An embodied intelligent decision-making control method and system based on six-dimensional force sensor

Abstract

The present application proposes an embodied intelligent decision-making and control method and system based on a six-dimensional force sensor, which belongs to the field of embodied intelligent technology. The method includes: when the robotic arm is in the task execution state, the six-dimensional force sensor collects the force signal of the end effector of the robotic arm in real time; extracts the vertical force from the force signal; calculates the mass of the object grasped by the robotic arm in real time based on the vertical force; judges the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction; completes the robotic arm motion planning instruction based on the state of the robotic arm, and returns to execute the next robotic arm motion planning instruction. The present application can accurately measure the load mass of the robotic arm, effectively making up for the shortcomings of the existing embodied intelligent system with poor accuracy and delayed response.

Description

Technical Field

[0001] The present invention belongs to the field of embodied intelligence technology, and specifically relates to an embodied intelligence decision-making control method and system based on a six-dimensional force sensor. Background Art

[0002] In today's era of rapid technological development, embodied intelligence, as a cutting-edge direction in the field of artificial intelligence with great potential and challenges, aims to give intelligent entities the comprehensive capabilities of accurate perception, efficient decision-making, and flexible action in real physical environments, thereby achieving smooth interaction with the surrounding environment.

[0003] In the technological landscape of embodied intelligence, accurate force perception plays a key role in enabling intelligent entities to perform tasks. Six-axis force sensors, as key components of force sensing, can measure force and torque components in three directions and are widely used in numerous scenarios, including industrial production and robotic operations. For example, in industrial manufacturing, robotic arms utilize six-axis force sensors to perform complex assembly and material handling tasks, adjusting their movements based on force feedback to ensure operational precision.

[0004] However, current embodied intelligence applications based on six-dimensional force sensors have significant drawbacks. For one thing, the measurement accuracy of existing six-dimensional force sensors is easily affected by environmental factors such as temperature fluctuations and electromagnetic interference, leading to deviations in the measurement data and, in turn, misjudging the forces applied to the intelligent entity. When a robotic arm grasps objects of varying materials and shapes, inaccuracies in the force sensors often result in inappropriate gripping force. A tight grip can damage the object, while a loose grip can render the grip unstable.

[0005] Furthermore, in complex operations, there's a lack of efficient algorithms to accurately translate the data collected by six-dimensional force sensors into a judgment of an object's state. For example, when a robotic arm assembles parts, even if the six-dimensional force sensor captures force and torque information, algorithmic limitations make it difficult to accurately determine whether the part has been correctly installed and whether the required assembly force has been achieved. This makes it difficult to ensure assembly quality, impacting production efficiency and product quality.

[0006] Furthermore, in embodied intelligence systems, the ability to determine whether a model has successfully completed a given action is crucial. Within its architecture, behavioral execution and judgment and decision-making capabilities are analogous to the human cerebellum and cerebrum, respectively, and their coordinated operation is crucial. However, existing six-dimensional force sensor technology has shortcomings in determining action completion. Even if an action can be executed based on force feedback, it is impossible to determine with certainty whether the action meets the intended goal, significantly limiting the overall effectiveness of the system. These issues are further amplified in large-scale application scenarios such as industrial production, severely hindering the practical promotion and application of embodied intelligence. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, this application proposes an embodied intelligent decision-making control method and system based on a six-dimensional force sensor.

[0008] In the first aspect, the present application proposes an embodied intelligent decision-making and control method based on a six-dimensional force sensor, comprising:

[0009] Step S1: Generate robot arm motion planning instructions according to preset task objectives;

[0010] Step S2: driving the manipulator into a task execution state according to the manipulator motion planning instruction;

[0011] Step S3: When the manipulator is in the task execution state, the six-dimensional force sensor collects the force signal of the end effector of the manipulator in real time;

[0012] Step S4: extracting the vertical force from the force signal;

[0013] Step S5: Calculating the mass of the object grasped by the robotic arm in real time based on the vertical force;

[0014] Step S6: According to the mass of the object grasped by the robot arm and the robot arm motion planning instruction, the state of the robot arm is judged in real time. According to the state of the robot arm, the robot arm motion planning instruction is completed, and the process returns to step S1 to execute the next robot arm motion planning instruction.

