Intelligent mobile robot obstacle recognition capability evaluation method for household complex application scene

Abstract

The invention discloses an intelligent mobile robot obstacle recognition capability evaluation method for a household complex application scene, and an adopted evaluation system comprises a plurality of collision sensors which are installed on the side surface of a detected robot in the circumferential direction. A two-axis force sensor composed of a fixed substrate, a dielectric layer capable of deforming and a detection substrate is arranged in the collision sensor, a fixed electrode plate is arranged on the fixed substrate, and a movable induction electrode plate is arranged on the detection substrate. The fixed electrode plate, the dielectric layer and the induction electrode plate form a capacitor, and the fixed substrate is fixedly connected with a detected robot. According to the invention, the robot obstacle object identification capability can be represented based on the collision mode of the robot and the surrounding obstacle and the collision force.

Description

Technical Field

[0001] The present invention relates to an obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios. Background Art

[0002] With the rapid development of science and technology, service robots, as an emerging product, are increasingly being applied to human social life because they can replace humans to complete work tasks in specific scenarios. In particular, intelligent mobile service robot products for household and commercial scenarios such as sweeping, mopping, air purification, mowing, food delivery and material distribution need to have more autonomy and intelligence because their usage scenarios are closely related to people's living environment. The premise for mobile robots to achieve high autonomy and intelligence is that they need to recognize the surrounding environment information and make action decisions quickly and accurately. Obstacle recognition is a key intelligent technology for mobile robots to achieve effective obstacle avoidance in complex application scenarios. It mainly relies on its own sensor device to detect obstacles in the surrounding environment, and makes judgments through software algorithm recognition to adopt corresponding obstacle avoidance strategies, so as to avoid collisions between robots and surrounding people and scenes. In the face of various mobile service robot products on the market, the robot's obstacle recognition ability directly determines the robot's obstacle avoidance safety. It is an important basis for safe obstacle avoidance in complex application scenarios, and it is also a key technical indicator for assessing the mobile service robot's ability to achieve various work tasks. Therefore, it is extremely important to detect intelligent obstacle avoidance and obstacle scene recognition for intelligent mobile robots in low-speed unmanned driving application scenarios for household, commercial and similar purposes.

[0003] At present, the industry's research on the robot's obstacle recognition ability focuses on the research of collision detection or obstacle avoidance technology, as well as the optimization of the product's structure, key components, software and hardware control systems, etc. For example:

[0004] 1. Publication No. CN115056263A relates to a robot collision avoidance test detection device, which detects the avoidance of obstacles of different shapes at different positions, controls the position of each obstacle block, and is conducive to testing the robot's collision avoidance ability against moving obstacles.

[0005] 2. The robot collision detection method, robot system and computer-readable medium involved in Publication No. CN116476043A, the method obtains the current value of each joint at every preset time period within a preset monitoring cycle, and pre-analyzes the current value of each joint. If the analysis shows that the current value fluctuation within the preset monitoring cycle is abnormal, the theoretical joint torque and the theoretical current value are further calculated, and the theoretical current value is compared with the current value for verification. If the analysis shows that the current value fluctuation within the preset monitoring cycle is not abnormal, there is no need to calculate the theoretical current value. The method of pre-analyzing the current value within the preset monitoring cycle is adopted to reduce unnecessary calculation amount and calculation time during collision detection, thereby meeting the real-time requirements of the robot arm.

[0006] 3. The collision detection method, device, detection equipment and / or readable storage medium involved in publication number CN114474076A can accurately detect whether the robot has a collision without setting up a force sensor, obtain the current moment and the actual torque feedback at the current moment, and obtain the target torque feedback based on the relationship between the current moment, the torque feedback related information and the feedback moment, wherein the relationship is obtained based on historical torque feedback waveform data of at least one historical period, the torque feedback related information is used for indicator torque feedback, and based on the actual torque feedback and the target torque feedback, it is determined whether the robot is currently in a collision.

[0007] 4. Publication No. CN117710454A involves an obstacle recognition method, device, storage medium and robot. After determining the current captured image and current posture information of the robot, the three-dimensional model of the target scene matching the current captured image can be determined based on the current captured image. Then, the current posture information can be used to determine the target depth image from the target three-dimensional scene, and the target depth image can be used to determine the obstacle information of the robot in the capture direction.

[0008] 5. Announcement No. CN219594469U relates to an obstacle recognition device for a cleaning robot to avoid obstacles. Compared with the existing obstacle recognition devices, the obstacle recognition device for a cleaning robot to avoid obstacles is provided with a rotation and pitch angle adjustment module, which can identify and detect obstacles in all directions and objects at high places, which is beneficial to improve the detection range of the device. At the same time, the device is provided with a radar detection structure that can detect garbage and terrain, which is beneficial to avoid the cleaning robot from falling. The device is also provided with a positioning and navigation module. When the cleaning robot is low on power, it can be controlled to automatically return to the charging base for charging, and can avoid the cleaning robot from being lost, which is beneficial to improve the convenience of the device.

