Control method for five-finger dexterous hand of intelligent explosive-handling robot

Abstract

The invention discloses an intelligent explosive-handling robot five-finger dexterous hand control method, which relates to the technical field of program-controlled manipulators, and comprises the following steps of: calibrating a five-finger dexterous hand, collecting surrounding environment data by using a sensor, preprocessing, analyzing the data through image processing and a mode recognition algorithm, and obtaining a control result of the five-finger dexterous hand. Surrounding environment data and the position, shape and characteristics of the explosive are obtained; according to the surrounding environment data and the position, shape and characteristics of the explosive, a machine learning algorithm is used for planning a safe path from the five-fingered dexterous hand to the position of the explosive, and after planning is completed, the five-fingered dexterous hand is moved to the position near the explosive according to the planned safe path; after the five-finger dexterous hand is moved to the position near the explosive, a machine learning algorithm is used for generating an action sequence, according to the generated action sequence, high-precision force feedback is used for grabbing the explosive, and after grabbing is completed, the explosive handling action is executed. Accurate calibration steps are executed on the five-finger dexterous hand, and accurate control over the actions of all the fingers is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of program-controlled manipulators, in particular to a control method for a five-finger dexterous hand of an intelligent bomb disposal robot. Background Art

[0002] In the development of intelligent bomb disposal robots, the control method of the five-fingered dexterous hand is one of the key research areas. In recent years, with the advancement of robotics and artificial intelligence, especially the application of computer vision, machine learning algorithms and high-precision force feedback systems, robots can perform more complex and delicate tasks.

[0003] In actual operation, traditional bomb disposal robots mostly rely on fixed-mode operating procedures, and their flexibility and adaptability are relatively limited, making it difficult to effectively cope with complex and changing on-site environments. Especially when dealing with explosives of various shapes, traditional mechanical structures and control methods often cannot provide sufficient accuracy and safety, resulting in a low mission success rate. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method for the five-finger dexterous hand of an intelligent bomb disposal robot to solve the problems of inaccurate path planning, lack of flexibility in grasping action and insufficient real-time monitoring of existing bomb disposal robots in complex environments.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a control method for a five-finger dexterous hand of an intelligent bomb disposal robot, which includes calibrating the five-finger dexterous hand, collecting surrounding environment data using sensors, and preprocessing the data, analyzing the data through image processing and pattern recognition algorithms, and obtaining the surrounding environment data and the location, shape and characteristics of the explosive;

[0008] Based on the surrounding environment data and the location, shape and characteristics of the explosive, a machine learning algorithm is used to plan a safe path from the five-fingered dexterous hand to the location of the explosive. After the planning is completed, the five-fingered dexterous hand is moved to the vicinity of the explosive along the planned safe path.

[0009] After the five-fingered dexterous hand is moved near the location of the explosive, a machine learning algorithm is used to generate an action sequence. Based on the generated action sequence, high-precision force feedback is used to grab the explosive. After the grab is completed, the bomb disposal action is performed;

[0010] After performing the bomb disposal action, the status of the smart material is monitored in real time through sensors, providing a real-time video stream to the operator. After completing the task, the robot is returned to the safe area, a comprehensive inspection of the robot is carried out, any abnormalities are recorded, and maintenance is carried out.

[0011] As a preferred solution of the control method of the five-finger dexterous hand of the intelligent bomb disposal robot of the present invention, wherein: the five-finger dexterous hand is calibrated, the surrounding environment data is collected by sensors, and pre-processed, including the following steps:

[0012] Start the sensor array, communication device and controller, run the hardware and software self-test procedures, perform physical and electrical calibration of the five-finger dexterous hand, and calibrate the force sensor and other sensors;

[0013] Activate the sensor installed on the robot. The activated sensor starts to collect data of the surrounding environment to obtain the surrounding environment data;

[0014] The collected surrounding environment data is filtered, denoised and format converted to obtain pre-processed surrounding environment data.

[0015] As a preferred solution of the five-finger dexterous hand control method of the intelligent bomb disposal robot of the present invention, wherein: the data is analyzed by image processing and pattern recognition algorithm to obtain the surrounding environment data and the position, shape and characteristics of the explosive, including the following steps:

[0016] Use image processing algorithms to extract location information, shape information, size information, material information, background information and posture information from the preprocessed surrounding environment data, and apply pattern recognition algorithms to analyze the preprocessed surrounding environment data to determine the location, shape and characteristics of the environmental data and explosives;

[0017] Generate a 3D model and map of the surrounding environment and target objects based on the environmental data and the location, shape and characteristics of the explosives, expressed as,

[0018]

[0019] Where M is the generated 3D model, V is the integration area, p is any point in the environment, D(p) is the distance function, α is the attenuation coefficient, is the feature extraction function, is the shape matching function, N is the number of known feature points in the environment, i is the index of the feature point, and w i is the importance weight of the i-th feature point, β is the three-dimensional model enhancement coefficient, It is a complex information filtering function.

