Mobile robot multi-person cooperative interaction method and system based on magnetic force feedback

Abstract

The invention relates to the technical field of robot cooperative interaction, and discloses a mobile robot multi-person cooperative interaction method and system based on magnetic feedback, and the method comprises the steps: setting operation parameters, and selecting an operation mode; hand movement information of an operator is received through the interaction device and serves as operation information of the robot; the interaction device can move along with the hand of an operator; according to a force compensation algorithm, the feedback force transmitted to the hands of the operator by the interaction device is changed; a motion instruction is generated according to the operation information of the single operator or according to the operation information of the multiple operators, prediction simulation is conducted on the motion instruction, and the motion instruction passing prediction simulation verification is sent to the mobile robot to be executed. The mobile robot is used as a carrier of the magnetic field device to track the hand position of an operator in real time, the mixed reality device is combined to unify the user operation space and the feedback space, and the defect that the operation space is separated from the feedback space is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of robot collaborative interaction, and in particular to a mobile robot multi-person collaborative interaction method and system based on magnetic feedback. Background Art

[0002] According to whether contact occurs during interaction, human-robot interaction methods can generally be divided into contact methods and non-contact methods. Interaction devices provide an interaction channel between users and robots, usually including control buttons, joysticks, touch screens, keyboards, etc. Contact interaction methods are widely used due to their simple structure, easy control, and low latency. However, due to the limitations of the device's range of motion, direct contact operation methods have problems such as limited operating range, high learning costs for operators, and unnatural interaction methods.

[0003] Compared with contact interaction methods, non-contact interaction methods minimize restrictions on operators and increase the naturalness and experience of interaction. Non-contact interaction methods based on gestures, voice, etc. can maximize the naturalness of operation and the experience of operators, but most of these methods focus more on the operator's control of the robot and ignore the feedback generated by the robot during the interaction process, which ultimately leads to the separation of operation and feedback in the human-machine interaction process, affecting the accuracy of operation. Especially for the operation of mobile robots,

[0004] At present, most of the feedback methods of human-robot interaction rely heavily on visual feedback, lacking feedback methods of other channels. Moreover, single visual feedback can easily cause high cognitive load for operators, and long-term operation can easily lead to distraction of operators, resulting in inaccurate operation. Force feedback, as a form of tactile feedback, plays a very important role in human-robot interaction. It can enrich the feedback information and interaction channels of robots, and improve operation efficiency and accuracy. In the process of contact interaction, feedback force is mainly generated by joysticks and wearable devices, but the friction, mechanical hysteresis and other nonlinear noise interference in its mechanical structure greatly affect the sense of presence of interaction and reduce the interaction efficiency to a certain extent. Therefore, non-contact force feedback has become a popular choice for teleoperation force feedback. Common non-contact force feedback devices use air pressure and air jets to generate non-contact impedance force, thereby realizing non-contact force tactile perception. At the same time, among various non-contact force feedback devices, magnetic force has become the main research direction of non-contact force feedback interaction devices due to its strong stability, smooth and stable force field and the ability to generate strong force within a short distance. However, the force field range provided by a simple magnetic device is limited and cannot meet the operational requirements of large-scale interactions of mobile robots. Therefore, how to increase the interactive range of the magnetic device has become a hot research issue in magnetic-based force feedback devices.

[0005] In the process of human-robot interaction, in order to improve the operation efficiency, the operation process faces the situation that multiple people operate the same robot at the same time. In order to solve the problem of interaction rights allocation and multi-person information fusion in the process of multi-person collaborative operation, it is necessary to integrate and calculate the multi-person operation information and map it to the operation behavior of the same robot on the basis of ensuring data security and operation transparency. The mutual exclusion of interaction data and the allocation of interaction rights in the process of multi-person collaborative interaction are the main difficulties in multi-person information fusion operation.

[0006] The existing invention patent "A mobile robot remote control system, method and storage medium" with publication number CN117301059A discloses a mobile robot remote control system, method and storage medium in the field of robot remote control technology, aiming to improve the control efficiency of the mobile robot and the user experience. The system includes: a mobile host and a somatosensory controller and a mobile robot connected to the mobile host signal; the somatosensory controller is used to obtain gesture data within its own recognition range and transmit it to the mobile host; the mobile host is used to: determine the movement instruction corresponding to the received gesture data according to the received gesture data and the corresponding mapping relationship between the predetermined gesture data and the movement mode of the mobile robot, and transmit it to the mobile robot; the mobile robot is configured to move according to the received movement instruction.