[0009] At present, there is research and development in the industry on robot collision force detection and related professional fields of collision avoidance methods. Most of them focus on the optimization and improvement of the product's obstacle avoidance technology and collision force detection in specific single situations, such as specifying the obstacle type and position or specifying the robot's operating conditions. They do not consider the robot's obstacle scene recognition and collision avoidance situations in real complex and changeable obstacle scenes (such as head-on collision, side collision and other effective collision information), and cannot simulate the robot's recognition ability and obstacle avoidance level in real scene environments. The test is not universal and the test implementation conditions are difficult to reproduce.

[0010] Currently, there is a lack of mobile robot collision dynamic real-time monitoring (side collision, head-on collision, collision duration, etc.) detection systems that can realize complex application scenarios (static obstacles, dynamic obstacles, obstacle uncertainty, etc.) and evaluation methods for characterizing the robot's obstacle object recognition capabilities. Summary of the invention

[0011] The purpose of the present invention is to provide a method for evaluating the obstacle recognition ability of an intelligent mobile robot for complex home application scenarios, and to characterize the robot's obstacle object recognition ability based on the collision mode and collision force between the robot and the surrounding obstacles.

[0012] The technical solution of the present invention is as follows:

[0013] An evaluation method for the obstacle recognition capability of an intelligent mobile robot for complex home application scenarios adopts the following evaluation system:

[0014] The evaluation system comprises a plurality of collision sensors installed along the circumferential direction on the side of the robot under test, wherein a two-axis force sensor composed of a fixed substrate, a dielectric layer capable of deformation and a detection substrate is arranged inside the collision sensor, a fixed electrode plate is arranged on the fixed substrate, a movable sensing electrode plate is arranged on the detection substrate, the fixed electrode plate, the dielectric layer and the sensing electrode plate constitute a capacitor, and the fixed substrate is fixedly connected to the robot under test,

[0015] When the detection substrate of the collision sensor hits an obstacle, it is subjected to a normal force F 正 That is, the positive collision force or the tangential force F 切 That is, when there is a lateral collision force, the distance or the facing area between the fixed electrode plate and the sensing electrode plate of the collision sensor changes. When the distance between the fixed electrode plate and the sensing electrode plate decreases, the capacitance value of the capacitor increases. When the facing area between the fixed electrode plate and the sensing electrode plate decreases, the capacitance value of the capacitor decreases. Based on this change characteristic, the forward collision force F is obtained. 正 , side impact force F 切The relationship between the capacitance change and the force is obtained, which is used to measure the collision sensor's forward collision or side collision, as well as the collision force.

[0016] The evaluation method comprises the steps of:

[0017] S1. Build various obstacle test scenes in a home environment according to the shapes, sizes, materials, colors and spatial positions of different obstacles, and install cameras in the test scenes.

[0018] S2. The robot under test is fixed with a plurality of collision sensors on the side along the circumferential direction, and the functions of the robot under test are checked and confirmed to be normal and fully charged, and the robot under test is made familiar with the preset test scene.

[0019] S3. Set the starting position and ending target position of the robot under test in the test scenario test or set the stopping position of the robot under test after completing the specified work task.

[0020] S4. Real-time monitoring and recording of the collisions between the robot under test and the surrounding obstacles in the test scene during the entire test process, including monitoring and recording the number of collisions, collision force time, and collision force of forward and side collisions, and obtaining the maximum value, effective value, and average value of the collision force.

[0021] S5. During the test, the camera is used to monitor the operation of the robot under test and collect real-time data.

[0022] S6. Determine the specific part of the robot under test where the collision occurs based on the sampling point numbers of the collision sensors on the robot under test, determine whether a lateral collision or a forward collision occurs based on the force of the collision sensor, and determine the type of obstacle and the location of the collision based on the camera video acquisition data.

[0023] S7. Determine the obstacle recognition capability of the robot under test by the number of effective collisions, collision strength, and types of obstacles successfully avoided.

[0024] The present invention installs a collision sensor on the side of the robot under test in a 360° circumferential direction. The outer layer of the detection substrate of the collision sensor is a protective shell, which serves as the force-bearing contact surface when the robot collides with an obstacle, wherein the dielectric layer of the collision sensor can be a positive silicone dielectric layer. The present invention can realize the measurement of a positive collision or a side collision of the robot under test. By analyzing key technical parameters such as the number of collisions of the effective collision force, the collision strength, whether the collision type is a positive collision or a side collision, and the accumulated force action time, the robot's scene recognition or obstacle avoidance capabilities can be comprehensively and accurately analyzed in the same test environment and the same obstacle scene.

[0025] The present invention also has the following preferred design:

[0026] The inductive electrode plate of the collision sensor of the present invention is connected to the grounded metal end, and the fixed electrode plate is connected to the data acquisition module. The data acquisition module includes an integrated capacitance measurement circuit encapsulated in the outer layer of the substrate, a digital signal processing chip and its peripheral circuits, which are used to collect the capacitance value and capacitance value change of the collision sensor capacitor, realize the analog-to-digital conversion of the capacitance signal, and obtain the sensing induction signal of the collected capacitance value and capacitance value change. The data acquisition module is encapsulated in the outer layer of the fixed substrate. The capacitance measurement circuit is usually composed of a reference capacitor and multiple measurement capacitors. The reference capacitor is a fixed capacitor used to calibrate the capacitance measurement circuit. The measurement capacitor is the capacitance between the inductive electrode plate and the fixed electrode plate of the capacitive force sensor. When the capacitance in the capacitance measurement circuit changes, it will cause the voltage in the circuit to change. By measuring the voltage change in the circuit, the change in capacitance is calculated, and then the applied force is derived.

