Modular disassembly robot supporting working condition adaptive configuration

Abstract

The application relates to the technical field of industrial robots, and provides a modular disassembly robot supporting working condition adaptive configuration, which comprises a built-in power source, a driving chassis moving power formed by direct current, power output for supplying an external module interface, a quick interface external dust removal module forming negative pressure dust collection and dust residue collection functions, and an immediate use connection of air paths and circuits between the external module interface and the chassis; a motion execution mechanism obtains large torque, variable speed rotation or linear output conforming to terrain requirements, and forms composite motion of chassis advancing, steering and posture adjustment; a controller module is connected with the built-in power source, the quick interface external dust removal module and the motion execution mechanism, and forms coordinated control functions of built-in power source power scheduling, motion mechanism cooperative action and external module start-stop interlocking. The application reduces system internal consumption and response delay, and optimizes energy distribution and equipment utilization.

Description

Technical Field

[0001] This invention relates to the field of industrial robot technology, and in particular to a modular disassembly robot that supports adaptive configuration under working conditions. Background Technology

[0002] With the deepening of industrial automation and intelligentization, the demand for dismantling operations in fields such as waste equipment recycling, hazardous environment operations, and special engineering processing is increasing. Traditional dismantling operations mostly rely on manual operation or fixed mechanical equipment, resulting in prominent problems such as low efficiency, poor safety, and insufficient adaptability. Especially when facing complex and changing working environments, such as dismantling objects with different materials, structures, and spatial conditions, existing technologies often struggle to achieve efficient, accurate, and safe operations. Modular robot technology has made significant progress in recent years, demonstrating good environmental adaptability and task flexibility through reconfigurable mechanical units, sensing systems, and control architectures. However, in the specific application scenario of dismantling, robots not only need modular reconfiguration capabilities but also need to be able to dynamically adjust their hardware configuration and control strategies according to real-time working conditions—including the physical characteristics of the object, environmental constraints, and task objectives. Currently, modular dismantling robot systems that can truly achieve working condition self-adaptation are still in the technological exploration stage, and mature, scalable solutions have not yet been formed.

[0003] Prior art 1, publication number CN120839757A, discloses a modular robot and its control system. The technical solution includes: the modular robot has four functional modules: a power module, a perception module, an execution module, and an energy module. The control system of the modular robot includes a task orchestrator, an adaptive feedback loop module, and an open communication interface, which respectively realize task-level structural scheduling, state adaptive control, and real-time information exchange between modules and between robots. Although it possesses multimodal mobility, supports task-level adaptive orchestration, has feedback learning capabilities, and can achieve module-level intelligent collaboration under an open communication architecture, the adaptive feedback loop mainly focuses on task logic and module collaboration states, without addressing real-time perception and direct response to core physical conditions at the work site, such as mechanical load and environmental dust.

[0004] Prior art 2, publication number CN121156997A, discloses a modular robot, including a humanoid upper module, which includes an upper control system, a head mechanism, an upper limb mechanism, a waist mechanism, an upper arm mechanism, a forearm mechanism, and a hand mechanism. The head mechanism is located on the upper limb mechanism. There are two upper arm mechanisms, which are located on both sides of the upper limb mechanism. The lower end of each upper arm mechanism is provided with a forearm mechanism, and the lower end of the forearm mechanism is connected to the hand mechanism. The waist mechanism is located on the lower part of the upper limb mechanism. The upper arm mechanism can move in the XYZ direction relative to the upper limb mechanism. The forearm mechanism can move in the XZ direction relative to the upper arm mechanism. The hand mechanism can move in the XZ direction relative to the forearm mechanism. The upper part of the waist mechanism can rotate axially relative to the upper limb mechanism, and the middle or lower part of the waist mechanism can rotate back and forth. The waist mechanism is provided with a first connecting part that connects to the lower module. Although modular robots are modular robot-shaped upper modules that can be combined with different equipment to form new robots, they lack the ability to automatically identify and dynamically configure external functional modules, especially non-mechanical operation modules with different performance parameters, such as dust removal equipment. After the modules are connected, their working modes are usually fixed or require manual setting.

[0005] Existing technology three, publication number CN119897859A, discloses a gait planning method for modular robot truss crawling, aiming to solve the problem of efficient and stable movement of modular robots in complex truss structure environments. It analyzes and designs based on the structure of a modular unit, models the modular robot, studies the truss structure, designs a method to process the truss map, studies semi-circular and linear motion trajectories, and designs the motion trajectory of the modular robot's end effector. It uses analytical methods to solve for the angle values ​​of each joint of the modular robot, and designs four gaits: opposite-side crossing gait, adjacent-side lateral climbing gait, rotation around a member gait, and inchworm gait along a member gait, along with selection strategies for smooth gait planning of the modular robot's truss structure crawling. Although it adapts to the non-fixed structure and different truss structures of modular robots, can quickly obtain accurate joint angle values, and improves the stability of modular robot truss crawling, it is suitable for scenarios such as aerospace and building maintenance. However, it does not consider the real-time load changes generated during operation and the impact of operation on the environment, such as dust generation; that is, the motion planning and the dynamic working conditions caused by the operation process are decoupled.