[0020] As a preferred solution of the control method of the five-finger dexterous hand of the intelligent bomb disposal robot of the present invention, wherein: the safe path from the five-finger dexterous hand to the explosive position is planned using a machine learning algorithm according to the surrounding environment data and the position, shape and characteristics of the explosive. After the planning is completed, the five-finger dexterous hand is moved to the vicinity of the explosive according to the planned safe path, including the following steps:

[0021] According to the generated 3D model and map of the surrounding environment and target object, the trained machine learning model is loaded, the surrounding environment data and explosive feature information are input into the machine learning model, and the current state of the robot is input to obtain the constructed machine learning model;

[0022] Use machine learning models to simulate different approach paths, combined with energy-aware adaptive motion planning, consider the current power state, optimize the path to minimize energy consumption, consider safety factors, and plan a safe path from the five-fingered dexterous hand to the location of the explosive through iterative optimization;

[0023] Start the action execution program of the five-finger dexterous hand, move step by step according to the planned safe path, monitor the execution of each action in real time, and continuously monitor the applied force through high-precision force feedback.

[0024] As a preferred solution of the control method of the five-finger dexterous hand of the intelligent bomb disposal robot of the present invention, after the five-finger dexterous hand is moved to the vicinity of the explosive position, a machine learning algorithm is used to generate an action sequence, including the following steps:

[0025] After moving the five-fingered dexterous hand to the designated position according to the planned path, the machine learning model is loaded and the model parameters are verified;

[0026] The collected environmental data and explosive feature information are input into the machine learning model, and the machine learning model is used to simulate different grasping and manipulation actions to generate action sequences, which are expressed as,

[0027]

[0028] Where A is the generated action sequence, T is the total time period, λ is the time decay coefficient, is the machine learning model output function, is the security assessment function, K is the number of known key points in the environment, k is the index of the key point, γ is the action sequence enhancement coefficient, is the information filtering function, a k is the action parameter of the kth key point;

[0029] The finalized motion sequence is loaded into the robot’s embedded control panel.

[0030] As a preferred solution of the five-finger dexterous hand control method of the intelligent bomb disposal robot of the present invention, wherein: according to the generated action sequence, high-precision force feedback is used to grab the explosive, and after the grabbing is completed, the bomb disposal action is performed, including the following steps:

[0031] Load the generated action sequence from the control panel, activate the high-precision force sensor, and use the vision sensor to continuously monitor the position deviation according to the first action of the action sequence, fine-tune the path to ensure accurate positioning, and monitor the contact force in real time through high-precision force feedback during the approach process;

[0032] Use force feedback data to fine-tune the posture and strength of the dexterous hand, dynamically adjust the opening and closing angle and pressure of the fingers according to the shape and surface characteristics of the object, and use high-precision force feedback to accurately control the strength of each finger for grasping according to the grasping instructions in the action sequence;

[0033] After completing the grasping, high-precision force feedback is used to fine-tune the posture of the five-fingered dexterous hand to better implement the bomb disposal action, transfer the explosives to a safe area, implement harmless treatment and dismantle the device.

[0034] As a preferred solution of the five-finger dexterous hand control method of the intelligent bomb disposal robot of the present invention, wherein: after the bomb disposal action is executed, the state of the intelligent material is monitored in real time by a sensor, and a real-time video stream is provided to the operator, including the following steps:

[0035] After executing the bomb disposal action, the data of the smart material status is continuously collected through the sensors installed on the five-finger dexterous hand and the robot to obtain the smart material status data, and the smart material status data is preprocessed to obtain the preprocessed smart material status data;

[0036] Use machine learning models to analyze pre-processed smart material status data to identify abnormal patterns and potential problems;

[0037] When an abnormal situation is detected, the alarm mechanism is triggered immediately to notify the operator and take protective measures, and all abnormal events and their handling process are recorded;

[0038] Activate cameras and visual sensors to capture real-time images of the operation site, use compression technology to reduce bandwidth usage, and transmit video streams to the operator's monitoring terminal, allowing the operator to monitor progress in real time and intervene when necessary.