[0027] The data acquisition module communicates with the data processing module of the collision sensor. The data processing module reads the sensing signal output by the data acquisition module circuit and processes it to obtain whether the force type of the sensing signal sensed by the collision sensor is a forward collision force or a lateral collision force, and obtains the force value.

[0028] The present invention can read and record the capacitance value change of the capacitive force sensor in real time through the serial port of the host computer and form a data table, and then obtain the corresponding relationship mapping data table of capacitance value and force (that is, the corresponding relationship between the capacitance value change and different types of forces) by fitting the capacitance value change and the corresponding force change, calculate the capacitance value change according to the voltage change in the measuring circuit under the corresponding force type of each capacitor, and then map the data table according to the corresponding relationship between the capacitance value change and the actual force, and find the value of the force.

[0029] In the present invention, a data transceiver is fixed on the top of the robot under test, and the collision sensor transmits the collected data to the data transceiver through CAN communication, and the data transceiver uploads it to the host computer, and the host computer monitors, stores and intercepts effective collision information in real time.

[0030] Preferably, the main control board of the data transceiver converts and filters the collected data before uploading it to the host computer.

[0031] In order to improve the anti-interference ability and reliability of the circuit, it is ensured that a stable sampling output signal can be provided for the circuit to meet the measurement of the short-term contact force between the robot body sensor and the surface of the surrounding environment obstacle. The data acquisition module of the present invention adopts an AD7147 chip with an IIC protocol, and has multiple external capacitor input detection channels. In any measurement cycle, the pin of the external capacitor input detection channel is excited by a square wave, and a ∑-ΔADC converter is used to continuously sample the charge passing through the pin. The output signal of the ∑-ΔADC converter is processed by a digital filter and saved to a register. The ∑-ΔADC converter includes a sampling and holding amplifier, a differential amplifier or a subtractor, an analog low-pass filter or an integrator, a 1-bit A / D converter, i.e., a comparator, a 1-bit DAC and a digital filter, wherein the sampling and holding amplifier, the differential amplifier or a subtractor, the analog low-pass filter or an integrator, the 1-bit A / D converter, and the 1-bit DAC constitute a ∑-ΔADC modulator, which is responsible for converting the analog input signal into a high-speed pulse digital signal for modulation, and the digital filter is responsible for filtering out the digital signal containing noise.

[0032] The data processing module communicates with the AD7147 chip through the IIC protocol to perform initialization and data reading, and converts force and capacitance values ​​through calibration.

[0033] The conversion modulation process of the sigma-delta ADC converter includes:

[0034] Oversampling disperses the quantization noise into a wider frequency range, reducing the impact of noise within the target bandwidth.

[0035] Noise shaping pushes the quantization noise in the low frequency band to the high frequency band.

[0036] As a feasible implementation method: the model of the AD7147 chip is the AD7147ACPZ-1500RL7 chip with the IIC protocol, and a 2.2K pull-up resistor is connected in series between the clock bus SCL and the data line SDA of the IIC communication and VCC to prevent the current in the circuit from being too large and burning the circuit; a capacitor is connected in series between BAIS, VCC and GND to filter out stray capacitance, and the ADD1 pin and ADD0 pin on the AD7147 chip are set to high / low levels to realize different addresses of the tactile sensor chip, and five wires of VCC, GND, INT, SCL and SDA are led out from the AD7147 chip and connected to the sensor acquisition module through flying wires to realize module separation.

[0037] Furthermore, the AD7147 chip has two communication modes: IIC communication and SPI communication, and has the characteristics of high precision, good linearity and high resolution. The chip has 24 pins, 13 external capacitor input detection channels CIN0-CIN12, and adopts 4mm×4mm LFCSP_WQ package. The normal power supply voltage is 2.6V-3.6V. When working in full power mode, the working current is only 1mA; when working in low power mode, the working current is 26μA.

[0038] The side of the robot to be tested of the present invention is provided with 36 collision sensors along the circumferential direction. The electrodes of the capacitors of the collision sensors adopt differential finger electrodes, which can improve the sensitivity and resolution of the sensors.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The present invention installs multiple split-type connected block capacitive force sensors (the number of sensors can be determined according to the size of the robot body and the actual needs of the test, and the capacitive force sensors can be easily disassembled, installed and maintained from the robot body) as a collision sensor for testing, so as to measure and analyze the key parameters of the dynamic multi-axial torque of the robot and the surrounding obstacles, which is used to characterize the robot's obstacle object recognition ability. By measuring the key measurement parameters of the collision force, the number of collisions and the force action time, the force mode (lateral collision, forward collision), the type of collision obstacle, the location of the collision obstacle and the specific part of the robot where the collision occurs, the detection data is analyzed and counted, and the obstacle recognition ability of the robot under test is determined by the number of effective collisions, the collision force and the type of successfully avoided obstacles, avoiding the uncertainty of the evaluation methods such as the single test scene, poor test real-time performance, complex test conditions and difficult test implementation of the existing test methods. The present invention provides a third-party laboratory with a method for evaluating the obstacle avoidance effect of intelligent mobile robots for complex home environments and multi-obstacle scene recognition based on real-time dynamic collision torque monitoring, filling the gap in the certification and testing TIC industry.