[0007] The existing invention patent "A mobile robot control method based on mixed reality" with publication number CN117707171A involves the field of human-computer interaction technology, specifically a mobile robot control method based on mixed reality. The mobile robot is provided with a path interaction system, a radar-vision integrated ranging unit, a conversion control unit and a mobile control unit. The path interaction system is used to generate path information and is connected to the mobile control unit to control the movement of the mobile robot. The movement of the mobile robot is restricted. The radar-vision integrated ranging unit is connected to the mobile control unit through the conversion control unit to control the movement of the mobile robot. The radar-vision integrated ranging unit is used to obtain the distance between the mobile robot and the obstacle. When the mobile robot is blocked by controlling the preset path data, the mobile robot can be switched to the distance data between the obstacle for movement control through the conversion control unit.

[0008] The existing invention patent with publication number CN117325147A is "A robot remote operation method combining virtual-reality fusion and force feedback perception". The invention discloses a robot remote operation method combining virtual-reality fusion and force feedback perception. The robot remote operation method specifically includes the following operation steps: S1. An industrial computer is equipped at each end. The computers communicate through the TCP / IP network protocol and the WiFi network. The main station operation end computer and the force feedback device Touch and the head-mounted display are connected, and the integrated remote operation control interface can send task instructions to the server through button control; the remote operation method combining mixed reality with force feedback can improve the operation efficiency on the basis of ensuring the safety of robot operation, and is regarded as the future development direction of remote operation robots. The present invention proposes innovation from the technical route, based on the master-slave arm operation system, combined with mixed reality technology, and proposes a remote operation robot system implementation plan based on mixed reality + force feedback.

[0009] In summary, in view of the characteristics of large operating range, high operating precision requirements and stable force feedback in the process of multi-person collaborative interaction of mobile robots, in order to ensure system transparency and operational safety, the present invention combines mixed reality technology and force feedback technology to construct a mobile robot multi-person collaborative interaction method and system based on magnetic feedback. Summary of the invention

[0010] To solve the above technical problems, the present invention provides a mobile robot multi-person collaborative interaction method and system based on magnetic force feedback. In order to expand the force feedback space and generate multi-dimensional feedback force, the present invention proposes to use a mobile robot as a carrier of a magnetic field device to track the operator's hand position in real time, and unify the user's operation space and feedback space in combination with a mixed reality device, thereby solving the defect of separation of the operation space and feedback space in traditional interaction methods.

[0011] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0012] A mobile robot multi-person collaborative interaction method based on magnetic feedback comprises the following steps:

[0013] Set the operation parameters and select the operation mode; the operation parameters include the operation weight of the operator; the operation modes include: single-person operation of a single robot, multi-person collaborative information fusion operation, and multi-person cooperative operation of multiple robots;

[0014] receiving the operator's hand movement information as the robot's operation information through the interactive device; the interactive device can move along with the operator's hand movement; and changing the feedback force transmitted by the interactive device to the operator's hand according to the force compensation algorithm;

[0015] A motion instruction is generated according to the operation information of a single operator or a motion instruction is generated according to the operation information of multiple operators, a prediction simulation is performed on the motion instruction, and the motion instruction verified by the prediction simulation is sent to the mobile robot for execution.

[0016] Furthermore, when the operation mode selects the multi-person collaborative information fusion operation, the prediction simulation of the motion command specifically includes:

[0017] Obtain information on the nature of the task, the task objectives, and the execution process, collect information on the task area environment, and establish a model related to the work environment;

[0018] Using the Hololens 2 client, a mixed reality environment is created based on the mission nature information, mission target information, execution process information, and mission area environment information;

[0019] Build a computer server and Hololens 2 client, establish communication between multiple clients, and share the mixed reality operating environment;

[0020] The motion instructions generated according to the operation information and operation weights of multiple operators are mapped to the virtual robot in the prediction simulation software; if the virtual robot collides with the surrounding environment, the motion instructions are corrected to obtain the final motion instructions.