[0039] As a preferred solution of the five-finger dexterous hand control method of the intelligent bomb disposal robot of the present invention, wherein: after completing the task, the robot is returned to the safe area, a comprehensive inspection of the robot is carried out, any abnormalities are recorded, and maintenance is carried out, including the following steps:

[0040] When the task is completed, the robot's return program is started, the navigation panel is activated, and the collected environmental data and the latest sensor information are used to recalculate a safe path back to the safe zone, generate path planning instructions, and send them to the motion control panel;

[0041] According to the planned safety path, control the robot to move to the designated safety zone, start the comprehensive inspection program, activate all self-inspection functions, generate a detailed inspection report, submit it to the relevant technicians and management department, and formulate a specific maintenance plan based on the inspection results;

[0042] After completing all maintenance work, all inspection records, abnormal reports and maintenance logs will be archived to create a complete task file, which will be stored in the storage medium and cloud server.

[0043] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for controlling the five-finger dexterous hand of an intelligent bomb disposal robot as described in the first aspect of the present invention is implemented.

[0044] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for controlling the five-finger dexterous hand of an intelligent bomb disposal robot as described in the first aspect of the present invention is implemented.

[0045] The beneficial effects of the present invention are as follows: by executing precise calibration steps on the five-fingered dexterous hand, precise control of the movements of each finger is achieved; by utilizing a highly sensitive sensor network to collect data on the surrounding environment and preprocessing it, the position, shape and characteristics of the explosive can be quickly and accurately determined; through the previously obtained environmental and target information, a machine learning algorithm is used to plan a safe path from the current position to the explosive, thereby achieving efficient approach to the target; by calling the machine learning algorithm to generate a series of precise action instructions, the dexterous hand is guided to complete the capture of the explosive, thereby achieving fine-tuning of the grasping force and avoiding danger caused by excessive force. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0047] Figure 1 This is a flow chart of the control method of the five-finger dexterous hand of the intelligent bomb disposal robot in Example 1.

[0048] Figure 2 This is a flowchart of the five-finger dexterous hand grasping in Example 1. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0052] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for controlling the five-finger dexterous hand of an intelligent bomb disposal robot, comprising the following steps:

[0053] S1. Calibrate the five-finger dexterous hand, use sensors to collect surrounding environment data, and perform preprocessing. The specific steps are as follows:

[0054] Turn on the robot's main power switch, ensure that all components have a stable power supply, activate all types of sensors, including but not limited to cameras, lidar, infrared sensors, temperature sensors, check their connection status, start wireless and wired communications, ensure that the communication link with the operator console is normal, activate the embedded control panel, and load the operation panel and control software;

[0055] Automatically run hardware diagnostic programs to check the status of various hardware components, including but not limited to the CPU, memory, storage, and sensor interfaces, confirm that all hardware is working properly, record any abnormalities and generate reports, load and run software self-test scripts, verify that all applications and services are started correctly, and check system logs to confirm that there are no error prompts or warning messages;

[0056] Place the five-fingered dexterous hand in the standard position, use precision measuring tools to measure the angles and displacements of each joint, adjust the zero point setting of the robot arm to ensure that the initial position of each joint is accurate, calibrate the motor driver and encoder to ensure that they can accurately reflect the position changes of the joints, and calibrate the motor driver and encoder to ensure that they can accurately reflect the position changes of the joints;

[0057] Use standard weights and force gauges to perform multi-point calibration on force sensors to ensure that they can provide accurate readings throughout the entire range, record calibration results, and update sensor calibration parameters. Perform zero-point and span calibration on temperature, humidity, and distance sensors to ensure that they can provide reliable data under various environmental conditions. Use calibration certificates and standard reference values ​​to verify sensor accuracy.

[0058] Activate all sensors installed on the robot in sequence, ensure that each sensor is in working condition, verify the communication interface of the sensor, ensure that data transmission is normal, set the working mode of the sensor including but not limited to continuous collection and timed collection, and prepare to start collecting data about the surrounding environment;

[0059] Start acquiring real-time data from various sensors, including but not limited to visual information, distance measurement, temperature, and humidity, and temporarily store the raw data in a local cache;

[0060] Apply digital filters to remove high-frequency noise and smooth signal curves, use advanced noise reduction algorithms to further reduce random noise and improve data quality, convert data from different sensors into a format suitable for subsequent processing, and ensure that the timestamps of all data are synchronized.

