Manipulator control adaptation system

Abstract

The application discloses a kind of mechanical hand's drive control adaptation system, the mechanical hand's drive control adaptation system includes mechanical hand and mechanical arm, the mechanical hand is installed on the mechanical arm, the drive control adaptation system further includes acquisition unit, processing unit and control unit, the acquisition unit is used to obtain the characteristic information of the mechanical hand;The processing unit determines the identity information of the mechanical hand according to the characteristic information, determines drive information according to the identity information;The control unit generates control signal according to the instruction information of external input and the drive information, controls the mechanical hand movement according to the control signal.The application can automatically identify the specific identity information of the mechanical hand on the mechanical arm, to carry out corresponding drive matching.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to a drive and control adaptation system for a robotic arm. Background Technology

[0002] Robots or other automated machinery typically have robotic arms, which are equipped with different types of robotic hands to perform different tasks. Therefore, existing robotic arms are compatible with a variety of different types of robotic hands. However, different robotic hands require different drive and control logics. Manually matching different robotic hands is inefficient and sometimes leads to misoperation due to human error.

[0003] Therefore, there is an urgent need for a drive and control adaptation system for robotic arms that can solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a drive and control adaptation system for a robotic arm that automatically identifies the specific identity information of the robotic arm to perform corresponding drive matching, with accurate and efficient identification.

[0005] To achieve the above objectives, the present invention provides a drive and control adaptation system for a robotic arm, comprising a robotic arm and a robotic hand, wherein the robotic arm is mounted on the robotic arm. The drive and control adaptation system further comprises an acquisition unit, a processing unit, and a control unit. The acquisition unit is used to acquire feature information of the robotic arm; the processing unit determines the identity information of the robotic arm based on the feature information and determines drive information based on the identity information; the control unit generates a control signal based on externally input instruction information and the drive information, and controls the movement of the robotic arm based on the control signal.

[0006] Preferably, the robotic arm is equipped with a feature tag, and the acquisition unit includes a tag acquisition module. The tag acquisition module acquires the tag information of the feature tag and sends the tag information as the feature information of the robotic arm to the processing unit. The processing unit compares the tag information with a pre-stored tag information database to obtain the tag candidate model corresponding to the tag information, and determines the identity information of the robotic arm based on the tag candidate model.

[0007] Specifically, the feature tag is an RFID tag, and the tag acquisition module is an RFID reader / writer.

[0008] Specifically, the feature label is a QR code, image identifier, or mechanical code, and the label acquisition module is an image scanner.

[0009] Specifically, the feature label is a DIP switch, and the label acquisition module is a DIP switch status recognizer.

[0010] Specifically, the feature tag is an electrical connector within a preset resistance range, and the tag acquisition module is a resistance detector.

[0011] Preferably, the acquisition unit includes a vision module; the control unit controls the robotic arm to perform a preset action, and controls the vision module to synchronously acquire motion image information of the robotic hand when the robotic arm performs the preset action, and sends the motion image information as feature information of the robotic hand to the processing unit; the processing unit receives the motion image information, identifies the motion features of the motion image information, filters visual candidate models that match the motion features in a pre-stored model candidate set, and determines the identity information of the robotic hand based on the visual candidate models.

[0012] Preferably, the robotic arm is equipped with a feature tag, and the acquisition unit includes a tag acquisition module and a vision module. The tag acquisition module acquires the tag information of the feature tag and sends the tag information as the feature information of the robotic arm to the processing unit. The processing unit compares the tag information with a pre-stored tag information database to identify the tag information. If the identification is successful, the tag candidate model corresponding to the tag information is taken as the tag identification result; if the identification fails, the tag identification failure is taken as the tag identification result. The control unit controls the robotic arm to perform a preset action and controls the vision module to simultaneously acquire the motion image information of the robotic arm during the preset action, and sends the motion image information as the feature information of the robotic arm to the processing unit. The processing unit identifies the motion features of the motion image information, filters the visual candidate models that match the motion features in a pre-stored model candidate set, and if the match is successful, the visual candidate model is taken as the visual recognition result; if the match fails, the visual detection failure information is taken as the visual recognition result. The processing unit performs cross-fusion judgment based on the tag recognition result and the visual recognition result to obtain the identity information of the robotic arm. This solution uses two methods to identify and cross-integrate specific information about the robotic arm, thereby further improving the accuracy of identification.

