulated handle resistance value obtaining unit 401 is used to obtain the simulated handle resistance value in the initial IVR environment;

[0144] A weight coefficient allocation unit 402, configured to allocate an appropriate weight coefficient to each first adjustment parameter value and second adjustment parameter value;

[0145] The final resistance adjustment value calculation unit 403 is used to perform linear calculation on the simulated handle resistance value in the initial IVR environment and the weighted result of the first adjustment parameter value and the second adjustment parameter in a weighted linear combination manner to obtain the final resistance adjustment value.

[0146] In the embodiment of the present invention, in the initial IVR environment, the resistance value of the simulated handle refers to the basic resistance felt by the operator when using the handle to control the manipulator device. This resistance reflects the force sense simulated by the handle in the virtual environment, such as the clamping force when grabbing an object, the resistance of the object during movement, etc. This value is set based on the operating experience in the initial device state.

[0147] When making adjustments, we first use the two adjustment parameters obtained previously: the first adjustment parameter value and the second adjustment parameter value. The first adjustment parameter value is calculated from the difference in force feedback between the new and old handles, which quantifies the change in force feeling when using the new handle compared to the old handle. The second adjustment parameter value is derived from the degree of deviation between the new and old handles in the motion trajectory, reflecting the difference in the motion path of the handle when controlling the movement of the manipulator.

[0148] Next, we assign weights to these two parameters. Let's say that in this process, we think that the change in force is more important, so the weight assigned to the first adjustment parameter value is 70%, and the weight assigned to the second adjustment parameter value is 30%. These weights are used to determine the proportion of the impact of these two parameters on the final adjustment value.

[0149] We then combine the weighted results of these two parameters into a percentage change. Assume:

[0150] The first adjustment parameter value is 1.33, which means that the change in force feel between the new and old handles is relatively significant.

[0151] The second adjustment parameter value is 0.22, indicating a smaller change in trajectory deviation.

[0152] Now, we weight these two parameter values:

[0153] First, multiply the change value of 1.33 by its weight (70%), which gives it an impact value of about 0.931.

[0154] Then, multiplying 0.22 by its weight (30%) gives an impact value of approximately 0.066.

[0155] Adding the two results together gives an overall change, which represents the overall difference between the old and new controllers after taking into account the force and trajectory changes during operation. The result after adding is 0.997, which can be understood as the overall change between the old and new controllers, which is approximately equivalent to a 10% change ratio.

[0156] Next, we apply this change ratio to the initial analog handle resistance value. Assuming the initial analog handle resistance value is 10N, a 10% change ratio means that we need to increase the initial resistance value by 10%. The specific operation is to multiply 10N by (1+10%), that is, 10N×1.10, and finally get the final resistance adjustment value of 11N.

[0157] The key to the entire calculation process is that, through reasonable weight distribution and balance, we can scientifically adjust the resistance value of the simulated handle in the IVR environment according to the actual data of force feedback changes and trajectory deviations. This adjustment value makes the operator feel closer to the actual operation experience of the new handle in the virtual environment when the new handle replaces the old handle, so as to adapt to the new equipment faster and improve training efficiency and operation effect.

[0158] In summary, this immersive virtual reality (IVR) training method uses a data-driven approach to adjust the resistance of the simulated handle in the virtual training environment by accurately analyzing the operational differences between the new and old actual operating handles, ensuring that operators can quickly adapt to the new operating experience after the new equipment is updated. The entire solution includes: obtaining the resistance value of the simulated handle in the initial IVR environment, generating the first adjustment parameter and the second adjustment parameter by capturing the force feedback data and motion trajectory deviation data during the operation, and assigning weights to each parameter, and adjusting the final force feedback of the handle in combination with these weights to enhance the sense of reality in virtual training.