[0041] 2. The present invention is highly versatile and can be applied to autonomous mobile robot applications in home, commercial and office environments and similar indoor environments. This can be used as a basis for evaluating the robot's ability to identify obstacles, thereby achieving the purpose of characterizing the intelligent obstacle avoidance performance level of the robot's autonomous navigation operation.

[0042] 3. The present invention designs the circuit of the sensor capacitance acquisition module and the data processing module, adopts ∑-ΔADC conversion technology, improves the circuit's anti-interference ability and reliability, and ensures that a stable sampling output signal can be provided to the circuit to meet the measurement of the short-term contact force between the robot body sensor and the surface of the surrounding environment obstacle. The internal structure of the collision sensor of the present invention mainly has three layers, and the thickness of the capacitive force sensor used can be very thin, only 4mm, and the volume is small. It can be arranged at the specified position of the robot under test according to actual needs to realize the collision force data test of the robot.

[0043] 4. The present invention arranges an appropriate number of block-shaped capacitive force sensors as sampling points for collision force detection on the 360° fuselage side walls of the robot's moving main body according to the actual situation of a certain gap, and numbers the sensor sampling points. When the sensor at a certain sampling point collides with the surrounding environment, the test personnel can obtain the coordinate information and collision force information of the sampling point through the upper computer graphics software to quickly determine the specific collision location on the robot.

[0044] 5. The present invention uses differential finger electrodes to improve the sensitivity and resolution of the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the structure of an evaluation system for an evaluation method of obstacle recognition capability of an intelligent mobile robot for complex home application scenarios provided by the present invention.

[0046] Figure 2 It is a working schematic diagram of the collision sensor of the present invention;

[0047] Figure 3 for Figure 2 The working principle diagram of the collision sensor;

[0048] Figure 3a for Figure 3 The structural principle diagram of the state in Figure a;

[0049] Figure 3b for Figure 3 The structural principle diagram of the state in Figure b;

[0050] Figure 3c for Figure 3 The structural principle diagram of the state in Figure c;

[0051] Figure 4 This is a structural block diagram of an evaluation system of an evaluation method for obstacle recognition capability of an intelligent mobile robot for complex home application scenarios according to the present invention;

[0052] Figure 5 A circuit diagram of a data acquisition module of a collision sensor in an embodiment;

[0053] Figure 6 A circuit diagram of a data processing module of a collision sensor in an embodiment;

[0054] Figure 7 A schematic diagram of capacitance signal measurement of AD7147 based on ∑-Δ ADC conversion technology in an embodiment;

[0055] Figure 8 Schematic diagram of the working principle of the ∑-Δ ADC converter in the embodiment;

[0056] Fig. 9 The present invention is a flow chart of a method for evaluating the obstacle recognition capability of an intelligent mobile robot for complex home application scenarios.

[0057] Description of reference numerals:

[0058] 1- robot under test; 2- collision sensor; 21- fixed substrate; 22- dielectric layer; 23- detection substrate; 24- fixed electrode plate; 25- sensing electrode plate; 26- fixed substrate outer layer; 27- detection substrate outer layer. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0060] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0061] It should be understood that the present invention can be implemented in different forms and should not be interpreted as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0062] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0063] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0064] like Figures 1 to 9 As shown in the figure, an evaluation method for obstacle recognition capability of an intelligent mobile robot for complex home application scenarios adopts the following evaluation system:

[0065] The evaluation system includes a plurality of collision sensors 2 installed along the circumferential direction on the side of the robot 1 to be tested. A two-axis force sensor composed of a fixed substrate 21, a dielectric layer 22 capable of deformation and a detection substrate 23 is arranged inside the collision sensor 2. A fixed electrode plate 24 is arranged on the fixed substrate 21, and a movable sensing electrode plate 25 is arranged on the detection substrate 23. The fixed electrode plate 21, the dielectric layer 22 and the sensing electrode plate 25 constitute a capacitor. The fixed substrate 21 is fixedly connected to the robot 1 to be tested.

[0066] like Figure 3 , Figure 3a , Figure 3b and Figure 3c As shown, when the detection substrate 23 of the collision sensor 2 hits an obstacle, it is subjected to a normal force F 正 That is, the positive collision force or the tangential force F 切 That is, when there is a lateral collision force, the distance or the facing area between the fixed electrode plate 24 and the sensing electrode plate 25 of the collision sensor 2 changes. When the distance between the fixed electrode plate 24 and the sensing electrode plate 25 decreases, the capacitance value of the capacitor increases. When the facing area between the fixed electrode plate 24 and the sensing electrode plate 25 decreases, the capacitance value of the capacitor decreases. Based on this change characteristic, the forward collision force F is obtained. 正 , side impact force F 切 The relationship between the capacitance change and the force is obtained, which is used to measure the collision sensor's forward collision or side collision, as well as the collision force.