[0015] Based on the vertical force, the mass of the object grasped by the robotic arm is calculated in real time using the following formula:

[0016] ;

[0017] Where m is the mass of the object grasped by the robotic arm, is the vertical force, is the robot's own gravity, is the offset caused by other interference forces, is the acceleration due to gravity, It is the acceleration in directions other than the direction of movement of the end of the robot arm during the movement of the robot arm.

[0018] The states of the robotic arm include: a robotic arm picked up state, a robotic arm idle state, and a robotic arm lowered state.

[0019] The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes:

[0020] When the robot arm motion planning instruction is a grasping instruction, if the mass of the object grasped by the robot arm jumps from the zero value with tolerance to greater than the standard weight of the material with tolerance, it is determined that the robot arm has successfully grasped the material, and the robot arm enters the picking state; if the mass of the object grasped by the robot arm does not jump from the zero value with tolerance to greater than the standard weight of the material with tolerance, it is determined that the robot arm has not successfully grasped the material, then adjust the position parameters and force parameters of the robot arm's gripper, return to step S1, regenerate the robot arm motion planning instruction, and re-grasp.

[0021] The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes:

[0022] When the motion planning instruction of the robot arm is a lowering instruction, if the mass of the object grasped by the robot arm drops from the effective grasping weight to the zero value with a tolerance, it is determined that the material is successfully placed by the robot arm, and the robot arm enters the lowering state; if the mass of the object grasped by the robot arm does not drop from the effective grasping weight to the zero value with a tolerance, it is determined that the material is not successfully placed by the robot arm, then the position parameters and force parameters of the gripper of the robot arm are adjusted, and the process returns to step S1, regenerates the motion planning instruction of the robot arm, and performs the lowering again.

[0023] The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes:

[0024] In the case where no robot arm motion planning instruction is generated, if the mass of the object grasped by the robot arm fluctuates within a preset range of zero value with a tolerance within a preset time period, the robot arm is in an idle state and returns to step S1 to wait for the next robot arm motion planning instruction to be generated.

[0025] The zero value with tolerance is an error range of 5% above and below the zero value; the standard weight of the material with tolerance is an error range of 5% above and below the standard weight of the material.

[0026] In a second aspect, the present application proposes an embodied intelligent decision-making and control system based on a six-dimensional force sensor, comprising:

[0027] The instruction generation module is used to generate the robot arm motion planning instructions according to the preset task objectives;

[0028] The task execution module is used to drive the manipulator into the task execution state according to the manipulator motion planning instructions;

[0029] The signal acquisition module is used to collect the force signal of the end effector of the robotic arm in real time by the six-dimensional force sensor when the robotic arm is performing a task;

[0030] Vertical force extraction module, used to extract vertical force from the force signal;

[0031] A mass calculation module, configured to calculate the mass of the object grasped by the robotic arm in real time based on the vertical force;

[0032] The state judgment module is used to judge the state of the robotic arm in real time according to the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction. According to the state of the robotic arm, the robotic arm motion planning instruction is completed and returned to the instruction generation module to execute the next robotic arm motion planning instruction.

[0033] In a third aspect, the present application proposes an electronic device comprising: one or more processors, and a memory, wherein the memory is used to store instructions. When the instructions are executed by the one or more processors, the one or more processors execute the embodied intelligent decision-making and control method based on a six-dimensional force sensor.

[0034] In a fourth aspect, the present application proposes a computer-readable storage medium storing executable instructions, which, when executed, enable a processor to execute the embodied intelligent decision-making and control method based on a six-dimensional force sensor.

[0035] Beneficial effects:

[0036] This application proposes an embodied intelligent decision-making control method and system based on a six-dimensional force sensor, which can accurately measure the load mass of the robotic arm, effectively compensating for the shortcomings of the existing embodied intelligent system with poor accuracy and delayed response. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of an embodied intelligent decision-making and control method based on a six-dimensional force sensor according to an embodiment of the present application;

[0038] Figure 2 A schematic diagram of an embodied intelligent decision-making control process based on a six-dimensional force sensor according to an embodiment of the present application;

[0039] Figure 3 A principle block diagram of an embodied intelligent decision-making and control system based on a six-dimensional force sensor in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The specific implementation of the present application is further described in detail below with reference to the accompanying drawings and examples.