[0021] Furthermore, the generating of motion instructions according to the operation information of multiple operators specifically includes:

[0022] S21. Setting an operation weight for each operator;

[0023] S22, processing the acquired operation data of each operator to eliminate unreasonable operation data;

[0024] S23. Perform weighted calculation on the operation data of each operator according to the operation weight of the operator to obtain a motion instruction.

[0025] Furthermore, the interaction device can follow the movement of the operator's hand, specifically including:

[0026] S41, receiving hand movement information;

[0027] S42, allocating the movement distances of the trolley and the robotic arm by setting the allocation ratio and the movement limit range;

[0028] S43, transmitting the allocated data to the robot arm of the interactive device, performing inverse solution of the robot arm, and sending the motion data of the car to the industrial computer of the car;

[0029] S44. Control the movement of the trolley through the trolley mobile chassis control algorithm, and at the same time control the movement of the robotic arm, and based on the hand movement information, make the interactive device follow the operator when the operator moves.

[0030] Furthermore, the feedback force transmitted by the interactive device to the operator's hand is changed according to the force compensation algorithm, which specifically includes: the force compensation algorithm compensates for the feedback force by sensing the magnitude of the magnetic force based on the inverse solution of the robot arm and performing position offset.

[0031] A mobile robot multi-person collaborative interaction system based on magnetic feedback, comprising:

[0032] Parameter setting module: used to set operating parameters and select operating modes; operating parameters include the operating weight of the operator; operating modes include: single-person operation of a single robot, multi-person collaborative information fusion operation, and multi-person cooperative operation of multiple robots;

[0033] Interaction module: receiving the operator's hand movement information through the interaction device as the robot's operation information; the interaction device can follow the movement of the operator's hand; according to the force compensation algorithm, changing the feedback force transmitted by the interaction device to the operator's hand;

[0034] Control module: Generates motion instructions according to the operation information of a single operator or generates motion instructions according to the operation information of multiple operators, performs predictive simulation on the motion instructions, and sends the motion instructions verified by the predictive simulation to the mobile robot for execution.

[0035] Compared with the prior art, the beneficial technical effects of the present invention are:

[0036] 1. The present invention designs an interactive control method for mobile robots, which enables operators to effectively control and collaboratively manipulate mobile robots. When encountering complex operations and unexpected situations, the teleoperation system can ensure that the robot accurately executes the operator's instructions and provides real-time feedback so that the operator can understand the robot's status and environment. The integrated multi-operation process can avoid individual operator's misoperation to a certain extent and ensure the safety of the operating system.

[0037] 2. The present invention uses mixed reality (MR) technology in the human-computer interaction of the remote operation system. Mixed reality (MR) technology is a new interactive mode platform that perceives human natural behavior through multiple modalities, obtains multiple interactive channels such as human voice, gestures, and sight, and enables them to be selectively parallel and collaborative to achieve multi-channel, high-bandwidth, efficient, and unrestricted location human-computer interaction.

[0038] 3. The present invention designs a dedicated teleoperation interaction device and control method for a mobile robot operated by multiple people, and performs complex teleoperation tasks by sensing the interaction mode of human natural behavior through the interaction device. The present invention designs a method combining gesture and voice interaction to control the end of the mechanical arm of the mobile robot, which can achieve a more natural and humanized interaction mode.

[0039] 4. In view of the fact that current single-person remote-operated robots may fail to respond to emergencies and may misoperate, the present invention designs an interactive system for multi-person collaborative mobile robots to complete complex tasks. The present invention creates an environment that integrates virtual and real elements to provide a richer collaborative experience, enables real-time information sharing, and can observe the simulated and actual states of mobile robots, environmental information, and real-time information on task progress in a virtual reality environment. The present invention enables multiple operators to jointly operate the same mobile robot, perform tasks collaboratively, and avoid collisions and conflicts. In addition, based on the 5G communication protocol, multiple people can conduct real-time voice and video exchanges in the MR environment to discuss task progress and adjust strategies.

[0040] 5. Based on the MR platform, the present invention designs a combination of gesture and voice interaction to control the end of the mechanical arm of the bomb disposal robot, which can achieve a more natural and humanized interaction method. An interactive device and system for multi-person collaborative mobile robots based on magnetic feedback are designed, which are mainly divided into three modes: single-person stand-alone, multi-person collaborative information fusion operation, and multi-person cooperative operation of multiple robots. The multi-person collaborative interaction method and system of mobile robots based on magnetic feedback provide a large-scale, contactless remote operation interaction method.