[0067] The evaluation method comprises the steps of:

[0068] S1. Build various obstacle test scenes in a home environment according to the shapes, sizes, materials, colors and spatial positions of different obstacles, and install cameras in the test scenes.

[0069] S2. The robot under test is fixed with a plurality of collision sensors on the side along the circumferential direction, and the functions of the robot under test are checked and confirmed to be normal and fully charged, and the robot under test is made familiar with the preset test scene.

[0070] S3. Set the starting position and ending target position of the robot under test in the test scenario test or set the stopping position of the robot under test after completing the specified work task.

[0071] S4. Real-time monitoring and recording of the collisions between the robot under test and the surrounding obstacles in the test scene during the entire test process, including monitoring and recording the number of collisions, collision force time, and collision force of forward and side collisions, and obtaining the maximum value, effective value, and average value of the collision force.

[0072] S5. During the test, the camera is used to monitor the operation of the robot under test and collect real-time data.

[0073] S6. Determine the specific part of the robot under test where the collision occurs based on the sampling point numbers of the collision sensors on the robot under test, determine whether a lateral collision or a forward collision occurs based on the force of the collision sensor, and determine the type of obstacle and the location of the collision based on the camera video acquisition data.

[0074] S7. Determine the obstacle recognition capability of the robot under test by the number of effective collisions, collision strength, and types of obstacles successfully avoided.

[0075] In one embodiment, the collision sensor 2 is integrated with Figure 5 , Figure 6The data acquisition module and data processing module shown, the sensing electrode plate 25 of the collision sensor 2 is connected to the grounded metal end for grounding, and the fixed electrode plate 24 is connected to the data acquisition module. The data acquisition module includes an integrated capacitance measurement circuit encapsulated in the outer layer of the substrate, a digital signal processing chip and its peripheral circuits, which are used to collect the capacitance value and capacitance value change of the capacitor of the collision sensor 2, realize the analog-to-digital conversion of the capacitance signal, and obtain the sensing induction signal of the collected capacitance value and capacitance value change. The data acquisition module is encapsulated in the outer layer 26 of the fixed substrate. The capacitance measurement circuit is usually composed of a reference capacitor and multiple measurement capacitors. The reference capacitor is a fixed capacitor used to calibrate the capacitance measurement circuit. The measurement capacitor is the capacitance between the sensing electrode plate and the fixed electrode plate of the capacitive force sensor. When the capacitance in the capacitance measurement circuit changes, it will cause the voltage in the circuit to change. By measuring the voltage change in the circuit, the change in capacitance is calculated, and then the applied force is derived.

[0076] The data acquisition module communicates with the data processing module of the collision sensor 2. The data processing module reads the sensing signal output by the data acquisition module circuit and processes it to obtain the force type of the sensing signal sensed by the collision sensor, whether it is a forward collision force or a side collision force, and obtains the force value.

[0077] In one embodiment, the capacitance change of the capacitive force sensor can be read and recorded in real time through the serial port of the host computer to form a data table, and then the capacitance change and the corresponding force change are fitted to obtain a capacitance and force correspondence mapping data table (i.e., the correspondence between the capacitance change and different types of forces). The capacitance change is calculated based on the voltage change in the measuring circuit under the corresponding force type of each capacitor, and then the force value is found based on the correspondence mapping data table between the capacitance change and the actual force.

[0078] In one embodiment, see Figure 4 A data transceiver is fixed on the top of the robot 1 under test. The collision sensor 2 transmits the collected data to the data transceiver through CAN communication, and the data transceiver uploads it to the host computer, and the host computer monitors, stores and intercepts the effective collision information in real time.

[0079] In one embodiment, the main control board of the data transceiver converts and filters the collected data before uploading it to the host computer.

[0080] Since the dielectric layer has the viscoelastic characteristics, the response time of the sensor is greatly reduced and the performance of the sensor is greatly damaged. In order to shorten the response time of the sensor and improve the response speed of the sensor.

[0081] In one embodiment, the data acquisition module uses an AD7147 chip with an IIC protocol and has multiple external capacitor input detection channels. In any measurement cycle, the pin of the external capacitor input detection channel is excited by a square wave, and a ∑-ΔADC converter is used to continuously sample the charge passing through the pin. The output signal of the ∑-ΔADC converter is processed by a digital filter and saved in a register. The ∑-ΔADC converter includes a sampling and holding amplifier, a differential amplifier or a subtractor, an analog low-pass filter or an integrator, a 1-bit A / D converter, i.e., a comparator, a 1-bit DAC, and a digital filter. The sampling and holding amplifier, the differential amplifier or a subtractor, the analog low-pass filter or an integrator, the 1-bit A / D converter, and the 1-bit DAC constitute a ∑-ΔADC modulator, which is responsible for converting the analog input signal into a high-speed pulse digital signal for modulation. The digital filter is responsible for filtering out digital signals containing noise.