[0061] S2, analyzing the data through image processing and pattern recognition algorithms to obtain the surrounding environment data and the location, shape and characteristics of the explosive, including the following steps,

[0062] Load the preprocessed surrounding environment data, initialize the image processing library and tools, apply Canny edge detection and Sobel operator to highlight the edges in the image, use the contour finding algorithm to extract the outline of the object, preliminarily separate the foreground and background, and divide the image into foreground and background based on grayscale or color thresholds. Starting from the seed point, gradually merge similar pixels to form connected areas, and separate different areas by simulating the water flow process. It is particularly suitable for object segmentation under complex backgrounds;

[0063] Apply pattern recognition algorithms to calculate the center position of objects in the image through the coordinate system, use Hough transform to detect lines and circles, describe the shape of objects, measure the length, width and height of objects, calculate their area and perimeter, obtain surface characteristics through color histogram and texture analysis, identify and mark non-target areas, provide contextual information, use PCA and geometric methods to estimate the direction and angle of objects, and integrate all extracted information into a structured data format;

[0064] Use depth sensors and stereo vision technology to generate point cloud data to represent the three-dimensional point set in the environment. Apply Delaunay triangulation and Poisson reconstruction algorithms to convert point clouds into continuous three-dimensional grids. Combine RGB images, depth maps, and perception data to generate a complete three-dimensional model. Use SLAM technology to build a three-dimensional map containing the environment and target objects, expressed as,

[0065]

[0066] Where M is the generated three-dimensional model, V is the integral region, which is used to represent the volume of the entire environment space, p is any point in the environment, which is used to describe every possible position in the environment, D(p) is the distance function, which is used to represent the distance from point p to the nearest known environmental feature point, and α is the attenuation coefficient, which is used to control the influence of distance on density. It is a feature extraction function, which is used to extract the features of each point in the environment. (p) is the shape matching function used to evaluate the similarity between point p and the shapes in the known shape library. N is the number of known feature points in the environment, which is used to represent the number of key points detected by the sensor. i is the index of the feature point, which is used to process each feature point in the summation operation. w i is the importance weight of the i-th feature point, which is used to dynamically adjust according to the type of feature point. β is the 3D model enhancement coefficient, which is used to adjust the influence of the feature point on the overall model. It is a complex information filtering function used to process data from multi-source sensors.

[0067] S3, based on the surrounding environment data and the location, shape and characteristics of the explosive, a safe path from the five-fingered dexterous hand to the location of the explosive is planned using a machine learning algorithm. After the planning is completed, the five-fingered dexterous hand is moved to the vicinity of the explosive according to the planned safe path, including the following steps:

[0068] Collect surrounding environment data, including but not limited to three-dimensional maps, obstacle locations, terrain features, collect explosive samples of different types, shapes, sizes and materials, record their locations, shapes, sizes and surface features, and record the robot's operating data in different scenarios, including but not limited to path planning, action sequences, and force feedback information;

[0069] Select a machine learning algorithm based on task requirements and design the model architecture, including the design of the input layer, hidden layer, and output layer, as well as the connection method between the layers. For the reinforcement learning model, define the state space, action space, and reward function, initialize the model parameters, set the initial weights and biases, divide the data set into training set, validation set, and test set, usually in a ratio of 70%, 15%, and 15%, select the loss function, select the optimizer, use batch gradient descent and mini-batch gradient descent for training, gradually adjust the model parameters to minimize the loss function, and continuously optimize the model performance through multiple iterations until the predetermined convergence conditions and maximum number of iterations are reached;

[0070] Evaluate model performance on the validation set, use grid search, random search, and Bayesian optimization methods to tune hyperparameters, and use k-fold cross validation to further verify the stability and generalization ability of the model;

[0071] Load the pre-trained machine learning model, check the model parameters to ensure that it is suitable for the current task environment and target object characteristics, integrate the machine learning model into the robot control panel, and ensure that the model can receive sensor data and output path instructions;

[0072] Loading the generated 3D model and map of the surrounding environment and target object into the machine learning model, inputting data collected from the environment, including but not limited to obstacle locations, terrain features, inputting the specific location, shape, size and surface features of the explosive, and inputting the current state of the robot so that the model takes into account physical limitations and energy optimization;

[0073] Start the path planning module and prepare to simulate different approach paths. Use the machine learning model to simulate multiple possible approach paths, evaluate the safety and feasibility of each path, optimize the path to minimize energy consumption based on the current power status, consider safety factors, avoid obstacles and other potential risk points, and select the optimal path through iterative optimization to ensure that the path is both safe and can reach the target location efficiently.