[0041] Example 1:

[0042] This embodiment proposes an embodied intelligent decision-making control method based on a six-dimensional force sensor, such as Figure 1 、 Figure 2 As shown, including:

[0043] Step S1: Generate robot arm motion planning instructions according to preset task objectives;

[0044] Step S2: driving the manipulator into a task execution state according to the manipulator motion planning instruction;

[0045] In this embodiment, after the robotic arm system is powered on, it completes a hardware self-test (including calibration of the six-axis force sensor and status monitoring of the servo motors in each joint of the robotic arm) and software initialization (loading the control algorithm and configuring the communication protocol). Subsequently, the embodied intelligent decision-making control system triggers the model inference module, generating initial instructions for the robotic arm's motion plan based on the preset task objectives (e.g., material handling or parts assembly), driving the robotic arm into task execution.

[0046] Step S3: When the manipulator is in the task execution state, the six-dimensional force sensor collects the force signal of the end effector of the manipulator in real time;

[0047] In this embodiment, a customized calibration of the six-dimensional force sensor is first performed. Targeting the specific application scenario of a robotic arm grasping an object, the sensor is calibrated under various operating conditions. By measuring standard objects of known weight at varying temperatures, humidity levels, and in various grasping postures, a high-precision calibration model is established. This ensures that the sensor can accurately measure vertical force in complex environments, thereby accurately calculating the object's weight. The calibration process is state-of-the-art and will not be further described in this embodiment.

[0048] In this embodiment, a six-dimensional force sensor collects real-time force and torque signals from the end effector during the robot's movements. Specifically, the sensor, using built-in strain gauges and other sensitive components, converts the three-dimensional force and torque at the end of the robot in Cartesian space into electrical signals. These signals are then amplified, filtered, and converted to analog-to-digital before being transmitted to the control system.

[0049] Step S4: extracting the vertical force from the force signal;

[0050] Specifically, in a Cartesian three-dimensional coordinate system, it is easy to extract the force in the vertical direction from the force signal.

[0051] Step S5: Calculate the mass of the object grasped by the robotic arm in real time based on the vertical force. The calculation formula is as follows:

[0052] ;

[0053] Where m is the mass of the object grasped by the robotic arm, is the vertical force, is the robot's own gravity, is the offset caused by other interference forces, is the acceleration due to gravity, It is the acceleration in directions other than the direction of movement of the end of the robot arm during the movement of the robot arm.

[0054] In this embodiment, a specialized weight calculation algorithm is designed to extract vertical force for weight calculation. Based on the vertical force component measured by the six-dimensional force sensor, combined with gravitational acceleration and the robot arm's structural parameters (such as the lever arm length, which is used to correct the force measurement), the weight of the object is calculated using a precise mathematical formula.

[0055] The method of this embodiment can quickly and accurately obtain the weight of an object, ensuring both real-time performance and meeting measurement accuracy requirements.

[0056] Step S6: According to the mass of the object grasped by the robot arm and the robot arm motion planning instruction, the state of the robot arm is judged in real time. According to the state of the robot arm, the robot arm motion planning instruction is completed, and the process returns to step S1 to execute the next robot arm motion planning instruction.

[0057] The states of the robotic arm include: a robotic arm picked up state, a robotic arm idle state, and a robotic arm lowered state.

[0058] The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes:

[0059] When the robot arm motion planning instruction is a grasping instruction, if the mass of the object grasped by the robot arm jumps from the zero value with tolerance to greater than the standard weight of the material with tolerance, it is determined that the robot arm has successfully grasped the material, and the robot arm enters the picking state; if the mass of the object grasped by the robot arm does not jump from the zero value with tolerance to greater than the standard weight of the material with tolerance, it is determined that the robot arm has not successfully grasped the material, then adjust the position parameters and force parameters of the robot arm's gripper, return to step S1, regenerate the robot arm motion planning instruction, and re-grasp.

[0060] The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes:

[0061] When the motion planning instruction of the robot arm is a lowering instruction, if the mass of the object grasped by the robot arm drops from the effective grasping weight to the zero value with a tolerance, it is determined that the material is successfully placed by the robot arm, and the robot arm enters the lowering state; if the mass of the object grasped by the robot arm does not drop from the effective grasping weight to the zero value with a tolerance, it is determined that the material is not successfully placed by the robot arm, then the position parameters and force parameters of the gripper of the robot arm are adjusted, and the process returns to step S1, regenerates the motion planning instruction of the robot arm, and performs the lowering again.