[0013] Specifically, when the vision module is a monocular camera, the control unit controls the left and right robotic arms to perform preset actions in a time-division manner, and controls the monocular camera to collect motion image information of the left and right robotic arms in a time-division manner; when the vision module is a binocular camera, the distribution direction of the two cameras in the binocular camera corresponds to the distribution direction of the first and second regions, and the control unit simultaneously controls the left and right robotic arms to perform preset actions, and controls the binocular camera to simultaneously collect motion image information of the left and right robotic arms.

[0014] Specifically, the first region and the second region are located on the left and right sides of the center line, respectively, with the center line of the vision module or the center line of the two robotic arms as the boundary.

[0015] Preferably, the robotic arm is mounted on the robotic arm via an installation module, which includes a positioning mechanism, a locking mechanism, and a signal interface. The positioning mechanism is used to achieve precise positioning of the robotic arm, the locking mechanism is used to lock the robotic arm to the robotic arm, and the signal interface is used to connect the processing unit to the robotic arm for signal connection.

[0016] Specifically, the robotic arm is mounted on the main structure, and buttons are provided on the robotic arm or the main structure. Operation commands are input through the operation buttons. The processing unit detects the installation status of the robotic arm based on the connection status of the signal interface and / or the locking status of the locking mechanism. When the robotic arm is not mounted on the robotic arm, the processing unit controls the acquisition unit to power on to exit the pre-power-on state according to the operation command. When the robotic arm is mounted on the robotic arm and the robotic arm information is not determined, the processing unit controls the acquisition unit to start working. After the robotic arm is mounted on the robotic arm and the robotic arm information is determined, the signal interface is powered off. When the acquisition unit fails to recognize the robotic arm information, the processing unit controls the acquisition unit to return to the pre-power-on state.

[0017] Compared with the prior art, the present invention can automatically identify the identity information of the robot through the processing unit, and then generate corresponding drive signals based on the identity information to match the control of the robot, which is accurate and efficient. Attached Figure Description

[0018] Figure 1 This is a structural diagram of one embodiment of the drive and control adapter system.

[0019] Figure 2 This is a structural diagram of another embodiment of the drive and control adapter system.

[0020] Figure 3 This is a structural block diagram of the drive and control adaptation system.

[0021] Figure 4 This is a flowchart of how the drive and control adaptation system works.

[0022] Figure 5 This is a partial flowchart of the operation of the drive and control adaptation system in Embodiment 1 of the present invention.

[0023] Figure 6 This is a partial flowchart of the operation of the drive and control adaptation system in Embodiment 2 of the present invention.

[0024] Figure 7This is a partial flowchart of the operation of the drive and control adaptation system in Embodiment 3 of the present invention. Detailed Implementation

[0025] To illustrate the technical content, structural features, objectives, and effects of the present invention in detail, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0026] refer to Figure 1 The present invention discloses a drive and control adaptation system for a robotic arm 103, including a robotic arm 103 and a robotic arm 102, wherein the robotic arm 103 is mounted on the robotic arm 102.

[0027] refer to Figure 2 The drive and control adapter system also includes a main structure 101, on which the robotic arm 102 is mounted. In some embodiments, the main structure 101 can be the body of a robot or a structure that serves as the main body of a robot or mechanical device.

[0028] The robotic arm 103 can be a two-finger dexterous hand, a three-finger dexterous hand, a five-finger dexterous hand, a gripper, an electric cylinder, a voice coil motor, etc., and is not limited to these.