[0041] 6. The present invention solves the technical problems existing in the prior art that the interactive equipment is too redundant, the operation process is complicated resulting in dependence on manual operation experience, and the interactive control effect is poor. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic diagram of a mobile robot multi-person collaborative interaction method based on magnetic feedback in an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of a process of controlling a mobile robot under a multi-person collaborative framework in an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of data flow interaction processing in a multi-person collaborative mode in an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram of a magnetic force-based interaction device in an embodiment of the present invention;

[0046] Figure 5A schematic diagram of data flow interaction processing of multi-person collaborative information fusion operation in an embodiment of the present invention;

[0047] Figure 6 It is a flow chart of the vehicle-arm coordination algorithm adopted in the embodiment of the present invention;

[0048] Figure 7 Schematic diagram of data interaction processing of an interactive system of a multi-person collaborative mobile robot based on magnetic feedback in an embodiment of the present invention;

[0049] Figure 8 It is a schematic diagram of the structure of the interactive device in an embodiment of the present invention.

[0050] In the figure: 1. Robotic arm control device; 11. Robotic arm controller; 12. Robotic arm; 13. Magnetic sensing device; 2. Trolley; 21. Trolley body; 22. Trolley positioning device; 23. Trolley wheel hub; 3. Base. DETAILED DESCRIPTION

[0051] A preferred embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0052] like Figure 1 As shown, a mobile robot multi-person collaborative interaction method based on magnetic feedback of the present invention comprises:

[0053] S1. Use the preset software to run the terminal device to run the preset software and obtain the single-person operation information;

[0054] S2. Set the operation parameters, set the operation weight of the operator and the frequency of command issuance, select the number of mobile robots to be collaboratively controlled, and analyze the information fusion operation mode of multi-person collaboration and the mode of multi-person cooperation to operate multiple robots;

[0055] S3. Obtain the operator's operation data, perform prediction simulation on the collaborative operation results, ensure the safety of robot operation, avoid collisions, and issue motion instructions after prediction simulation;

[0056] S4, the interactive device receives the hand movement information, follows the operator to move, and controls the offset of the magnetic feedback device based on the received information according to the force compensation algorithm, and changes the magnitude of the feedback force by changing the relative distance between the magnets;

[0057] S5. The mobile robot receives motion instructions and performs related actions.

[0058] The present invention designs a multi-person cooperative magnetic feedback interactive control method for mobile robots, so that multiple operators can effectively control the same mobile robot. When encountering complex operations and unexpected situations during task execution, the multi-person cooperative teleoperation system can comprehensively execute the instructions issued by the operators, ensure the safety of the robot's execution of actions, and provide real-time visual and force feedback so that the operators can understand the robot's status and environment.

[0059] like Figure 3 As shown, in the human-computer interaction of the remote operation system of the present invention, mixed reality (MR) is a new type of interaction platform, which recognizes human's natural behavior through multimodal perception technology, obtains multiple interaction channels such as voice, gesture, and line of sight, and enables it to selectively realize multi-channel, high-bandwidth, efficient and unrestricted location human-computer interaction behavior through parallelism and collaboration.

[0060] like Figure 2 As shown, in a more specific technical solution, step S2 specifically includes:

[0061] S21, obtaining information on the nature of the task, the task target, and the execution process, collecting information on the task area environment, and establishing a model related to the work environment;

[0062] S22. Using Hololens 2 development tools, create a mixed reality environment based on task nature information, task target information, execution process information, and task area environment information;

[0063] S23, build a computer server and Hololens 2 client, establish multiple client communications, and share the mixed reality operating environment;

[0064] S24, sending the Hololens 2 client's mobile robot guidance command to the prediction simulation software, wherein the mobile robot guidance command types mainly include: gesture command, voice command:

[0065] S25, at the prediction simulation end, receiving the motion instructions of multiple people and comprehensively calculating the corresponding motion results according to the set motion weight of each operator;

[0066] S26, after obtaining the calculation result of the multi-person collaborative operation, mapping the calculation result to the virtual robotic arm in the prediction simulation software, and controlling the robotic arm to perform motion simulation as the final control result;

[0067] S27. Correct the motion of the robotic arm in HoloLens 2 using the predicted simulation result, and send the corrected result to Hololens 2 to correct the motion of the robotic arm in Hololens 2 so that it is the same as the motion of the robotic arm in the predicted simulation software.