[0062] The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes:

[0063] In the case where no robot arm motion planning instruction is generated, if the mass of the object grasped by the robot arm fluctuates within a preset range of zero value with a tolerance within a preset time period, the robot arm is in an idle state and returns to step S1 to wait for the next robot arm motion planning instruction to be generated.

[0064] In this embodiment, the control system compares the calculated weight with a preset threshold and performs a status judgment:

[0065] When the robot arm motion planning instruction is a grasping instruction, the picking state is determined as follows: if the weight jumps from a value close to zero to greater than the standard weight of the material (considering the measurement error, set as the tolerance), it is determined that the robot arm has successfully grasped the material and enters the "picking state".

[0066] When the robot arm motion planning instruction is a drop instruction, the drop state is determined as follows: if the weight drops from the effective grab weight value to a value close to zero, it is determined that the material is successfully placed and enters the "drop state".

[0067] In the absence of robot arm motion planning instructions, idle state judgment: If the weight always fluctuates around zero (no pick-up or put-down judgment conditions are triggered) and the robot arm has no motion instructions to execute, it is judged to be in "idle state".

[0068] Establish a grasping status feedback mechanism. After determining the grasping status through weight comparison, the six-axis force sensor promptly feeds grasping status information (success or failure) to the robotic arm control system. Based on this feedback, the control system continues to perform subsequent operations if the grasp is successful; if the grasp fails, it adjusts parameters such as the position and force of the robotic arm's gripper and attempts the grasp again.

[0069] Enables linkage with other equipment. In scenarios such as industrial production, the gripping status information from the six-dimensional force sensor can be linked with other equipment on the production line. For example, if the robotic arm successfully grasps a material, subsequent equipment on the production line can automatically start and proceed to the next processing operation. If the grasp fails, the production line is paused, avoiding subsequent production problems caused by incorrect material grasping and improving the reliability and efficiency of the entire production system.

[0070] Regardless of whether the robotic arm is in the "pickup" state, "placement" state, or "idle" state, as long as the complete task logic is met (for example, completing the transfer and placement of materials from station A to station B), the control system determines that the task is complete. At this point, it sends a command to return the robotic arm to a safe position (such as the initial stop), shuts down the model inference process, and the six-axis force sensor enters low-power monitoring mode. The entire task process is terminated and the next task is triggered.

[0071] The main thing to note is: if data abnormalities occur in the state judgment link (such as six-dimensional force sensor signal jumps, weight calculation results exceed the physically reasonable range), the control system triggers the exception handling mechanism: first, data filtering and recalculation are performed. If the calculation is still abnormal after three consecutive times, the robot arm action is suspended, and an error is reported through the human-machine interface, prompting manual inspection of the sensor connection, material status, etc. to ensure the reliability of the system operation.

[0072] The zero value with tolerance is an error range of 5% above and below the zero value; the standard weight of the material with tolerance is an error range of 5% above and below the standard weight of the material.

[0073] In this embodiment, a comparison and judgment algorithm needs to be constructed. The calculated weight of the object is compared with the preset actual mass of the object. A reasonable weight error tolerance range is set. When the calculated weight is within the error range of the actual mass, it is judged that the robot arm gripper has completely picked up the object; if the calculated weight is significantly less than the actual mass, it is judged that the gripper is incomplete. For example, the error range is set to ±5% of the actual mass. When When , it is determined that the crawl is successful, among which, The standard weight of the material.

[0074] This embodiment proposes an embodied intelligent decision-making and control method based on a six-dimensional force sensor. Compared with the traditional method of determining the completion of an action, the six-dimensional force sensor-based determination technology adopted in this embodiment has significant advantages. Traditional methods have drawbacks such as poor accuracy and delayed response in embodied intelligent systems, making it difficult to accurately determine whether an action is completed. In sharp contrast, the solution of this embodiment can accurately measure the load mass of the robotic arm, effectively making up for the many shortcomings of existing embodied intelligent systems in determining the completion of an action.