[0159] The advantage of this method is that it uses visual technology and sensor data to capture the real force feedback and motion trajectory during the operation, and quantifies the differences between the old and new handles during the operation. By weighted integration of these data, a comprehensive adjustment parameter is generated, so that the resistance adjustment of the simulated handle in the IVR environment can accurately reflect the changes in the operating feel. Compared with the traditional manual debugging method that relies on the subjective feedback of the operator, this solution is more scientific and objective, which can significantly reduce the debugging time and improve the accuracy of the training effect.

[0160] This technical solution solves the operational adaptability problem caused by equipment updates that are common in existing technologies. Traditional IVR training often lacks the ability to quickly adjust to the operating characteristics of different devices when facing equipment updates. When using new equipment, operators need to re-familiarize themselves with the new force sense and motion characteristics, resulting in a long adaptation period, a high rate of misoperation, and unsatisfactory training results. This solution automatically adjusts the operating parameters in the IVR by using comparative analysis of force feedback data and motion trajectory deviation data, allowing operators to experience a feel that is closer to the actual feeling of the new equipment in a virtual environment. In this way, the adaptation period after equipment updates is significantly shortened, the risk of misoperation due to unfamiliar operation is reduced, and the flexibility and adaptability of the virtual training system in practical applications are improved.

[0161] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0162] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0163] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An immersive virtual reality training method, characterized in that: The method comprises: Acquire first force feedback data and second force feedback data of simulating handle operation of an initial practical handle and an updated practical handle in an initial IVR environment, and control a manipulator device to grasp a target object and complete a preset grasping stroke; According to the first force feedback data and the second force feedback data, calculating the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle, and determining the first adjustment parameter value; The steps of calculating, according to the first force feedback data and the second force feedback data, the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle, and determining the first adjustment parameter value include: According to the preset grasping stroke, several key grasping points are selected, and according to the first force feedback data and the second force feedback data, force feedback values ​​at these key grasping points are obtained when the simulated handle operates the initial practical handle and updates the practical handle; Compare the difference in feedback force values ​​of each key grasping point when operating the updated practical handle and operating the initial practical handle, and calculate the change value of each key grasping point; Calculate the average value of the change values ​​of all key grasping points to obtain the average feedback force change degree of the simulated handle in the entire preset grasping stroke, and set it as the first adjustment parameter value; The key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in the preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation; The significance of selecting key grasping points is that they are the moments when the force sense changes most significantly during the process of the manipulator device applying force to the target object in the preset grasping stroke. Selecting these points can more accurately capture the force feedback changes during the operation. Key grasping points include the initial contact point of grasping, the maximum force point of clamping during grasping, the balance point during movement, and the release point when placing the object. By focusing on these key contact or operation moments, the differences between the new and old practical handles during the operation process can be more effectively reflected to avoid redundant data interference; Determine the motion trajectory of the target object when the simulated handle operates the initial practical handle, and the motion trajectory of the target object when the practical handle is updated, calculate the average deviation between the two, and determine the second adjustment parameter value; The steps of determining the motion trajectory of the target object when the simulation handle operates the initial practical handle and the motion trajectory of the target object when the practical handle is updated, calculating the average deviation between the two, and determining the second adjustment parameter value include: Use visual technology to capture and record the motion trajectory of the target object in the entire preset grasping stroke when the simulated handle operates the initial actual handle and updates the actual handle, including position changes and path curves; Compare the two motion trajectories, measure the distance difference between them in space at the key grasping points, and calculate the deviation value of the motion trajectory at each key grasping point; The deviation values ​​of all key grasping points are averaged to obtain the average deviation degree of the overall trajectory, and the result is converted into the second adjustment parameter value; The resistance value of the simulated handle in the initial IVR environment is obtained, and is adjusted in combination with the first adjustment parameter value and the second adjustment parameter value to obtain a final resistance adjustment value.

2. The immersive virtual reality training method according to claim 1, characterized in that: The steps of obtaining the simulated handle resistance value in the initial IVR environment and adjusting it in combination with the first adjustment parameter value and the second adjustment parameter value to obtain the final resistance adjustment value include: Get the simulated handle resistance value in the initial IVR environment; Assigning appropriate weight coefficients to each first adjustment parameter value and second adjustment parameter value; The weighted linear combination method is adopted to linearly calculate the simulated handle resistance value in the initial IVR environment and the weighted result of the first adjustment parameter value and the second adjustment parameter to obtain the final resistance adjustment value.

