A robot grasping high-precision positioning method

Abstract

This invention discloses a high-precision positioning method for robot grasping, applied to a robot system including a robot body and a gripper assembly. The method utilizes a radar sensor module positioned near the gripper assembly and a capacitive sensor module at the gripper's fingertips. The radar sensor enables long-range, non-contact positioning between the gripper and the target object, while the capacitive sensor precisely detects the minute distance between them as the gripper approaches the target object. By fusing the radar and capacitive detection information and incorporating environmental interference compensation and attitude and force control mechanisms, the position and attitude of the gripper assembly are continuously corrected, achieving rapid long-range guidance and high-precision close-range alignment. During the grasping process, grasping stability and success rate are improved through grasping state determination and backtracking control.

Description

Technical Field

[0001] This invention relates to the field of robot grasping control methods, specifically a high-precision positioning method for robot grasping. Background Technology

[0002] With the rapid development of industrial automation, intelligent manufacturing, and flexible production, robotic grasping technology has become a core component for achieving efficient and precise object manipulation. In modern production lines, logistics warehousing, medical assistance, and home services, robots need to reliably grasp various objects, especially non-conductive objects (such as plastic products, glass containers, ceramic parts, and organic materials), which constitute a significant portion of everyday industrial and consumer goods. However, existing robotic grasping and positioning methods still have many limitations, making it difficult to meet practical needs in terms of grasping accuracy, robustness, and adaptability in complex environments.

[0003] Traditional robotic grasping primarily relies on visual perception systems (such as RGB cameras, RGB-D depth cameras, or laser scanners) for object detection, pose estimation, and grasping point planning. These methods have achieved good results in structured or well-lit environments, but they suffer from the following prominent problems: Inherent limitations of visual perception: RGB or RGB-D based vision methods are highly sensitive to changes in lighting, background interference, object reflection, or transparency / transparency. Under non-ideal conditions (such as low light, dusty environments, or complex backgrounds), objects are prone to being missed, positioning errors, or incomplete depth information, leading to a significant increase in grasping failure rates. For non-conductive objects, especially materials with smooth surfaces or low dielectric constants, vision systems struggle to accurately capture changes in near-distance distances, making sub-millimeter-level soft-contact positioning impossible.

[0004] The trade-off between accuracy and range of a single sensor: Existing non-contact proximity sensors (such as optical proximity sensors or ultrasonic sensors) are effective for coarse positioning at long distances, but their accuracy at close distances is limited and they are susceptible to environmental noise. Tactile sensors (such as piezoresistive or piezoelectric sensors) can provide force feedback after contact, but they are contact-based sensing and cannot achieve high-precision distance prediction before grasping, easily causing damage to objects or missing the target. Capacitive sensors have advantages in non-contact proximity detection, responding to both conductive and non-conductive objects (through changes in dielectric constant), and have been applied in flexible electronic skin and tactile arrays. However, the detection range of a single capacitive sensor is typically limited to a few millimeters to centimeters, and it is significantly affected by humidity, dust, and electromagnetic interference, making it difficult to independently achieve high-precision positioning throughout the entire process from long distance to close distance.

[0005] Environmental interference and insufficient robustness: Factors such as humidity fluctuations, dust accumulation, and electromagnetic noise commonly encountered in industrial settings can significantly interfere with the accuracy of capacitance measurements, leading to capacitance value drift or false triggering. Although existing technologies have local compensation schemes (such as filtering or shielding), they lack a systematic, multi-modal fusion mechanism, making it difficult to maintain stable performance in dynamic and complex environments.

[0006] Existing fusion solutions lack specificity: In recent years, some studies have attempted to fuse radar (such as millimeter-wave radar) with cameras for the grasping of transparent objects (such as the FuseGrasp system), utilizing the penetrating characteristics of radar to compensate for the lack of visual depth. However, such solutions are mainly for transparent / reflective objects, and the focus of fusion is on depth completion and camera data enhancement. Dedicated optimization is still lacking for close-range, high-precision distance perception and interference compensation for non-conductive ordinary objects. While pure vision or vision-tactile fusion methods are widely used in grasping detection, they have not effectively resolved the contradiction between non-contact, high-precision positioning and environmental adaptability.

[0007] In summary, existing robot grasping and positioning technologies generally suffer from problems when handling non-conductive objects, such as discontinuous perception at near and far distances, difficulty in balancing accuracy and range, poor robustness to environmental interference, and insufficient adaptability to non-conductive materials. These shortcomings limit the application of robots in complex industrial environments and flexible manufacturing scenarios, necessitating a novel multimodal, non-contact, high-precision positioning method to achieve reliable and accurate grasping operations. Summary of the Invention

[0008] The present invention aims to solve the technical problems existing in the prior art or related technologies.