[0082] The data processing module communicates with the AD7147 chip through the IIC protocol to perform initialization and data reading, and converts force and capacitance values ​​through calibration.

[0083] The conversion modulation process of the sigma-delta ADC converter includes:

[0084] Oversampling disperses the quantization noise into a wider frequency range, reducing the impact of noise within the target bandwidth.

[0085] Noise shaping pushes the quantization noise in the low frequency band to the high frequency band.

[0086] ∑-ΔADC (Sigma-Delta ADC) is a high-resolution analog-to-digital converter. Its conversion modulator principle and process mainly involve key technical principles and characteristics such as oversampling, noise shaping, digital filtering and extraction as follows:

[0087] Oversampling: ∑-Δ ADC improves the signal-to-noise ratio by increasing the sampling frequency. The sampling frequency of a traditional ADC usually only satisfies the Nyquist theorem, that is, the sampling frequency is at least twice the highest frequency of the signal. However, ∑-Δ ADC uses a higher sampling frequency, that is, oversampling. The benefit of oversampling is that the quantization noise is dispersed over a wider frequency range, thereby reducing the impact of noise within the target bandwidth.

[0088] Noise shaping: The quantization noise in the low frequency band is pushed to the high frequency band through the integrator and comparator in the ∑-Δ ADC modulator. The integrator integrates the difference between the input signal and the feedback DAC output and sends the integrated result to the comparator. The comparator compares the integrated result with a reference voltage to generate a 1-bit digital output. This process is repeated continuously to form a high-speed 1-bit data stream. Due to the existence of the integrator, the quantization noise in the low frequency band is accumulated and pushed to the high frequency band.

[0089] Digital filtering and decimation: The digital filter filters the 1-bit data stream to remove high-frequency noise and improve the signal-to-noise ratio. Decimation is the process of downconverting the filtered 1-bit data stream to reduce the output data rate. This can be achieved by retaining some samples in the frequency domain and discarding the rest. The decimation ratio can be selected as needed, but it should ensure that the output data rate is higher than twice the signal bandwidth to avoid information loss.

[0090] ∑-ΔADC conversion function implementation process:

[0091] Analog signal input: The analog signal is input to the input of the ∑-Δ ADC. This analog signal can come from the output signal of external circuits such as sensors, amplifiers, filters, etc.

[0092] Sampling and quantization: ∑-Δ ADC samples the input signal at a high sampling rate and quantizes the sampled value. Quantization is the process of converting a continuously changing analog signal into a discrete digital signal.

[0093] Σ-Δ modulation: The sampled analog signal is sent to the Σ-Δ modulator for modulation processing. The modulator compares the analog signal with a high-frequency clock signal to generate a high-speed 1-bit data stream. This process involves the coordinated work of components such as differential amplifiers, integrators, and comparators.

[0094] Digital filtering: The generated 1-bit data stream is sent to the digital filter for filtering. The digital filter removes high-frequency noise and improves SNR to obtain a higher-precision digital output.

[0095] Digital output: The data processed by the digital filter is output as a high-precision digital signal, which represents the value of the original analog signal. This digital output can be used for subsequent digital signal processing, storage or transmission.

[0096] In one embodiment: the model of the AD7147 chip is the AD7147ACPZ-1500RL7 chip with the IIC protocol, and a 2.2K pull-up resistor is connected in series between the clock bus SCL and the data line SDA and VCC of the IIC communication to prevent the current in the circuit from being too large and burning the circuit; a capacitor is connected in series between BAIS, VCC and GND to filter out stray capacitance, and the ADD1 pin and the ADD0 pin on the AD7147 chip are set to high / low levels to realize different addresses of the tactile sensor chip, and five wires of VCC, GND, INT, SCL, and SDA are led out from the AD7147 chip and connected to the sensor acquisition module through flying wires to realize module separation.

[0097] Furthermore, the AD7147 chip has two communication modes: IIC communication and SPI communication, and has the characteristics of high precision, good linearity and high resolution. The chip has 24 pins, 13 external capacitor input detection channels CIN0-CIN12, and adopts 4mm×4mm LFCSP_WQ package. The normal power supply voltage is 2.6V-3.6V. When working in full power mode, the working current is only 1mA; when working in low power mode, the working current is 26μA.