[0029] refer to Figure 3 and Figure 4 The drive and control adaptation system also includes an acquisition unit 10, a processing unit 20, and a control unit 30. The acquisition unit 10 is installed on the robotic arm 102 and / or the main structure 101. (1) The acquisition unit 10 is used to acquire the feature information of the robotic arm 103. (2) The acquisition unit 10 sends the feature information acquired by the acquisition unit 10 to the processing unit 20. (3) The processing unit 20 determines the identity information of the robotic arm 103 based on the feature information and determines the drive information based on the identity information. (4) The processing unit 20 sends the drive information to the control unit 30. (5) The control unit 30 generates a control signal based on the externally input instruction information and drive information. (6) The control unit 30 controls the movement of the robotic arm 103 based on the control signal.

[0030] The processing unit 20 stores a mapping table between the identity information and driving information of various types of robotic arms 103. The driving information includes one or more of the following: driving voltage, driving current, motion speed threshold, torque parameters, and motion acceleration parameters.

[0031] In the above steps, if the identification of the robot arm 103 fails in step (3), the control acquisition unit 10 will automatically return to the power-on state to prevent misoperation. At this time, the robot arm information identification process can be restarted by pressing and holding the button to identify the robot arm 103's identity information again. If the identification fails twice, an identification failure reminder will be issued.

[0032] In this invention, the robotic arm 103 is mounted on the robotic arm 102 via an installation module. The installation module includes a positioning mechanism, a locking mechanism, and a signal interface. The positioning mechanism is used to achieve precise positioning of the robotic arm 103, the locking mechanism is used to lock the robotic arm 103 onto the robotic arm 102, and the signal interface is used to connect the processing unit 20 to the robotic arm 103 via signal connection.

[0033] Specifically, the robotic arm 102 is mounted on the main structure 101. Buttons are provided on the robotic arm 102 or the main structure 101. Operation commands are input through the operation buttons. The processing unit 20 detects the installation status of the robotic hand 103 based on the connection status of the signal interface and / or the locking status of the locking mechanism. When the robotic hand 103 is not mounted on the robotic arm 102, the processing unit 20 controls the acquisition unit 10 to power on to exit the pre-power-on state based on the operation command. When the robotic hand 103 is mounted on the robotic arm 102 and the robotic hand 103 information is not determined, the processing unit 20 controls the acquisition unit 10 to start working. After the robotic hand 103 is mounted on the robotic arm 102 and the robotic hand 103 information is determined, the processing unit 20 cuts off the power to the signal interface. When the acquisition unit 10 fails to recognize the robotic hand 103 information, the processing unit 20 controls the acquisition unit 10 to return to the pre-power-on state.

[0034] refer to Figure 5 In Embodiment 1, a feature tag 13 is installed on the robotic arm 103. The acquisition unit 10 includes a tag acquisition module 11, which is used to acquire feature information of the robotic arm 103. When the tag acquisition module 11 identifies the feature information of the robotic arm 103: (1a) the tag acquisition module 11 acquires the tag information of the feature tag 13 on the robotic arm 103; (1b) the tag acquisition module 11 sends the tag information as feature information of the robotic arm to the processing unit 20; (1c) the processing unit 20 compares the pre-stored tag information database and the tag information to identify the tag information, thereby obtaining the tag candidate model corresponding to the tag information, and determining the identity information of the robotic arm 103 based on the tag candidate model. This allows the present invention to quickly identify the identity information of the robotic arm 103 based on the feature tag 13 on the robotic arm 103. In this embodiment, the tag acquisition module 11 is installed on the robotic arm 102. Of course, the tag acquisition module 11 can also be installed on the main structure 101.

[0035] Continue to refer to Figure 5 In step (1c), when the processing unit 20 identifies the tag information, if the identification is successful, step (1d) is executed, and the candidate tag model is used as the tag identification result; if the identification fails, step (1d') is executed, and the tag identification failure information is used as the tag identification result. In this embodiment, if the tag information is successfully identified, the processing unit 20 uses the candidate tag model as the identity information of the robot arm 103; if the tag information is identified, the processing unit 20 reminds the user of the tag identification failure.