[0009] Therefore, the technical solution adopted by the present invention is: a high-precision positioning method for robot grasping. The method is applied to a robot system including a robot body and a gripper assembly. By setting a radar sensor module near the gripper assembly and setting a capacitive sensor module at the fingertip of the gripper assembly, position information of different precision levels is obtained at different stages when the gripper assembly approaches the target object.

[0010] This invention fuses radar detection information and capacitance detection information, and combines environmental interference compensation and attitude and force control mechanisms to continuously correct the position and attitude of the gripper assembly. This enables the robot to quickly approach the target object at a long distance and achieve precise alignment at close distance. Furthermore, it determines the gripping state and controls the retreat during the gripping process, thereby achieving high-precision positioning and stable gripping of the target object.

[0011] In a preferred example, the configuration is further as follows: when a large distance is maintained between the gripper assembly and the target object, the radar sensor module is used to perform non-contact detection on the target object to obtain the initial relative position information between the gripper assembly and the target object, and the robot body is controlled to drive the gripper assembly to move towards the target object based on the initial relative position information; when the distance between the gripper assembly and the target object decreases to within a preset threshold range, the capacitive sensor module is enabled to perform fine detection on the distance between the gripper assembly and the target object.

[0012] The specific technical effect is that by adopting sensing methods with different detection accuracy levels in stages, while ensuring the efficiency of long-distance rapid positioning, high-precision positioning at close range can be achieved, avoiding the problem that a single sensing method cannot balance efficiency and accuracy across the entire range.

[0013] In a preferred example, the detection information acquired by the radar sensor module and the detection information acquired by the capacitive sensor module are further configured to be fused, and different weights are assigned to different detection information according to the change in the distance between the gripper assembly and the target object, so as to obtain continuous and stable relative position information of the gripper assembly relative to the target object.

[0014] The specific technical effect is that by using a fusion processing method, the positioning information can be smoothly transitioned between different detection stages, avoiding discontinuity or jitter in the positioning information during the switching of detection methods, thereby improving the stability and reliability of the positioning process.

[0015] In a preferred embodiment, the capacitance sensor module is further configured such that: the capacitance sensor module includes a capacitance detection array composed of multiple electrode units; by analyzing the differences in capacitance changes detected by different electrode units, the lateral offset and attitude tilt trend of the gripper assembly relative to the target object are estimated, and the position and attitude of the gripper assembly are adjusted accordingly.

[0016] The specific technical effect is that it can not only realize the distance positioning between the gripper assembly and the target object, but also simultaneously correct lateral offset and attitude error, thereby improving the alignment accuracy of the gripper assembly with the target object's grasping area.

[0017] In a preferred example, the configuration is further as follows: the moving direction, moving speed and attitude angle of the gripper component are dynamically corrected based on the fused relative position information; when an abnormal motion state of the gripper component is detected or the fluctuation of the detection signal exceeds a preset threshold, the approach speed of the gripper component is reduced or the attitude adjustment is paused.

[0018] The specific technical effect is that by introducing attitude adjustment and motion constraint mechanisms, the risk of misjudgment or collision of the gripper component when approaching the target object is reduced, thereby improving the safety and stability of the system operation.

[0019] In a preferred example, the system is further configured to monitor changes in ambient humidity, temperature, or electromagnetic environment, and to compensate and correct the capacitance detection signal based on changes in environmental parameters, so as to reduce the impact of environmental changes on positioning accuracy.

[0020] The specific technical effect is that by compensating for environmental interference factors, the capacitance detection can maintain high detection stability and positioning accuracy under complex environmental conditions, thereby improving the system's adaptability to environmental changes.

[0021] In a preferred example, the gripper assembly is further configured to: after the gripper assembly performs the gripping action, determine the gripping state based on the stability change of the capacitance detection signal and the drive state information of the gripper assembly; when it is determined that the gripping state does not meet the preset conditions, control the gripper assembly to revert to the positioning stage and re-execute the position and attitude adjustment process.

[0022] The specific technical benefits are as follows: by introducing a capture status determination and rollback control mechanism, erroneous operations caused by capture failures are avoided, thereby improving the overall capture success rate and system reliability.