[0074] Output the finalized safe path, including the starting position, intermediate nodes, target position, obstacle avoidance strategy, and emergency response. Simulate the generated safe path in a virtual environment to observe whether the whole process is smooth and risk-free. If any potential problems and unreasonable paths are found, return to the previous step for adjustment and optimization.

[0075] Load the finalized safe path into the robot’s embedded control panel, confirm that all path instructions are correct, prepare for actual execution, and set up monitoring mechanisms to ensure that the path can be adjusted in real time during execution;

[0076] Start the action execution program of the five-finger dexterous hand, move step by step according to the planned safe path, monitor the execution of each action in real time, make fine adjustments according to the actual situation to ensure the safety and accuracy of the operation, continuously monitor the applied force through high-precision force feedback to ensure that no unnecessary pressure is caused to the environment or itself, and keep synchronization with other sensors during the movement to ensure the accuracy of the path;

[0077] When the five-fingered dexterous hand successfully moves to the predetermined position near the explosive, it stops moving and confirms that the position is accurate, and performs a final environmental perception and verification to ensure that the dexterous hand is at the optimal operating distance and posture.

[0078] S4, after moving the five-fingered dexterous hand to the vicinity of the explosive location, a machine learning algorithm is used to generate an action sequence, including the following steps:

[0079] Start the action execution program of the five-finger dexterous hand, move step by step according to the pre-planned safe path, monitor the execution of each action in real time, make fine adjustments according to the actual situation to ensure the safety and accuracy of the operation, and continuously monitor the applied force through the high-precision force feedback system to ensure that no unnecessary pressure is caused to the environment or itself. When the five-finger dexterous hand successfully moves to the predetermined position near the explosive, stop moving and confirm that the position is accurate;

[0080] Start model loading, prepare to load a pre-trained machine learning model, load the model file from the storage medium, including the model architecture and weight parameters, verify the integrity of the model file to ensure that no key parts are damaged or missing, check the model parameters to ensure that they are suitable for the current task environment and target object characteristics, and adjust the model parameters to adapt to the new environment or task requirements if necessary;

[0081] Activate all relevant sensors to re-collect data on the current environment, including but not limited to the latest three-dimensional map, obstacle locations, terrain features, and the specific location, shape, size, and surface features of the explosives, pre-process the collected data, and input the pre-processed environmental data and explosive feature information into the machine learning model, and input the current state of the robot so that the model can take into account physical limitations and energy optimization;

[0082] Start motion planning and prepare to simulate different grasping and manipulation actions. Use machine learning models to simulate multiple possible grasping actions, evaluate the safety and feasibility of each action, and simulate subsequent manipulation actions to ensure that each step meets safety standards and energy optimization requirements.

[0083] According to the current power state, the action sequence is optimized to minimize energy consumption, taking into account safety factors to avoid additional pressure on explosives and other risks. Through iterative optimization, the optimal action sequence is selected to ensure that the action is both safe and can complete the task efficiently. The final action sequence is output, including approach path, grasping action, manipulation action, safety checkpoint and emergency response strategy, which is expressed as,

[0084]

[0085] Among them, A is the generated action sequence, T is the total time period, which is used to represent the time range of the entire action sequence, and λ is the time decay coefficient, which is used to control the influence of different time periods on the final action sequence. It is the machine learning model output function, which is used to represent the action suggestions or feature extraction results output by the machine learning model based on the current environment and target information. is a safety assessment function, which is used to represent the safety score based on the current environment data and operation risk assessment. K is the number of known key points in the environment, which is used to enhance the safety and accuracy of the action sequence. k is the index of the key point, which is used to process each key point in the summation operation. γ is the action sequence enhancement coefficient, which is used to adjust the influence of the key point on the overall action sequence. is an information filtering function used to process data from multi-source sensors, a k is the action parameter of the kth key point, which is used to represent the role and influencing factors of the key point in the action sequence;

[0086] S5. According to the generated action sequence, high-precision force feedback is used to grab the explosives. After the grabbing is completed, the bomb disposal action is performed, including the following steps:

[0087] Start the robot's embedded control panel, prepare to load the generated action sequence, and load the pre-generated action sequence file from the control panel, including but not limited to timestamps and action parameters;

[0088] Start the high-precision force sensor and other related sensors to ensure that they are working properly, calibrate the force sensor to ensure that its readings are accurate;

[0089] According to the first action instruction in the action sequence, the movement operation of the five-finger dexterous hand is started, and the position deviation of the dexterous hand is continuously monitored using a visual sensor. The position error is calculated in real time through an image processing algorithm. According to the position deviation, the path of the dexterous hand is fine-tuned to ensure that it is accurately positioned at the target position.