[0036] In this embodiment, the feature tag 13 is an RFID tag, and the tag acquisition module 11 is an RFID reader / writer. However, in another embodiment, the feature tag 13 can also be a QR code, image identifier, or mechanical code, and the tag acquisition module 11 can be an image scanner. In yet another embodiment, the feature tag 13 is a DIP switch, and the tag acquisition module 11 is a DIP switch status identifier. In yet another embodiment, the feature tag 13 is an electrical connector within a preset resistance range, and the tag acquisition module 11 is a resistance detector.

[0037] Specifically, the tag information includes one or more of the following: robot arm information code, robot arm drive type code, series information code, and check code. The processing unit 20 identifies the tag information by: verifying the check code; if the verification is successful, searching for the corresponding tag candidate model in the pre-stored tag information and tag candidate model correspondence table; if the verification fails, the tag identification failure information is used as the tag identification result.

[0038] The tag information includes the type and identity information of the robotic arm. The robotic arm information code is a category code based on the 103 categories of robotic arms, and in this embodiment, it is set to 4 digits, such as 0001=voice coil, 0002=gripper, 0003=dexterous hand. The robotic arm drive type code is also set to four digits in this embodiment: 0001=DC, 0002=AC, 0003=servo drive, 0004=brushless drive. The series information code is set to eight digits in this embodiment to identify the product series under the same category. The check code is set to four digits in this embodiment to verify the integrity of the tag information and avoid data transmission errors.

[0039] Specifically, the working principle and process of the tag acquisition module 11 and processing unit 20 in acquiring and identifying the identity information of the robotic arm 103 are as follows: 1. The RFID reader (tag acquisition module 11) is activated to read the tag information of the RFID tags (feature tags) on the robotic arm and send it to the processing unit 20. 2. The processing unit 20 parses and verifies the tag information. If the verification passes, it extracts the classification information corresponding to the robotic arm information code, the type information corresponding to the robotic arm drive type code, and the series information corresponding to the series information code from the tag information, and outputs the above information to obtain a subset of candidate tag models. If the verification fails, the RFID tag is marked as abnormal, and tag identification fault information is generated as the tag identification result.

[0040] The control unit 30 includes a safety timing unit, a master control unit, a verification unit, a storage unit, a communication unit, and a slave control unit. The master control unit is located outside the robot 103, for example, on the main structure 101 or the robotic arm 102, while the slave control units are located on the robot 103. When the control unit 30 controls the robot 103 to move according to the control signals, the robot 103 acts as an end effector. The master control unit of the control unit 30 sends the control signals to the slave control units, which then send the control signals to the drive circuit of the robot 103 to control the robot 103 to perform actions matching its own identity. During the control process, the control unit 30 also receives motion status feedback signals from the robot 103 in real time and adjusts the control signals according to the feedback signals to achieve closed-loop motion control of the robot 103. The communication unit enables communication between the master control unit and the slave control units.

[0041] The storage unit of the control unit 30 stores a mapping table of identity information and drive information for various types of robotic arms 103. Before sending the control signal to the drive circuit of the robotic arm 103, the control unit 30 also verifies whether the control signal and the identity information of the robotic arm 103 match through a verification unit: it checks whether the robotic arm's identity fingerprint is consistent with the above mapping table, determines whether the drive parameters in the control signal are within the safety threshold range, and determines whether the type of robotic arm information and drive capability match. If the above verification results are consistent, the control signal is sent to the drive circuit of the robotic arm 103; if the above verification results are inconsistent, the control signal is prohibited from being sent to the drive circuit of the robotic arm 103, and the robotic arm is controlled to enter a safe standby state.