3. An immersive virtual reality training system, characterized in that: The system comprises: a data acquisition module, a first adjustment parameter value determination module, a second adjustment parameter value determination module and a final resistance adjustment value calculation module, wherein: A data acquisition module is used to acquire first force feedback data and second force feedback data of simulating handle operation of an initial practical handle and updating the practical handle in an initial IVR environment, and controlling a manipulator device to grasp a target object and complete a preset grasping stroke; A first adjustment parameter value determination module is used to calculate, based on the first force feedback data and the second force feedback data, the average feedback force change degree of the actual operating handle in the entire preset grasping stroke relative to the initial actual operating handle when the simulated handle is operated to update the actual operating handle, and determine the first adjustment parameter value; The first adjustment parameter value determination module specifically includes: A force feedback value acquisition unit, used to select a number of key grasping points according to a preset grasping stroke, and acquire force feedback values ​​at these key grasping points when the simulated handle operates the initial actual handle and updates the actual handle according to the first force feedback data and the second force feedback data; A change value calculation unit, used to compare the difference in feedback force values ​​of each key grasping point when operating the updated practical handle and operating the initial practical handle, and calculate the change value of each key grasping point; An average value calculation unit is used to calculate the average value of the change values ​​of all key grasping points, obtain the average feedback force change degree of the simulated handle in the entire preset grasping stroke, and set it as the first adjustment parameter value; The key grasping point refers to the key contact or operation moment in the process of the manipulator device applying force to the target object in the preset grasping stroke. The key grasping point can reflect the key change position of the force change during the operation; The significance of selecting key grasping points is that they are the moments when the force sense changes most significantly during the process of the manipulator device applying force to the target object in the preset grasping stroke. Selecting these points can more accurately capture the force feedback changes during the operation. Key grasping points include the initial contact point of grasping, the maximum force point of clamping during grasping, the balance point during movement, and the release point when placing the object. By focusing on these key contact or operation moments, the differences between the new and old practical handles during the operation process can be more effectively reflected to avoid redundant data interference; A second adjustment parameter value determination module is used to determine the motion trajectory of the target object when the simulated handle operates the initial actual handle and the motion trajectory of the target object when the actual handle is updated, calculate the average deviation between the two, and determine the second adjustment parameter value; The second adjustment parameter value determination module specifically includes: A motion trajectory acquisition unit, used to capture and record the motion trajectory of the target object in the entire preset grasping stroke when the simulated handle operates the initial actual handle and updates the actual handle by using visual technology, including position changes and path curves; A deviation value calculation unit, used for comparing two motion trajectories, measuring the distance difference between them in space at key grasping points, and calculating the deviation value of the motion trajectory at each key grasping point; A second adjustment parameter value determination unit is used to average the deviation values ​​of all key grasping points to obtain an average deviation degree of the overall trajectory, and convert the result into a second adjustment parameter value; The final resistance adjustment value calculation module is used to obtain the simulated handle resistance value in the initial IVR environment, and adjust it in combination with the first adjustment parameter value and the second adjustment parameter value to obtain the final resistance adjustment value.

4. The immersive virtual reality training system according to claim 3, characterized in that: The final resistance adjustment value calculation module specifically includes: A simulated handle resistance value obtaining unit, used to obtain a simulated handle resistance value in an initial IVR environment; A weight coefficient allocation unit, used to allocate an appropriate weight coefficient to each first adjustment parameter value and second adjustment parameter value; The final resistance adjustment value calculation unit is used to use a weighted linear combination method to linearly calculate the simulated handle resistance value in the initial IVR environment and the weighted result of the first adjustment parameter value and the second adjustment parameter to obtain the final resistance adjustment value.

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