[0090] Turn on high-precision force feedback to monitor the contact force of the dexterous hand in real time during the approach process. According to the force feedback data, dynamically adjust the posture and strength of the dexterous hand to avoid unnecessary pressure on the environment or itself.

[0091] According to the shape and surface features of the object, adjust the finger opening and closing angle and pressure to ensure the best grasping effect. Before the formal grasping, make the final posture adjustment to ensure that the dexterous hand is in the best grasping position.

[0092] According to the grasping instructions in the action sequence, high-precision force feedback is used to accurately control the strength of each finger for grasping, and the force feedback data is used to confirm whether the grasping is successful to ensure that the object is firmly grasped;

[0093] After the grasping is completed, the posture of the five-finger dexterous hand is fine-tuned using high-precision force feedback to ensure that it is suitable for the subsequent bomb disposal action, and the angle and strength of the dexterous hand are adjusted to prepare for the next bomb disposal action;

[0094] According to the predetermined path, the seized explosives are smoothly transferred to a safe area. In the safe area, harmless treatment measures are implemented, such as dismantling the device and other necessary operations. After the bomb disposal action is completed, a comprehensive inspection is carried out to ensure that all operations have been completed safely.

[0095] S6. After executing the bomb disposal action, the status of the smart material is monitored in real time through the sensor, and a real-time video stream is provided to the operator, including the following steps:

[0096] After the bomb disposal action is completed, the smart material status sensors installed on the five-finger dexterous hand and the robot are activated to continuously collect the status data of the smart material, including but not limited to stress, strain, and temperature changes. Timestamps are added to each collected data to ensure the time series integrity and traceability of the data;

[0097] Preprocess the collected data, extract key features, including but not limited to maximum stress and average temperature change rate, simplify the data analysis process, and obtain smart material status data;

[0098] Loading a pre-trained machine learning model Input the pre-processed smart material state data into the machine learning model;

[0099] Use models to analyze data, identify abnormal patterns and potential problems, including but not limited to excessive stress and abnormal temperature changes, and output analysis results, including normal state assessment and abnormal detection reports;

[0100] When an abnormal situation is detected, the alarm mechanism is triggered immediately, the operator is notified through the sound and light alarm, and the alarm information is sent to the operator's monitoring terminal to ensure that the operator receives the notification in time and automatically executes the preset protective measures, including but not limited to stopping the current operation, returning to a safe position, recording all abnormal events and their handling process, and generating detailed log files;

[0101] Activate the cameras and visual sensors installed on the robot, prepare to capture real-time images of the operation site, continuously capture high-definition images of the operation site to ensure coverage of the entire operation area, use efficient video compression technology to reduce bandwidth usage, ensure smooth transmission of video streams, and transmit the compressed video streams to the operator's monitoring terminal in real time, allowing the operator to monitor progress in real time and intervene when necessary.

[0102] S7. After completing the task, return the robot to the safe zone, conduct a comprehensive inspection of the robot, record any abnormalities, and perform maintenance, including the following steps:

[0103] After completing the task, start the return program in the embedded control panel, prepare to return to the safe zone, start the navigation panel, prepare to plan the path, and load the latest three-dimensional map and environmental data, including but not limited to the location of obstacles and terrain characteristics;

[0104] Activate all relevant sensors, obtain the latest sensor information, use the latest environmental data and sensor information to recalculate a safe path back to the safe zone, combine energy-aware adaptive motion planning, optimize the path to minimize energy consumption and ensure safety, and convert the recalculated safe path into specific path planning instructions, including but not limited to speed and steering angle;

[0105] Load the path planning instructions into the robot's motion control panel, confirm that all path instructions are correct, prepare to perform actual operations, start the robot to move step by step to the designated safety zone along the planned safety path, monitor the execution of each action in real time, make fine adjustments based on actual conditions to ensure the safety and accuracy of the operation, and when the robot successfully moves to the designated safety zone, stop moving and confirm that the position is accurate;