[0042] refer to Figure 3 and Figure 6Based on Embodiment 1, in Embodiment 2, the acquisition unit 10 further includes a vision module 12. If the tag acquisition module 11 fails to recognize the tag in step 1c, and the control unit 30 receives the tag recognition fault information, the vision recognition program is started: (2a) The control unit 30 controls the robotic arm 102 to perform a preset action; (2b) The control unit 30 controls the vision module 12 to synchronously acquire the motion image information of the robotic arm 103 when the robotic arm 102 performs the preset action; (2c) The vision module 12 sends the motion image information to the processing unit 20. (2d) The processing unit 20 identifies the motion features of the motion image information; (2e) The processing unit 20 filters the visual candidate models that match the motion features from the pre-stored model candidate set. (2f) If a match is successful, the visual candidate model is taken as the visual recognition result; if a match fails, the visual detection failure is taken as the visual recognition result. The success or failure of a match is determined by the confidence level during feature matching. Specifically, the confidence level is compared with a confidence threshold. If the confidence level exceeds the threshold, the match is considered successful, and visual recognition is successful; if it is less than the threshold, the match is considered unsuccessful, and visual recognition fails. The visual module 12 is a camera. The visual module 12 can be mounted on the robotic arm 102 or on the main structure 101.

[0043] In Example 2, when a match is successful in step 2f, the visual candidate model is directly used as the identity information of the robot arm 102.

[0044] When the control unit 30 controls the vision module 12 to synchronously acquire motion image information of the robot arm 103, under the action of the robot arm 102, it acquires motion image information of the robot arm 103 from multiple angles. Generally, it is necessary to capture appearance images of the robot arm 103 from at least three key angles: front, side and tail, to ensure complete coverage of all structural features.

[0045] Specifically, in order to facilitate the vision module 12 to acquire motion image information of the robotic arm 103, the control unit 30 controls the robotic arm 102 to move so that the robotic arm 103 is moved to a preset vision acquisition area. Then, the control unit controls the robotic arm 102 to move so that the robotic arm 103 presents multiple angles in the vision acquisition area, so that the vision module 12 can acquire motion image information of the robotic arm 103 from multiple angles.

[0046] When the main structure 101 of this invention is the body of a robot, it has two robotic arms 102: a left robotic arm and a right robotic arm, and robotic hands 103 are also corresponding left and right robotic hands. The left robotic hand is mounted on the left robotic arm, and the right robotic hand is mounted on the right robotic arm. Since the identification information of the left and right robotic hands can be the same or different, it is necessary to identify the identification information of the left and right robotic hands separately. To facilitate the identification of the identification information of the left and right robotic hands, the visual acquisition area is divided into a first area corresponding to the left robotic hand and a second area corresponding to the right robotic hand. The first and second areas can be divided into left and right areas with the center line of the main structure 101 as the interval, or they can be distributed vertically to form an upper and lower area. Of course, the center line of the visual module or the center line of the two robotic arms can also be used as the boundary, and the center line is not limited to the center line of the main structure 101.

[0047] In one embodiment, the vision module 12 is a monocular camera. During visual recognition, the control unit 30 controls the left robotic arm to move the left robotic hand to the first area and controls the right robotic arm to move the right robotic hand to the second area. The control unit 30 controls the left and right robotic arms to perform preset actions in a time-sharing manner and controls the monocular camera to collect the action image information of the left and right robotic hands in a time-sharing manner.

[0048] In another embodiment, the vision module 12 is a binocular camera. During visual recognition, the control unit 30 controls the left robotic arm to move the left robotic hand to the first area and controls the right robotic arm to move the right robotic hand to the second area. The control unit 30 controls the left and right robotic arms to perform preset actions simultaneously, and controls the binocular camera to simultaneously acquire motion image information of the left and right robotic hands. In this embodiment, the first and second areas are separated by the center line of the left and right robotic arms, and are located on the left and right sides of the center line, respectively.

[0049] After obtaining the motion image information, the processing unit 20 preprocesses and extracts features from the motion image information, then performs feature matching in the model candidate set, and outputs the corresponding visual candidate model and confidence level. If the confidence level is greater than or equal to 85%, it is determined that the visual recognition program has successfully matched or recognized the model. If the confidence level is less than 85%, it is determined that the visual recognition program has failed to match or recognize the model.