[0023] The beneficial effects achieved by this invention are as follows: 1. The present invention employs a fusion perception scheme that uses millimeter-wave radar for coarse positioning at long distances and switches to an embedded capacitive sensor array for high-precision distance perception at close distances. This achieves accurate, non-contact positioning of non-conductive objects, with grasping accuracy consistently reaching sub-millimeter level (better than the performance of traditional vision or single force sensors in complex environments). This significantly improves the accuracy and reliability of robot grasping and avoids damage to objects or grasping failures caused by excessive proximity or collisions.

[0024] 2. This invention addresses common environmental interference factors such as humidity, dust, and electromagnetic noise by designing a multi-layered shielding and dynamic compensation mechanism, including Faraday cage shielding, real-time humidity monitoring and capacitance correction model, dust self-cleaning, and adaptive filtering algorithm. This enables the system to maintain stable measurement accuracy in complex industrial environments, exhibiting strong robustness and wide adaptability, and reducing the negative impact of environmental factors on the success rate of grasping.

[0025] 3. This invention adopts a hierarchical data fusion architecture and a combination of real-time PID attitude / force feedback control to achieve seamless closed-loop control of perception-decision-execution. It has a short response time, low power consumption, and is suitable for various industrial automation, flexible manufacturing, and human-machine collaboration scenarios. It has high engineering practicality and economy, and is easy to integrate and promote on existing robot platforms. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall process of one embodiment of the present invention; Figure 2 This is a schematic diagram of the workflow of one embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0028] It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the invention.

[0029] The following describes, with reference to the accompanying drawings, some embodiments of a robot grasping high-precision positioning method provided by the present invention.

[0030] Combination Figures 1-2 As shown, the present invention provides a high-precision positioning method for robot grasping, which is applied to a robot system, the robot system including at least a robot body and a gripper assembly.

[0031] The robot body is a multi-degree-of-freedom robotic arm structure used to drive the gripper assembly to move and adjust its posture in three-dimensional space. The gripper assembly is installed at the end effector position of the robot body and is used to perform gripping or grasping operations on the target object. The target object is a non-conductive object, including plastic, glass, or ceramic products.

[0032] A radar sensor module is installed near the gripper assembly to perform non-contact detection of the target object when a large distance is maintained between the gripper assembly and the target object, so as to obtain the initial relative position information between the gripper assembly and the target object.

[0033] A capacitive sensor module is provided at the fingertip or contact surface of the gripper assembly. The capacitive sensor module includes a capacitive detection array composed of multiple electrode units, which is used to detect minute changes in the distance between the gripper assembly and the target object as the gripper assembly approaches the target object.

[0034] The robot system also includes a data fusion processing unit, an interference compensation unit, and an attitude adjustment control unit. The units work together through signal connections to achieve high-precision positioning and stable grasping of the target object by the gripper assembly.

[0035] In this embodiment, when the distance between the gripper assembly and the target object is within a large range, the system enters the long-distance positioning stage.

[0036] During this phase, the radar sensor module scans the operating environment and obtains the initial distance information of the target object relative to the gripper assembly by analyzing the relationship between the transmitted signal and the echo signal.

[0037] Based on the initial relative position information obtained by the radar sensor module, the data fusion processing unit controls the robot body to drive the gripper assembly to move towards the target object in a predetermined direction, thereby guiding the gripper assembly to the vicinity of the target object.

[0038] During the long-range positioning phase, the system prioritizes the detection information provided by the radar sensor module to ensure that the gripper assembly can quickly and stably approach the target object within a large spatial range.

[0039] In this embodiment, when the distance between the gripper assembly and the target object decreases to within a preset threshold range, the system switches from the long-distance positioning stage to the close-range precision positioning stage.

[0040] During this stage, the capacitive sensor module is activated, and by detecting the capacitance changes of each electrode unit in the capacitive detection array, it obtains the fine spacing information between the gripper assembly and the target object.

[0041] Because the different electrode units in the capacitance detection array have different spatial distributions, when the gripper assembly is laterally offset or tilted relative to the target object, the capacitance change detected by the different electrode units will be different.

[0042] The data fusion processing unit estimates the relative distance, lateral offset, and attitude tilt trend of the gripper assembly relative to the target object based on the capacitance change distribution of each electrode unit, providing a basis for subsequent attitude correction.

[0043] In this embodiment, the data fusion processing unit is used to fuse the detection information acquired by the radar sensor module and the capacitive sensor module.

[0044] As the distance between the gripper assembly and the target object gradually decreases, the data fusion processing unit assigns different weights to the radar detection information and the capacitance detection information according to the current distance change, so that the system emphasizes the radar detection information at long distances and the capacitance detection information at close distances.