[0106] Start all self-check functions, including but not limited to hardware diagnosis, software detection, sensor calibration, run comprehensive inspection procedures, evaluate the status of the Robot, including but not limited to mechanical components, electronic systems, sensors, generate detailed inspection reports, record all inspection results, including normal status and potential problems;

[0107] Export the inspection report to a standard format to ensure that it is easy to read and analyze. Submit the inspection report to relevant technicians and management departments to ensure that they can understand the status of the robot in a timely manner. Technicians and management departments jointly analyze the inspection report to identify the parts that need maintenance. According to the inspection results, formulate a specific maintenance plan, including but not limited to replacement of parts, software updates, and sensor calibration;

[0108] Perform all necessary maintenance work according to the maintenance plan to ensure that the robot is in optimal condition. After completing the maintenance, run the self-test program again to confirm that all problems have been resolved;

[0109] All inspection records, abnormal reports and maintenance logs are organized into complete task files, which are stored in the robot's local storage medium to ensure data security, and task files are synchronously uploaded to the storage medium and cloud server for remote access and backup.

[0110] This embodiment also provides a computer device, which is suitable for the control method of the five-finger dexterous hand of an intelligent bomb disposal robot, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the control method of the five-finger dexterous hand of an intelligent bomb disposal robot as proposed in the above embodiment.

[0111] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0112] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the control method of the five-finger dexterous hand of an intelligent bomb disposal robot proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0113] In summary, the present invention achieves precise control of the movements of each finger by performing precise calibration steps on the five-fingered dexterous hand, collects data of the surrounding environment by utilizing a highly sensitive sensor network and pre-processes the data to achieve rapid and accurate determination of the position, shape and characteristics of the explosive, plans a safe path from the current position to the explosive by using a machine learning algorithm based on the environmental and target information obtained previously, and achieves efficient approach to the target, generates a series of precise action instructions by calling the machine learning algorithm to guide the dexterous hand to complete the capture of the explosive, and achieves fine-tuning of the grasping force to avoid danger caused by excessive force.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling the five-finger dexterous hand of an intelligent bomb disposal robot, characterized in that: include, The five-finger dexterous hand is calibrated, and the surrounding environment data is collected and pre-processed using sensors. The data is analyzed through image processing and pattern recognition algorithms to obtain the surrounding environment data and the location, shape and characteristics of the explosives. Based on the surrounding environment data and the location, shape and characteristics of the explosive, a machine learning algorithm is used to plan a safe path from the five-fingered dexterous hand to the location of the explosive. After the planning is completed, the five-fingered dexterous hand is moved to the vicinity of the explosive along the planned safe path. After the five-fingered dexterous hand is moved near the location of the explosive, a machine learning algorithm is used to generate an action sequence. Based on the generated action sequence, high-precision force feedback is used to grab the explosive. After the grab is completed, the bomb disposal action is performed; After performing the bomb disposal action, the status of the smart material is monitored in real time through sensors, providing a real-time video stream to the operator. After completing the task, the robot is returned to the safe area, a comprehensive inspection of the robot is carried out, any abnormalities are recorded, and maintenance is carried out.

2. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 1, characterized in that: The five-finger dexterous hand is calibrated, and the surrounding environment data is collected by using sensors and pre-processed. The following steps are included: Start the sensor array, communication device and controller, run the hardware and software self-test procedures, perform physical and electrical calibration of the five-finger dexterous hand, and calibrate the force sensor and other sensors; Activate the sensor installed on the robot. The activated sensor starts to collect data of the surrounding environment to obtain the surrounding environment data; The collected surrounding environment data is filtered, denoised and format converted to obtain pre-processed surrounding environment data.

3. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 2, characterized in that: The data is analyzed by image processing and pattern recognition algorithms to obtain the surrounding environment data and the location, shape and characteristics of the explosives. The following steps are included: Use image processing algorithms to extract location information, shape information, size information, material information, background information and posture information from the preprocessed surrounding environment data, and apply pattern recognition algorithms to analyze the preprocessed surrounding environment data to determine the location, shape and characteristics of the environmental data and explosives; Generate a 3D model and map of the surrounding environment and target objects based on the environmental data and the location, shape and characteristics of the explosives, expressed as, Where M is the generated 3D model, V is the integration area, p is any point in the environment, D(p) is the distance function, α is the attenuation coefficient, is the feature extraction function, is the shape matching function, N is the number of known feature points in the environment, i is the index of the feature point, and w i is the importance weight of the i-th feature point, β is the three-dimensional model enhancement coefficient, It is a complex information filtering function.

4. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 3 is characterized in that: The method uses a machine learning algorithm to plan a safe path from the five-fingered dexterous hand to the explosive location based on the surrounding environment data and the location, shape and characteristics of the explosive. After the planning is completed, the five-fingered dexterous hand is moved to the vicinity of the explosive according to the planned safe path, including the following steps: According to the generated 3D model and map of the surrounding environment and target object, the trained machine learning model is loaded, the surrounding environment data and explosive feature information are input into the machine learning model, and the current state of the robot is input to obtain the constructed machine learning model; Use machine learning models to simulate different approach paths, combined with energy-aware adaptive motion planning, consider the current power state, optimize the path to minimize energy consumption, consider safety factors, and plan a safe path from the five-fingered dexterous hand to the location of the explosive through iterative optimization; Start the action execution program of the five-finger dexterous hand, move step by step according to the planned safe path, monitor the execution of each action in real time, and continuously monitor the applied force through high-precision force feedback.

5. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 4, characterized in that: After the five-fingered dexterous hand is moved to the vicinity of the explosive location, a machine learning algorithm is used to generate an action sequence, including the following steps: After moving the five-fingered dexterous hand to the designated position according to the planned path, the machine learning model is loaded and the model parameters are verified; The collected environmental data and explosive feature information are input into the machine learning model, and the machine learning model is used to simulate different grasping and manipulation actions to generate action sequences, which are expressed as, Where A is the generated action sequence, T is the total time period, λ is the time decay coefficient, is the machine learning model output function, is the security assessment function, K is the number of known key points in the environment, k is the index of the key point, γ is the action sequence enhancement coefficient, is the information filtering function, a k is the action parameter of the kth key point; The finalized motion sequence is loaded into the robot’s embedded control panel.

6. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 5, characterized in that: The method comprises the following steps: Load the generated action sequence from the control panel, activate the high-precision force sensor, and use the vision sensor to continuously monitor the position deviation according to the first action of the action sequence, fine-tune the path to ensure accurate positioning, and monitor the contact force in real time during the approach process through high-precision force feedback; Use force feedback data to fine-tune the posture and strength of the dexterous hand, dynamically adjust the opening and closing angle and pressure of the fingers according to the shape and surface characteristics of the object, and use high-precision force feedback to accurately control the strength of each finger for grasping according to the grasping instructions in the action sequence; After completing the grasping, high-precision force feedback is used to fine-tune the posture of the five-fingered dexterous hand to better implement the bomb disposal action, transfer the explosives to a safe area, implement harmless treatment and dismantle the device.

7. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 6, characterized in that: After the bomb disposal action is performed, the state of the smart material is monitored in real time by the sensor, and a real-time video stream is provided to the operator, including the following steps: After executing the bomb disposal action, the data of the smart material status is continuously collected through the sensors installed on the five-finger dexterous hand and the robot to obtain the smart material status data, and the smart material status data is preprocessed to obtain the preprocessed smart material status data; Use machine learning models to analyze pre-processed smart material status data to identify abnormal patterns and potential problems; When an abnormal situation is detected, the alarm mechanism is triggered immediately to notify the operator and take protective measures, and all abnormal events and their handling process are recorded; Activate cameras and visual sensors to capture real-time images of the operation site, use compression technology to reduce bandwidth usage, and transmit video streams to the operator's monitoring terminal, allowing the operator to monitor progress in real time and intervene when necessary.

8. The control method of the five-finger dexterous hand of the intelligent bomb disposal robot according to claim 7, characterized in that: After completing the task, the robot is returned to the safe area, a comprehensive inspection is performed on the robot, any abnormalities are recorded, and maintenance is performed, including the following steps: When the task is completed, the robot's return program is started, the navigation panel is activated, and the collected environmental data and the latest sensor information are used to recalculate a safe path back to the safe zone, generate path planning instructions, and send them to the motion control panel; According to the planned safety path, control the robot to move to the designated safety zone, start the comprehensive inspection program, activate all self-inspection functions, generate a detailed inspection report, submit it to the relevant technicians and management departments, and formulate a specific maintenance plan based on the inspection results; After completing all maintenance work, all inspection records, abnormal reports and maintenance logs will be archived to create a complete task file, which will be stored in the storage medium and cloud server.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for controlling the five-finger dexterous hand of an intelligent bomb disposal robot according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling the five-finger dexterous hand of an intelligent bomb disposal robot according to any one of claims 1 to 7 are implemented.

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