[0050] refer to Figure 7Based on Embodiment 1, in Embodiment 3, the acquisition unit 10 further includes a vision module 12 installed on the main structure 101. When the vision module 12 identifies the identity information of the robot arm 103, it also initiates a vision recognition program: (2a) the control unit 30 controls the robot arm 103 to perform a preset action; (2b) the control unit 30 controls the vision module 12 to synchronously acquire motion images of the preset action of the robot arm 103; (2c) the vision module 12 sends the motion images to the processing unit 20. (2d) the processing unit 20 identifies the motion features of the motion images; (2e) the processing unit 20 filters visual candidate models that match the motion features from a pre-stored model candidate set. (2f) if the match is successful, the visual candidate model is used as the recognition result; if the match fails, the visual detection failure is used as the recognition result. (2g) the processing unit 20 performs cross-fusion judgment based on the tag recognition result and the vision recognition result to obtain the identity information of the robot arm 103.

[0051] During the fusion judgment, if the identification is successful, the corresponding actuator type, specific model, and drive method are output as the identity information of the robot arm 103, and the identity information and confidence level of the robot arm 103 are sent to the downstream process. If the identification is abnormal, the corresponding processing logic is triggered according to the abnormality type, which may include secondary matching, alarm, and manual intervention.

[0052] The processing unit 20 first starts the tag acquisition module 11 to identify the identity information of the robot arm 103 after power-on, and then starts the vision module 12 to identify the identity information of the robot arm 103. The preset model candidate set is the smallest model candidate set where the tag candidate model is located.

[0053] Of course, unlike this, after power-on, the processing unit 20 can also first start the visual recognition program to identify the identity information of the robot arm 103, and then identify the identity information of the robot arm 103 through the tag acquisition module 11. Alternatively, the processing unit 20 can identify the identity information of the robot arm 103 simultaneously with starting the visual recognition program to identify the identity information of the robot arm 103 and using the tag acquisition module 11.

[0054] In step 2g, the processing unit 20 performs cross-fusion judgment based on the tag recognition result and the visual recognition result to obtain the final information of the robotic arm 103, including: When the label recognition result is a label candidate model and the visual recognition result is a visual candidate model, the processing unit 20 determines whether the visual candidate model and the label candidate model match. If they match, the visual candidate model is used as the identity information of the robot arm 103. If they do not match, the processing unit 20 reminds that the label recognition and visual recognition do not match and prompts "the label may be misplaced / mixed materials", and manual intervention is required for verification.

[0055] When the label recognition result is a candidate label model and the visual recognition result is a visual detection failure, the processing unit 20 restarts the visual recognition program. If the matching is successful again, the cross-fusion judgment continues. If the matching fails again, the user is notified of the visual matching abnormality and manual intervention is required for confirmation.

[0056] When the tag recognition result is a tag recognition failure and the visual recognition result is a visual candidate model, the processing unit 20 restarts the visual recognition program. If the match is successful again and consistent with the first match result, the visual candidate model is used as the final robot arm 103 information. If the match is successful but inconsistent with the first match result, a visual recognition failure alarm is issued, awaiting manual intervention. In this step, the confidence threshold for judging successful and unsuccessful matching in the visual recognition program is 85%. Of course, in another embodiment, when the tag recognition result is a tag recognition failure and the visual recognition result is a visual candidate model, the processing unit uses the visual candidate model as the robot arm 103's identity information. In this embodiment, a confidence level greater than or equal to 90% during the visual recognition program matching is considered successful recognition or successful matching, and a confidence level less than 90% is considered unsuccessful recognition or unsuccessful matching. The confidence threshold for judging successful and unsuccessful matching in the visual recognition program when the tag acquisition unit 10 fails to recognize the tag is greater than the confidence threshold for judging successful and unsuccessful matching in the visual recognition program when the tag acquisition unit 10 successfully recognizes the tag.