[0006] Current technologies 1, 2, and 3 lack multi-dimensional integrated perception of the core physical working conditions, including load and environmental quality; they cannot dynamically adjust the core power output according to real-time working conditions; they lack the ability to automatically identify external auxiliary function modules and the ability to adaptively and collaboratively configure based on working conditions and module capabilities; resulting in difficulties in maintaining optimal work efficiency, energy efficiency ratio, and environmental adaptability under dynamic changes. Therefore, this invention provides a modular disassembly robot that supports adaptive configuration based on working conditions. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a modular disassembly robot that supports adaptive configuration based on working conditions, comprising: The built-in power source is used to obtain DC power with controlled voltage and current through the status monitoring and power distribution processing of the battery management system. The DC power is used to drive the chassis movement and supply power to the external module interface. The quick-connect interface connects to an external dust removal module, which is used to obtain a clean airflow after multi-stage filtration and centrifugal separation within the module, forming a negative pressure dust suction and dust collection function. It also allows for plug-and-play connection of the air and electrical circuits to the chassis via the external module interface. The motion actuator is used to enhance and convert torque through the reducer and transmission mechanism to obtain a large torque, variable speed rotation or linear output that meets the terrain requirements, forming a compound motion of chassis travel, steering and attitude adjustment. The controller module connects to the built-in power source, the external dust removal module via a quick interface, and the motion actuators. It receives signals and commands from external operating terminals and sensors. Through centralized processing of command parsing, path planning, and multi-axis kinematics calculation, it obtains precise control commands for each actuator and interface, forming a coordinated control function for power scheduling of the built-in power source, coordinated action of motion mechanisms, and start / stop interlocking of external modules.

[0008] Optionally, the controller module also includes: The working condition perception and power control system is used to collect raw data on the working load torque and the concentration of dust in the space in real time, and integrate and calculate them into real-time working condition characteristic parameters to generate the first-level adaptive power output command. The intelligent identification and dust removal configuration system is used to receive real-time operating condition characteristic parameters and automatically obtain the rated power identifier of the external dust removal module, thereby generating the second-stage dust collector operation configuration command. An integrated adaptation and motion execution system is used to simultaneously receive two sets of instructions and perform integrated calculations to drive the robot to execute a third-level adaptive motion trajectory and operating speed that matches the current power and dust removal status.

[0009] Optionally, the operating condition sensing and power control system includes: The operating condition fusion processing subsystem is used to standardize the raw data into dimensionless standardized load coefficients and standardized dust coefficients; The coefficient pairing processing subsystem is used to pair synchronized standardized coefficients to form a two-dimensional joint operating condition vector; The coupled mapping computation subsystem is used to compute the two-dimensional joint operating condition vector and output scalar values ​​as real-time operating condition characteristic parameters.

[0010] Optionally, the intelligent identification and dust removal configuration system includes: The content mapping subsystem is used to map real-time operating condition characteristic parameters to dust removal demand baseline values, and to map rated power identifiers to dust collector rated air volume and rated negative pressure values. The instruction matching subsystem is used to compare the baseline value with the rated value to obtain the demand ratio coefficient, and based on this, query the matching rule set to obtain the instruction values ​​of fan speed and valve opening. The instruction encapsulation subsystem is used to encapsulate instruction values ​​into operation configuration instructions for the second-stage dust collector.

[0011] Optionally, the integrated adaptation and motion execution system includes: The feature vector integration subsystem is used to parse the power output level parameters and dust collector operating status parameters from the first-level and second-level instructions, combine them into an integrated feature vector, and map them into a cooperative mode code. The operation parameter calculation subsystem is used to calculate the key point sequence of the motion trajectory and the baseline value of the operation speed according to the cooperative mode encoding. The packaging and conversion subsystem is used to convert key point sequences and velocity reference values ​​into third-level motion control commands for driving actuators.

[0012] Optionally, the runtime parameter calculation subsystem includes: The parameter output component is used to calculate and output the trajectory curvature parameter and trajectory point spacing parameter based on the power output level parameter; The iterative rotation component is used to iteratively rotate the direction angle based on the starting point coordinates and direction angle of the operation, combined with the trajectory curvature parameter, and calculate the displacement based on the trajectory point spacing parameter, and cyclically output the key point sequence; The superimposed fixed components are used to calculate the speed adjustment factor based on the dust collector's operating status parameters, and then output the operating speed benchmark value.