[0045] Through the above fusion processing method, the system can continuously obtain continuous and stable relative position information throughout the approach process, avoiding the problem of discontinuous or jittering position information caused by switching detection methods.

[0046] In this embodiment, the attitude adjustment control unit adjusts the position and attitude of the gripper assembly based on the relative position information output by the data fusion processing unit.

[0047] Specifically, the attitude adjustment control unit controls the robot body to correct the moving direction, moving speed and attitude angle of the gripper assembly based on the estimated relative distance, lateral offset and attitude tilt trend, so that the gripper assembly gradually aligns with the gripping area of ​​the target object as it approaches the target object.

[0048] During the attitude adjustment process, when abnormal vibration of the gripper assembly is detected or the detection signal fluctuation exceeds the preset threshold, the attitude adjustment control unit reduces the approach speed of the gripper assembly or suspends the position adjustment to avoid misjudgment or collision.

[0049] In this embodiment, the interference compensation unit is used to monitor changes in ambient humidity, temperature, and electromagnetic environment.

[0050] By establishing a correspondence between environmental parameters and baseline changes in the capacitance detection signal, when a change in environmental parameters is detected, the interference compensation unit corrects the baseline of the capacitance detection signal, thereby reducing the impact of environmental changes on the fine positioning process and improving the overall positioning stability of the system.

[0051] In this embodiment, when the gripper assembly is guided to a preset safe distance range during the fine positioning stage, the attitude adjustment control unit controls the gripper assembly to perform a gripping action.

[0052] During the grasping process, the system determines the grasping state based on the stability changes of the capacitance detection signal and the driving state information of the gripper assembly.

[0053] When the relative position between the gripper assembly and the target object is stable and the gripping state meets the preset conditions, the system determines that the gripping is complete; when the gripping state does not meet the preset conditions, the system controls the gripper assembly to retract to the positioning stage and re-execute the position and attitude adjustment process.

[0054] In this embodiment, the target object is a non-conductive object, including but not limited to plastic products, glass products, or ceramic products. Using the method of this invention, the gripper assembly can achieve high-precision positioning and stable grasping of the target object without rigid contact.

[0055] The radar module employs the FMCW (Frequency Modulated Continuous Wave) principle, calculating distance by transmitting a frequency-modulated signal and receiving the echo. The formula is: d=(c*Δf) / (2*B), where c is the speed of light, Δf is the frequency difference, and B is the bandwidth (set to 4GHz). For non-conductive objects, the module optimizes signal processing, using the DBSCAN clustering algorithm (ε=0.5, minPts=3) to extract object contours from point cloud data. During installation, the radar antenna faces forward of the gripper with a 60° field of view to cover the gripping path. The module housing is made of ABS plastic with an IP65 waterproof rating.

[0056] The capacitor module is based on the parallel-plate capacitor principle: C = ε*A / d, where ε is the dielectric constant, A is the electrode area, and d is the spacing. When the gripper approaches a non-conductive object, the object acts as a dielectric, causing C to increase. ΔC is measured via ADC conversion, and d ≈ ε*A / C is calculated. The electrode array is arranged in a 2x2 grid, covering the gripper finger pad surface. The material is copper foil coated with a flexible PDMS (polydimethylsiloxane) substrate to adapt to the curved surface of the object. Sensor sensitivity is improved through differential measurement, with noise <0.1pF. For non-conductive objects (such as plastic with εr = 2.5), a preset compensation factor is used to optimize accuracy.

[0057] Humidity Compensation: Integrated DHT22 sensor monitors relative humidity (RH). When RH > 70%, the compensation model is activated: C_corrected = C_measured / (1 + 0.002 * RH). Dust Shielding: A vibration motor (50Hz frequency) is added to the finger pad surface, self-cleaning once per minute; auxiliary air nozzles blow away particles. Electromagnetic Noise Shielding: The sensor is wrapped in an aluminum foil Faraday cage, with a grounding resistance < 1Ω; an integrated MurataBNX002 EMI filter suppresses noise from 50-500MHz. Additional integrated TMP36 temperature sensor compensates for thermal effects (adjusted by 0.05% for every 1°C change) and MPU6050 IMU detects vibration (threshold 0.1g).

[0058] The above embodiments fully describe the specific implementation process of the robot grasping high-precision positioning method of the present invention, which can effectively solve the problems of insufficient positioning accuracy, large environmental interference and poor grasping stability in the robot grasping process in the prior art.