[0057] refer to Figure 6 Unlike Embodiment 1, in Embodiment 4, the acquisition unit 10 includes a vision module 12 installed on the main structure 101. When the processing unit identifies the identity information of the robotic arm 103, it starts a visual recognition program: (2a) The control unit 30 controls the robotic arm 102 to perform a preset action; (2b) The control unit 30 controls the vision module 12 to synchronously collect the motion image information of the robotic arm 103 when the robotic arm 102 performs the preset action; (2c) The vision module 12 sends the motion image information to the processing unit 20. (2d) The processing unit 20 identifies the motion features of the motion image information; (2e) The processing unit 20 filters the visual candidate models that match the motion features from the pre-stored model candidate set. (2f) If the match is successful, the visual candidate model is taken as the visual recognition result; if the match fails, the visual detection failure is taken as the visual recognition result.

[0058] In this embodiment, if visual recognition is successful, the processing unit 20 can directly use the visual candidate model as the identity information of the robot arm 103. If visual recognition fails, the processing unit 20 can directly remind the user of the visual detection failure.

[0059] Compared with the prior art, the present invention can identify the identity information of the robot arm 103 through the processing unit 20, and then generate corresponding drive signals based on the identity information to match the control of the robot arm 103, which is accurate and efficient.

[0060] The operation flow of the drive and control adaptation system for the robotic arm of the present invention is described as follows: 1. Before installing the robotic arm 103, manually click the button on the main structure 101 or the robotic arm 102 to generate an electrical signal to pre-power the acquisition unit 10 (e.g., to the RFID reader and / or camera, the drive circuit of the robotic arm 103 is disabled).

[0061] 2. After the robotic arm 103 is installed on the robotic arm 102, the operator clicks the button again to confirm that the locking mechanism has locked the robotic arm and to start the robotic arm information recognition process.

[0062] 3. The acquisition unit 10 collects the feature information of the robotic arm 103 and sends the features to the processing unit 20. The processing unit 20 determines the identity information of the robotic arm 103 based on the features and determines the drive information based on the identity information. The control unit 30 generates control signals based on the externally input instruction information and drive information, and controls the robotic arm 103 to move according to the control signals. This completes the drive and control adaptation of the robotic arm.

[0063] 4. Before disassembling the robotic arm 103, manually press the button to safely disconnect the power inside the robotic arm 103: first, control the drive circuit of the robotic arm 103 to power down. After power-down, the main control unit and the slave control unit communicate to allow the robotic arm 103 to be disassembled.

[0064] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any equivalent variations made in accordance with the scope of the present invention are still within the scope of the present invention.

Claims

1. A drive and control adaptation system for a robotic hand, the drive and control adaptation system comprising a robotic hand and a robotic arm, the robotic hand being mounted on the robotic arm, characterized in that, The drive control adapter system also includes: The acquisition unit is used to acquire the feature information of the robotic arm; The processing unit determines the identity information of the robotic arm based on the feature information, and determines the drive information based on the identity information; The control unit generates control signals based on externally input instruction information and the drive information, and controls the movement of the robotic arm based on the control signals.

2. The drive and control adaptation system for the robotic arm as described in claim 1, characterized in that: The robotic arm is equipped with feature tags. The acquisition unit includes a tag acquisition module, which acquires the tag information of the feature tags and sends the tag information as feature information of the robotic arm to the processing unit. The processing unit compares the pre-stored tag information database with the tag information to obtain the tag candidate model corresponding to the tag information, and determines the identity information of the robot arm based on the tag candidate model.

3. The drive and control adaptation system for the robotic arm as described in claim 2, characterized in that: The feature tag is an RFID tag, and the tag acquisition module is an RFID reader / writer; or... The feature label is a QR code, image identifier, or mechanical code, and the label acquisition module is an image scanner; or... The feature label is a DIP switch, and the label acquisition module is a DIP switch status recognizer; or... The feature tag is an electrical connector within a preset resistance range, and the tag acquisition module is a resistance detector.

4. The drive and control adaptation system for the robotic arm as described in claim 1, characterized in that: The acquisition unit includes a vision module; The control unit controls the robotic arm to perform a preset action, and controls the vision module to synchronously collect motion image information of the robotic hand when the robotic arm performs the preset action, and sends the motion image information as feature information of the robotic hand to the processing unit. The processing unit receives the motion image information, identifies the motion features of the motion image information, filters visual candidate models that match the motion features from a pre-stored model candidate set, and determines the identity information of the robotic arm based on the visual candidate models.