[0068] In a more specific technical solution, in step S3, in the multi-person collaborative information fusion operation mode, obtaining the operation data of the operator specifically includes:

[0069] S31, obtaining multi-person collaborative operation information and setting an operation weight for each operator;

[0070] S32. At the operation end, obtaining the operation data of the operator;

[0071] S33, performing data processing according to the operation weight and the operation data, eliminating unreasonable operation data, and performing weighted calculation on the screened data;

[0072] S34. Dynamically update operator operation data and weighted operation data.

[0073] Information fusion operation data stream interactive processing for multi-person collaboration, see Figure 5 .

[0074] The present invention designs a dedicated teleoperation interaction device and control method for mobile robots, and performs complex tasks and provides force feedback by sensing the interaction mode of human natural behavior through the interaction device. The present invention designs a method for controlling the end of the mechanical arm of the mobile robot in accordance with the mobile operation of the mobile robot, and can realize a teleoperation interaction process with a larger interaction range and a more natural interaction mode.

[0075] The interactive device includes a holographic glasses module, a virtual robot module, a real robot and a feedback unit module. The holographic glasses module is mainly used to locate the position of the operator's hand, recognize the operator's gestures, and control the interaction of the virtual scene and the rendering of related scenes; the virtual robot module is mainly used to calculate the robot kinematics in the virtual scene and control the position and posture of the virtual robot; the real robot control module realizes force-position hybrid control and control of the robot arm and force sensor; the feedback unit is used to calculate and solve the parameters related to the magnetic force, etc.

[0076] like Figure 8 As shown, the interactive device in this embodiment includes: a mechanical arm control device 1, a trolley 2, and a base 3. The mechanical arm control device 1 includes: a mechanical arm controller 11, a mechanical arm 12, and a magnetic sensing device 13; the trolley 2 includes: a trolley body 21, a trolley positioning device 22, and a trolley wheel hub 23.

[0077] like Figure 4 As shown, in a more specific technical solution, in step S4, the motion control process of the interactive device includes:

[0078] S41, receiving comprehensive hand motion information, wherein the hand motion information comes from processed weighted data;

[0079] S42, allocating the movement distances of the trolley and the robotic arm by setting the allocation ratio and the movement limit range;

[0080] S43. Use the vehicle-arm coordination algorithm to follow the operation position, and use the vehicle mobile chassis control algorithm to control the vehicle motion.

[0081] S44, obtain the end position information, and the robot arm performs inverse solution.

[0082] S45, mechanical arm force compensation offset.

[0083] In a more specific technical solution, in step S43, the step of controlling the movement of the trolley using the trolley mobile chassis control algorithm also includes: receiving the current position and the target position, planning the route through the position, identifying obstacles and automatically avoiding them.

[0084] For the specific process of the vehicle-arm coordination algorithm, see Figure 6 .

[0085] In a more specific technical solution, in step S44, the force compensation algorithm of the robotic arm compensates for the feedback force by sensing the magnetic force size and performing position offset based on the inverse solution of the robotic arm: the step of using the robotic arm force compensation algorithm to control the magnetic feedback of the interaction process also includes: anti-collision force compensation offset and force feedback offset.

[0086] Based on the MR platform, the present invention designs a method combining gesture and voice interaction to control the end of the mobile robot's mechanical arm, which can achieve a more natural and humanized interaction method. The interactive system of multi-person collaborative mobile robots is designed, which is mainly divided into three modes: single-person operation of a single robot, multi-person collaborative information fusion operation, and multi-person cooperative operation of multiple robots. Through multiple interactive modes, it can cope with the complex situations of bomb disposal tasks and dangerous environment rescue.

[0087] The system of the present invention corresponds to the method, and the technical solution applicable to the method is also applicable to the system. Figure 7 .

[0088] The magnetic component in the present invention can be static magnetic force or electromagnetic force; the trolley can be a differential trolley or other devices with a movable chassis.