[0075] In precision assembly scenarios in industrial production, its precise load quality determination capability ensures that each component is accurately stressed during assembly, greatly improving assembly accuracy, effectively reducing defective product rates, and significantly enhancing production efficiency and product quality. In the medical field, surgical assistance robots use this technology to accurately sense the forces between instruments and tissues, precisely control movements, and provide a solid guarantee for surgical accuracy and safety, helping doctors complete more complex and delicate surgical operations. In the service industry, robots providing personalized services use this solution to accurately determine the grasping status based on force feedback when grasping different items, thereby flexibly and accurately completing various service tasks, significantly improving service quality and user experience.

[0076] Example 2:

[0077] This embodiment proposes an embodied intelligent decision-making control system based on a six-dimensional force sensor. Figure 3 As shown, it includes: an instruction generation module, a task execution module, a signal acquisition module, a vertical force extraction module, a mass calculation module, and a state judgment module;

[0078] The instruction generation module is connected to the task execution module, the task execution module is connected to the signal acquisition module, the signal acquisition module is connected to the vertical force extraction module, the vertical force extraction module is connected to the mass calculation module, the mass calculation module is connected to the state judgment module, and the state judgment module is connected to the instruction generation module;

[0079] The instruction generation module is used to generate the robot arm motion planning instructions according to the preset task objectives;

[0080] The task execution module is used to drive the manipulator into the task execution state according to the manipulator motion planning instructions;

[0081] The signal acquisition module is used to collect the force signal of the end effector of the robotic arm in real time by the six-dimensional force sensor when the robotic arm is performing a task;

[0082] Vertical force extraction module, used to extract vertical force from the force signal;

[0083] A mass calculation module, configured to calculate the mass of the object grasped by the robotic arm in real time based on the vertical force;

[0084] The state judgment module is used to judge the state of the robotic arm in real time according to the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction. According to the state of the robotic arm, the robotic arm motion planning instruction is completed and returned to the instruction generation module to execute the next robotic arm motion planning instruction.

[0085] Example 3:

[0086] This embodiment proposes an electronic device, comprising: one or more processors, and a memory, wherein the memory is used to store instructions. When the instructions are executed by the one or more processors, the one or more processors execute the embodied intelligent decision-making and control method based on a six-dimensional force sensor.

[0087] The electronic device can be a mobile phone, computer, or tablet computer, and includes a memory and a processor. The memory stores a computer program that, when executed by the processor, implements the embodied intelligent decision-making and control method based on a six-dimensional force sensor as described in the embodiments. It is understood that the electronic device may also include an input / output (I / O) interface and a communication component.

[0088] The processor is configured to execute all or part of the steps of the embodied intelligent decision-making and control method based on a six-dimensional force sensor as described in the above embodiment. The memory is configured to store various types of data, such as instructions for any application or method in the electronic device, as well as application-related data.

[0089] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the embodied intelligent decision-making and control method based on a six-dimensional force sensor described in the above embodiment.

[0090] Example 4:

[0091] This embodiment provides a computer-readable storage medium storing executable instructions. When the instructions are executed, if they are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0092] The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of an embodied intelligent decision-making and control method based on a six-dimensional force sensor as described in various embodiments of the present application.

[0093] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (for example, SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR abbreviation, memory data register) memory, etc.), random access memory (RAM, Random Access Memory), static random access memory (SRAM, Static Random-Access Memory), read-only memory (ROM, Read-Only Memory), electrically erasable programmable read-only memory (EEPROM, Electrically Erasable Programmable Read Only Memory), programmable read-only memory (PROM, Programmable Read-only Memory), magnetic memory, disk, optical disk, server, APP (Application, abbreviation of application software) application store and other media that can store program verification codes, on which a computer program is stored. When the computer program is executed by the processor, it can implement the above-mentioned steps of the embodied intelligent decision-making and control method based on the six-dimensional force sensor.

[0094] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0095] The scope of protection of this application is not limited to the above-described embodiments. Obviously, those skilled in the art may make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, the disclosure is intended to include such modifications and variations.

Claims

1. An embodied intelligent decision-making and control method based on a six-dimensional force sensor, characterized in that: include: Step S1: Generate robot arm motion planning instructions according to preset task objectives; Step S2: driving the manipulator into a task execution state according to the manipulator motion planning instruction; Step S3: When the manipulator is in the task execution state, the six-dimensional force sensor collects the force signal of the end effector of the manipulator in real time; Step S4: extracting the vertical force from the force signal; Step S5: Calculating the mass of the object grasped by the robotic arm in real time based on the vertical force; Step S6: According to the mass of the object grasped by the robot arm and the robot arm motion planning instruction, the state of the robot arm is judged in real time, and the robot arm motion planning instruction is completed according to the state of the robot arm, and the process returns to step S1 to execute the next robot arm motion planning instruction; The states of the robotic arm include: a robotic arm picked up state, a robotic arm idle state, and a robotic arm lowered state.