[0059] The usage process of this invention: like Figure 2 As shown, the method includes the following steps: Initialization: Power on the system and perform a self-test on the sensor (radar echo intensity > -80dBm, capacitance baseline C0 < 10pF). Calibrate using a standard target (plastic plate, 10cm distance).

[0060] Long-range detection: The radar scans the environment to locate objects. If a non-conductive object (reflection intensity < -60dBm) is detected, the coarse spacing is calculated, and the guide arm approaches.

[0061] Sensing switching: When d < 10cm, the capacitive sensor is activated to collect ΔC.

[0062] Data fusion and compensation: EKF fusion, application of interference compensation (e.g., if RH=80%, adjust C_corrected).

[0063] Attitude adjustment: Based on fused d, PID control of gripper speed (v=0.1m / s*(d-d_target)), target d=0.5mm soft contact.

[0064] Grasping execution: The gripper closes, and force feedback confirms successful gripping (ΔC stabilizes).

[0065] End and Log: Release the object and record performance (accuracy RMSE < 0.1mm).

[0066] This embodiment demonstrates the practicality of the invention; in actual applications, the parameters can be adjusted according to specific needs.

[0067] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0068] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A robot grasping high-precision positioning method, characterized in that, Applied to a robot system including a robot body and a gripper assembly, the method includes the following steps: S1: Using a radar sensor module located near the gripper assembly, non-contact detection of the target object is performed while maintaining a large distance between the gripper assembly and the target object to obtain the initial relative position information between the gripper assembly and the target object. S2: Based on the initial relative position information, control the robot body to drive the gripper assembly to move towards the target object; when the distance between the gripper assembly and the target object decreases to within a preset threshold range, activate the capacitive sensor module set at the fingertip of the gripper assembly, and obtain fine distance information between the gripper assembly and the target object by detecting changes in capacitance. S3: The detection information obtained by the radar sensor module and the detection information obtained by the capacitive sensor module are fused to obtain the continuous relative position information of the gripper assembly relative to the target object. S4: Based on the fused relative position information, adjust the position and orientation of the gripper assembly so that the gripper assembly gradually aligns with the grasping area of ​​the target object; after the gripper assembly reaches the preset grasping conditions, control the gripper assembly to perform the grasping action.

2. The robot grasping high-precision positioning method according to claim 1, wherein, The radar sensor module is used to provide relative position information when the distance between the gripper assembly and the target object is greater than a preset first threshold, and the capacitive sensor module is used to provide fine distance information when the distance between the gripper assembly and the target object is less than a preset second threshold, wherein the first threshold is greater than the second threshold, so as to form a hysteresis interval for switching detection modes.

3. The robot grasping high-precision positioning method according to claim 1 or 2, characterized in that, The capacitance sensor module includes a capacitance detection array formed by multiple electrode units, and the method further includes: estimating the lateral offset and attitude tilt trend of the gripper assembly relative to the target object based on the differences in capacitance changes detected by different electrode units.

4. The robot grasping high-precision positioning method according to claim 3, characterized in that, The step of adjusting the position and orientation of the gripper assembly includes: correcting the moving direction and moving speed of the gripper assembly according to the lateral offset and orientation tilt trend, so that the gripper assembly gradually aligns with the gripping area of ​​the target object as it approaches the target object.

5. The robot grasping high-precision positioning method according to claim 1, wherein, When fusing the detection information acquired by the radar sensor module and the detection information acquired by the capacitive sensor module, different weights are assigned to different detection information based on the change in the distance between the gripper assembly and the target object.

6. The robot grasping high-precision positioning method according to claim 1, wherein, The method further includes: monitoring changes in ambient humidity, temperature, or electromagnetic environment, and compensating and correcting the capacitance detection signal according to changes in environmental parameters, so as to reduce the impact of environmental changes on positioning accuracy.

7. The robot grasping high precision positioning method of claim 1, wherein, When the gripper assembly approaches the target object, if abnormal vibration or fluctuation of the detection signal exceeds a preset threshold, the approach speed of the gripper assembly is reduced or the position adjustment is paused.

8. The robot grasping high precision positioning method of claim 1, wherein, After the gripper assembly performs the gripping action, the gripping state is determined based on the stability change of the capacitance detection signal and the driving state information of the gripper assembly.

9. The robot grasping high-precision positioning method according to claim 8, characterized in that, When it is determined that the gripping state has not met the preset conditions, the gripper assembly is controlled to retract to the positioning stage and the position and attitude adjustment steps are re-executed.

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