[0089] In summary, the present invention provides a mobile robot multi-person collaborative interaction method and system based on magnetic feedback, the method comprising: using MR equipment and a computer to start up and run preset software; selecting a mode among a single person operating a single robot, a multi-person collaborative information fusion operation, and a multi-person cooperative operation of multiple robots; generating motion instructions to control the interactive device to perform hand tracking movements and mobile robot movements; predicting and simulating the motion instructions of the mobile robot in a motion simulation module; finally sending a verified control instruction to control the mobile robot; shutting down the device, and the task is completed. The present invention solves the problems of large operating range and low operating transparency during the remote operation of the mobile robot, invents a large-range interactive force feedback device based on magnetic feedback, and combines MR technology to perform overall perception and information reproduction of the remote operation process, while interacting more naturally with the slave robot through gestures and voice interaction.

[0090] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0091] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A mobile robot multi-person collaborative interaction method based on magnetic feedback, characterized in that: The following steps are involved: Set operating parameters and select operating mode; operating parameters include the operating weight of the operator; The operation modes include: single-person operation of a single robot, multi-person collaborative information fusion operation, and multi-person cooperative operation of multiple robots; receiving the operator's hand movement information as the robot's operation information through the interactive device; the interactive device can move along with the operator's hand movement; and changing the feedback force transmitted by the interactive device to the operator's hand according to the force compensation algorithm; A motion instruction is generated according to the operation information of a single operator or a motion instruction is generated according to the operation information of multiple operators, a prediction simulation is performed on the motion instruction, and the motion instruction verified by the prediction simulation is sent to the mobile robot for execution.

2. The mobile robot multi-person collaborative interaction method based on magnetic feedback according to claim 1, characterized in that: When the operation mode selects the multi-person collaborative information fusion operation, the prediction simulation of the motion command specifically includes: Obtain information on the nature of the task, the task objectives, and the execution process, collect information on the task area environment, and establish a model related to the work environment; Using the Hololens 2 client, a mixed reality environment is created based on the mission nature information, mission target information, execution process information, and mission area environment information; Build a computer server and Hololens 2 client, establish communication between multiple clients, and share the mixed reality operating environment; The motion instructions generated according to the operation information and operation weights of multiple operators are mapped to the virtual robot in the prediction simulation software; if the virtual robot collides with the surrounding environment, the motion instructions are corrected to obtain the final motion instructions.

3. The method for multi-person collaborative interaction of mobile robots based on magnetic feedback according to claim 1, characterized in that: The generating of motion instructions according to the operation information of multiple operators specifically includes: S21. Setting an operation weight for each operator; S22, processing the acquired operation data of each operator to eliminate unreasonable operation data; S23. Perform weighted calculation on the operation data of each operator according to the operation weight of the operator to obtain a motion instruction.

4. The method for multi-person collaborative interaction of mobile robots based on magnetic feedback according to claim 1, characterized in that: The interactive device can follow the movement of the operator's hand, specifically including: S41, receiving hand movement information; S42, allocating the movement distances of the trolley and the robotic arm by setting the allocation ratio and the movement limit range; S43, transmitting the allocated data to the robot arm of the interactive device, performing inverse solution of the robot arm, and sending the motion data of the car to the industrial computer of the car; S44. Control the movement of the trolley through the trolley mobile chassis control algorithm, and at the same time control the movement of the robotic arm, and based on the hand movement information, make the interactive device follow the operator when the operator moves.

5. The mobile robot multi-person collaborative interaction method based on magnetic feedback according to claim 1, characterized in that: The feedback force transmitted by the interactive device to the operator's hand is changed according to the force compensation algorithm, which specifically includes: the force compensation algorithm compensates for the feedback force by sensing the magnitude of the magnetic force based on the inverse solution of the robot arm and performing position offset.

6. A mobile robot multi-person collaborative interaction system based on magnetic feedback, characterized in that: include: Parameter setting module: used to set operating parameters and select operating mode; Operational parameters include operator's operational weight; The operation modes include: single-person operation of a single robot, multi-person collaborative information fusion operation, and multi-person cooperative operation of multiple robots; Interaction module: receiving the operator's hand movement information through the interaction device as the robot's operation information; the interaction device can follow the movement of the operator's hand; according to the force compensation algorithm, changing the feedback force transmitted by the interaction device to the operator's hand; Control module: Generates motion instructions according to the operation information of a single operator or generates motion instructions according to the operation information of multiple operators, performs predictive simulation on the motion instructions, and sends the motion instructions verified by the predictive simulation to the mobile robot for execution.

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