[0098] In one embodiment, 36 collision sensors 2 are installed in a circumferential direction on the side of the robot 1 under test. The electrodes of the capacitor of the collision sensor 2 use differential finger electrodes, which can improve the sensitivity and resolution of the sensor. The outer layer 27 of the detection substrate of the collision sensor 2 is a protective shell, which serves as the force-bearing contact surface when the robot 1 under test collides with an obstacle; the inner layer of the detection substrate is a movable sensing electrode plate 25, which is provided with a working electrode and is connected to the grounded metal end; the dielectric layer 22 of the collision sensor 2 is provided with a dielectric block composed of sensitive elements sensed by the collision sensor 2 (which may deform when subjected to force); the inner layer of the fixed substrate 21 of the collision sensor 2 is a fixed electrode plate 24, which is provided with a working electrode, and the working electrode is connected to the digital signal processing chip, which is the AD7147 series digital capacitance conversion chip; the outer layer 26 of the fixed substrate of the collision sensor 2 is used to encapsulate the integrated capacitance measurement circuit and the digital signal processing chip and its peripheral circuits, constitute the data acquisition module of the collision sensor; the circuit of the data acquisition module is used to collect the capacitance value and capacitance value change of the capacitor, realize the analog-to-digital conversion of the capacitance signal, and obtain the sensing signal of the capacitance value and capacitance value change; the digital signal processing chip of the data acquisition module circuit and the STM32 series single-chip microcomputer of the data processing module of the collision sensor 2 communicate data through the IIC protocol, and the STM32 series single-chip microcomputer is used to read the sensing signal output by the collision sensor 2 data acquisition module circuit in real time for processing, and finally obtain the force type (forward collision pressure / lateral collision force) and corresponding force value of the sensing signal sensed by the collision sensor 2. The specific method for analyzing and processing the sensing signal is as follows:

[0099] Combination Figure 3a , Figure 3b and Figure 3c , collision sensor 2 is subjected to a positive collision force F 正 When the sensor 2 is in the state of being moved, a normal force is generated on the surface of the detection substrate 23 of the collision sensor 2, and the dielectric layer 22 is deformed. Assuming that the change is Δd, the distance d between the sensing electrode plate 25 and the fixed electrode plate 24 changes to d-Δd, and the capacitance value of the capacitor formed by the sensing electrode plate 25, the dielectric layer 22 and the fixed electrode plate 24 changes from C0 to C1, and the capacitance value changes by ΔC. d;

[0100] The collision sensor 2 is subjected to the lateral collision force F 切 When the collision sensor 2 detects the substrate 23, a tangential force is generated on the surface of the collision sensor 2, and a tangential displacement △l is generated between the sensing electrode plate 25 and the fixed electrode plate 24 of the collision sensor 2, resulting in a change in the facing area S between the sensing electrode plate 25 and the fixed electrode plate 24. Assuming that the change is ΔS, the facing area S changes to S-ΔS, and the capacitance value of the capacitor formed by the sensing electrode plate 25, the dielectric layer 22 and the fixed electrode plate 24 changes from C0 to C2, and the capacitance value changes by ΔC S ;

[0101] According to the capacitance calculation formula C = εS / (4πkd), when the collision sensor is subjected to a positive collision force, the capacitance value C0 of the capacitor will increase, and the capacitance value change is ΔC d =C1-C0; when the collision sensor is subjected to a lateral collision force, the capacitance value C0 of the capacitor will decrease, and the capacitance value change is ΔC S =C0-C2. According to this change characteristic, the capacitance change of collision sensor 2 is ΔC d , ΔC S With different types of force F 正 、F 切 The corresponding relationship of the force value is obtained by fitting the data after multiple simulation experiments in the early stage of the test. Specifically, the STM32 series microcontroller of the data processing module circuit of the collision sensor 2 sends the obtained data of the capacitance value change to the host computer through USART communication, and uses the host computer serial port to read the data in real time and record the different F 正 、F 切 Relative capacitance change ΔC under force d , ΔC S By fitting the relationship between the capacitance change and the corresponding force change, we can obtain the mapping relationship dataset ΦF between the capacitance change and the force. 正 ={f:ΔC d →F 正} and ΦF 切 ={f:ΔC S →F 切}, and finally the fitted mapping relationship dataset ΦF 正 and ΦF 切Stored in the STM32 series single-chip microcomputer. During the actual collision test, the host computer can obtain the change of the collision force of the collision sensor in real time, calculate the corresponding capacitance value change according to the change of the capacitance value of the sensor signal of the collision sensor data acquisition module circuit, and then reversely calculate the force value and the corresponding data set according to the mapping relationship data set of the capacitance value change and force in the STM32 series single-chip microcomputer of the collision sensor data processing module circuit, so as to realize whether the robot under test collides with the obstacle in a forward collision or a side collision and the magnitude of the collision force.