2. The embodied intelligent decision-making and control method based on a six-dimensional force sensor according to claim 1, characterized in that: Based on the vertical force, the mass of the object grasped by the robotic arm is calculated in real time using the following formula: Where m is the mass of the object grasped by the robotic arm, F z is the vertical force, F g is the gravity of the robot arm itself, F ON is the offset caused by other interference forces, g is the acceleration of gravity, and a is the acceleration in other directions except the direction of movement of the end of the robotic arm during the movement of the robotic arm.

3. The embodied intelligent decision-making and control method based on a six-dimensional force sensor according to claim 1, characterized in that: The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes: When the robot arm motion planning instruction is a grasping instruction, if the mass of the object grasped by the robot arm jumps from the zero value with tolerance to greater than the standard weight of the material with tolerance, it is determined that the robot arm has successfully grasped the material, and the robot arm enters the picking state; if the mass of the object grasped by the robot arm does not jump from the zero value with tolerance to greater than the standard weight of the material with tolerance, it is determined that the robot arm has not successfully grasped the material, then adjust the position parameters and force parameters of the robot arm's gripper, return to step S1, regenerate the robot arm motion planning instruction, and re-grasp.

4. The embodied intelligent decision-making and control method based on a six-dimensional force sensor according to claim 1, characterized in that: The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes: When the motion planning instruction of the robot arm is a lowering instruction, if the mass of the object grasped by the robot arm drops from the effective grasping weight to the zero value with a tolerance, it is determined that the material is successfully placed by the robot arm, and the robot arm enters the lowering state; if the mass of the object grasped by the robot arm does not drop from the effective grasping weight to the zero value with a tolerance, it is determined that the material is not successfully placed by the robot arm, then the position parameters and force parameters of the gripper of the robot arm are adjusted, and the process returns to step S1, regenerates the motion planning instruction of the robot arm, and performs the lowering again.

5. The embodied intelligent decision-making and control method based on a six-dimensional force sensor according to claim 1, characterized in that: The method of determining the state of the robotic arm in real time based on the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction and completing the robotic arm motion planning instruction based on the state of the robotic arm includes: In the case where no robot arm motion planning instruction is generated, if the mass of the object grasped by the robot arm fluctuates within a preset range of zero value with a tolerance within a preset time period, the robot arm is in an idle state and returns to step S1 to wait for the next robot arm motion planning instruction to be generated.

6. The embodied intelligent decision-making and control method based on a six-dimensional force sensor according to claim 3, characterized in that: The zero value with tolerance is an error range of 5% above and below the zero value; the standard weight of the material with tolerance is an error range of 5% above and below the standard weight of the material.

7. An embodied intelligent decision-making and control system based on a six-dimensional force sensor, characterized in that: The method is implemented by using the embodied intelligent decision-making control method based on a six-dimensional force sensor as described in any one of claims 1 to 6, comprising: The instruction generation module is used to generate the robot arm motion planning instructions according to the preset task objectives; The task execution module is used to drive the manipulator into the task execution state according to the manipulator motion planning instructions; The signal acquisition module is used to collect the force signal of the end effector of the robotic arm in real time by the six-dimensional force sensor when the robotic arm is performing a task; Vertical force extraction module, used to extract vertical force from the force signal; A mass calculation module, configured to calculate the mass of the object grasped by the robotic arm in real time based on the vertical force; The state judgment module is used to judge the state of the robotic arm in real time according to the mass of the object grasped by the robotic arm and the robotic arm motion planning instruction. According to the state of the robotic arm, the robotic arm motion planning instruction is completed and returned to the instruction generation module to execute the next robotic arm motion planning instruction.

8. An electronic device, characterized in that: include: One or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the embodied intelligent decision-making and control method based on a six-dimensional force sensor as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that It stores executable instructions, which, when executed, enable the processor to execute the embodied intelligent decision-making and control method based on a six-dimensional force sensor as described in any one of claims 1 to 6.

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