5. The drive and control adaptation system for the robotic arm as described in claim 1, characterized in that: The robotic arm is equipped with feature tags, and the acquisition unit includes a tag acquisition module and a vision module; The tag acquisition module acquires the tag information of the feature tag and sends the tag information as the feature information of the robot to the processing unit; The processing unit compares the pre-stored label information database with the label information to identify the label information. If the identification is successful, the candidate label model corresponding to the label information is taken as the label identification result. If the identification fails, the label identification failure is taken as the label identification result. The control unit controls the robotic arm to perform preset actions and controls the vision module to synchronously acquire motion image information of the robotic hand during the preset actions of the robotic arm. The motion image information is sent to the processing unit as feature information of the robotic hand. The processing unit identifies the motion features of the motion image information and filters visual candidate models that match the motion features from a pre-stored model candidate set. If the match is successful, the visual candidate model is used as the visual recognition result. If the match fails, the visual detection failure information is used as the visual recognition result. The processing unit performs cross-fusion judgment based on the tag recognition result and the visual recognition result to obtain the identity information of the robotic arm.

6. The drive and control adaptation system for a robotic arm as described in claim 4 or 5, characterized in that: The robotic arm consists of two parts, a left robotic arm and a right robotic arm. The robotic hand includes a left robotic hand and a right robotic hand respectively mounted on the left and right robotic arms. The control unit controls the left and right robotic arms to move the left and right robotic hands to a first area and a second area respectively. The control unit controls the left robotic arm to perform a preset action and simultaneously controls the vision module to collect motion image information of the left robotic arm in the first area when the left robotic arm performs the preset action. The control unit controls the right robotic arm to perform a preset action and simultaneously controls the vision module to collect motion image information of the right robotic arm in the second area when the right robotic arm performs the preset action. The processing unit determines the identity information of the left and right robotic hands based on the motion image information of the left and right robotic hands.

7. The drive and control adaptation system for the robotic arm as described in claim 6, characterized in that: When the vision module is a monocular camera, the control unit controls the left and right robotic arms to perform preset actions in a time-division manner, and controls the monocular camera to collect the action image information of the left and right robotic arms in a time-division manner; When the vision module is a binocular camera, the distribution direction of the two cameras in the binocular camera corresponds to the distribution direction of the first region and the second region. The control unit simultaneously controls the left and right robotic arms to perform preset actions and controls the binocular camera to simultaneously acquire the motion image information of the left and right robotic arms.

8. The drive and control adaptation system for the robotic arm as described in claim 6, characterized in that: The first region and the second region are located on the left and right sides of the center line, respectively, with the center line of the vision module or the center line of the two robotic arms as the boundary.

9. The drive and control adaptation system for the robotic arm as described in claim 1, characterized in that: The robotic arm is mounted on the robotic arm via an installation module, which includes a positioning mechanism, a locking mechanism, and a signal interface. The positioning mechanism is used to achieve precise positioning of the robotic arm, the locking mechanism is used to lock the robotic arm to the robotic arm, and the signal interface is used to connect the processing unit to the robotic arm for signal connection.

10. The drive and control adaptation system for the robotic arm as described in claim 9, characterized in that: The robotic arm is mounted on the main structure. Buttons are provided on the robotic arm or the main structure. Operation commands are input by pressing the buttons. The processing unit detects the installation status of the robotic arm based on the connection status of the signal interface and / or the locking status of the locking mechanism. When the robotic arm is not mounted on the robotic arm, the processing unit controls the acquisition unit to power on to remove the pre-power-on state based on the operation command. When the robotic arm is mounted on the robotic arm and the robotic arm information is not determined, the processing unit controls the acquisition unit to start working. After the robotic arm is mounted on the robotic arm and the robotic arm information is determined, the processing unit de-energizes the signal interface. When the acquisition unit fails to recognize the robotic arm information, the processing unit controls the acquisition unit to return to the pre-power-on state.

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