[0102] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for evaluating the obstacle recognition capability of an intelligent mobile robot for complex home application scenarios, characterized in that: The following evaluation system is used: The evaluation system comprises a plurality of collision sensors installed along the circumferential direction on the side of the robot under test, wherein a two-axis force sensor composed of a fixed substrate, a dielectric layer capable of deformation and a detection substrate is arranged inside the collision sensor, a fixed electrode plate is arranged on the fixed substrate, a movable sensing electrode plate is arranged on the detection substrate, the fixed electrode plate, the dielectric layer and the sensing electrode plate constitute a capacitor, and the fixed substrate is fixedly connected to the robot under test, When the detection substrate of the collision sensor hits an obstacle, it is subjected to a normal force F 正 That is, the positive collision force or the tangential force F 切 That is, when there is a lateral collision force, the distance or the facing area between the fixed electrode plate and the sensing electrode plate of the collision sensor changes. When the distance between the fixed electrode plate and the sensing electrode plate decreases, the capacitance value of the capacitor increases. When the facing area between the fixed electrode plate and the sensing electrode plate decreases, the capacitance value of the capacitor decreases. Based on this change characteristic, the forward collision force F is obtained. 正 , side impact force F 切 The relationship between the capacitance change and the force is obtained, which is used to measure the collision sensor's forward collision or side collision, as well as the collision force. The evaluation method comprises the steps of: S1. Build various obstacle test scenes in a home environment according to the shapes, sizes, materials, colors and spatial positions of different obstacles, and install cameras in the test scenes. S2. The robot under test is fixed with a plurality of collision sensors on the side along the circumferential direction, and the functions of the robot under test are checked and confirmed to be normal and fully charged, and the robot under test is made familiar with the preset test scene. S3. Set the starting position and ending target position of the robot under test in the test scenario test or set the stopping position of the robot under test after completing the specified work task. S4. Real-time monitoring and recording of the collisions between the robot under test and the surrounding obstacles in the test scene during the entire test process, including monitoring and recording the number of collisions, collision force time, and collision force of forward and side collisions, and obtaining the maximum value, effective value, and average value of the collision force. S5. During the test, the camera is used to monitor the operation of the robot under test and collect real-time data. S6. Determine the specific part of the robot under test where the collision occurs based on the sampling point numbers of the collision sensors on the robot under test, determine whether a lateral collision or a forward collision occurs based on the force of the collision sensor, and determine the type of obstacle and the location of the collision based on the camera video acquisition data. S7. Determine the obstacle recognition capability of the robot under test by the number of effective collisions, collision strength, and types of obstacles successfully avoided.

2. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 1 is characterized by: The collision sensor includes a data acquisition module and a data processing module. The induction electrode plate of the collision sensor is connected to the grounded metal end, and the fixed electrode plate is connected to the data acquisition module. The data acquisition module includes an integrated capacitance measurement circuit encapsulated in the outer layer of the substrate, a digital signal processing chip and its peripheral circuits, which are used to collect the capacitance value and capacitance value change of the collision sensor capacitor. The data acquisition module communicates with the data processing module of the collision sensor. The data processing module reads the sensing signal output by the data acquisition module circuit and processes it to obtain whether the force type of the sensing signal sensed by the collision sensor is a forward collision force or a lateral collision force, and obtains the force value.

3. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 1 is characterized by: A data transceiver is fixed on the top of the robot under test. The collision sensor transmits the collected data to the data transceiver through CAN communication, and the data transceiver uploads it to the host computer, and the host computer monitors, stores and intercepts effective collision information in real time.

4. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 3 is characterized by: The main control board of the data transceiver converts and filters the collected data and then uploads it to the host computer.

5. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 2 is characterized by: The data acquisition module adopts an AD7147 chip with an IIC protocol and has multiple external capacitor input detection channels. In any measurement cycle, the pin of the external capacitor input detection channel is excited by a square wave, and a ∑-ΔADC converter is used to continuously sample the charge passing through the pin. The output signal of the ∑-ΔADC converter is processed by a digital filter and saved in a register. The ∑-ΔADC converter includes a sampling and holding amplifier, a differential amplifier or a subtractor, an analog low-pass filter or an integrator, a 1-bit A / D converter, i.e., a comparator, a 1-bit DAC and a digital filter. The sampling and holding amplifier, the differential amplifier or a subtractor, the analog low-pass filter or an integrator, the 1-bit A / D converter, and the 1-bit DAC constitute a ∑-ΔADC modulator, which is responsible for converting the analog input signal into a high-speed pulse digital signal for modulation. The digital filter is responsible for filtering out the digital signal containing noise. The data processing module communicates with the AD7147 chip through the IIC protocol to perform initialization and data reading, and converts force and capacitance values ​​through calibration.

6. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 5 is characterized by: The conversion modulation process of the sigma-delta ADC converter includes: Oversampling disperses the quantization noise into a wider frequency range, reducing the impact of noise within the target bandwidth. Noise shaping pushes the quantization noise in the low frequency band to the high frequency band.

7. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 5 is characterized by: The model of the AD7147 chip selected is the AD7147ACPZ-1500RL7 chip with the IIC protocol. A 2.2K pull-up resistor is connected in series between the clock bus SCL and the data line SDA of the IIC communication and VCC to prevent the current in the circuit from being too large and burning the circuit; a capacitor is connected in series between BAIS, VCC and GND to filter out stray capacitance, and the ADD1 pin and the ADD0 pin on the AD7147 chip are set to high / low levels to realize different addresses of the tactile sensor chip. Five wires, VCC, GND, INT, SCL, and SDA, are led out from the AD7147 chip and connected to the sensor acquisition module through flying wires to realize module separation.

8. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 1 is characterized by: There are 36 collision sensors installed along the circumferential direction on the side of the robot being tested.

9. The obstacle recognition capability evaluation method for an intelligent mobile robot for complex home application scenarios according to claim 1 is characterized by: The electrodes of the capacitor of the collision sensor adopt differential finger electrodes.

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