[0013] Optionally, the iterative rotation component includes: The initialization and incremental preparation subcomponent is used to set the initial coordinates and orientation angle and convert the trajectory curvature parameters into orientation angle increment values. A single-iteration calculation sub-component is used to calculate the new orientation angle and decompose it to obtain the X-axis and Y-axis displacement increments; The coordinate update and sequence construction sub-component is used to update coordinates and construct a keypoint sequence, which is then output after iterating to a preset number.

[0014] Optionally, the single-iteration calculation subcomponent includes: The orientation correction coefficient calculation module is used to calculate the orientation angle deviation and query the dataset to output the orientation correction coefficient; The axial weight generation module is used to calculate the weight values ​​of the X-axis and Y-axis based on the direction correction coefficient and the trajectory point spacing parameter. The X-axis displacement increment calculation module is used to calculate the X-axis displacement increment and compare it with the minimum displacement threshold to take the larger one. The Y-axis displacement increment calculation module is used to calculate the Y-axis displacement increment and compare it with the minimum displacement threshold to take the larger one.

[0015] Optionally, the axial weight generation module includes: The evolution factor generation submodule is used to multiply the scaling factor and weight value and add the historical trend compensation value to output the X-axis and Y-axis evolution factors. The intermediate weight value generation submodule is used to compare the difference between two evolution factors. If the difference is greater than the threshold, the rebalancing procedure is started to adjust and the initial weight is output. The final weight generation submodule is used to normalize the initial weights and output the final X-axis and Y-axis weight values.

[0016] Optionally, the evolution factor generation submodule includes: The weight sequence acquisition unit is used to extract historical weight sequences and calculate weight changes; The conformal direction recognition unit is used to identify the trend direction of the weight change and output the trend indicator and average change magnitude. The trend indicator output unit is used to obtain the original trend strength value by processing the average change amplitude of the trend indicator, and then multiply it by the scaling factor to output the X-axis trend compensation value for the current period.

[0017] This invention automatically acquires the rated power of the external dust removal module through a power level identification unit and generates dust removal configuration instructions based on real-time operating condition characteristic parameters. This enables the system to automatically identify the capacity of the connected dust removal equipment and dynamically adjust its operating mode according to the current dust concentration. It eliminates the manual configuration step, ensuring that the workload of the dust removal module always matches the level of environmental pollution, guaranteeing dust removal effectiveness while avoiding excessive energy consumption. An integrated calculation module synchronously processes power output instructions and dust removal configuration instructions, mapping the power state and dust removal state together into control parameters for motion trajectory and operating speed. This allows the robot's motion planning to go beyond a single factor like path or load, integrating energy allocation and environmental maintenance needs, thereby achieving a balance between motion stability, operating efficiency, and environmental adaptability under complex operating conditions. The three systems form a serial information and control flow through data channels and a system bus, creating a real-time response closed loop for condition perception, resource allocation, and motion execution. It can automatically adjust the entire process from data acquisition and instruction generation to action execution based on continuously changing mechanical and environmental conditions during operation, thereby improving the overall adaptability, robustness, and automation of dismantling operations.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a block diagram of the modular disassembly robot that supports adaptive configuration based on working conditions in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the modular disassembly robot supporting adaptive working condition configuration in Embodiment 1 of the present invention; Figure 3 This is a block diagram of the working condition sensing and power control system in Embodiment 2 of the present invention; Figure 4 This is a block diagram of the intelligent identification and dust removal configuration system in Embodiment 5 of the present invention; Figure 5 This is a block diagram of the integrated adaptation and motion execution system in Embodiment 7 of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0023] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0024] Example 1: As Figure 1 As shown, this embodiment of the invention provides a modular disassembly robot that supports adaptive configuration based on working conditions, comprising: The built-in power source is used to obtain DC power with controlled voltage and current through the status monitoring and power distribution processing of the battery management system. The DC power is used to drive the chassis movement and supply power to the external module interface. The quick-connect interface connects to an external dust removal module, which is used to obtain a clean airflow after multi-stage filtration and centrifugal separation within the module, forming a negative pressure dust suction and dust collection function. It also allows for plug-and-play connection of the air and electrical circuits to the chassis via the external module interface. The motion actuator is used to enhance and convert torque through the reducer and transmission mechanism to obtain a large torque, variable speed rotation or linear output that meets the terrain requirements, forming a compound motion of chassis travel, steering and attitude adjustment. The controller module connects to the built-in power source, the external dust removal module via a quick interface, and the motion actuators. It receives signals and commands from external operating terminals and sensors. Through centralized processing of command parsing, path planning, and multi-axis kinematics calculation, it obtains precise control commands for each actuator and interface, forming a coordinated control function for power scheduling of the built-in power source, coordinated action of motion mechanisms, and start / stop interlocking of external modules.

[0025] The working condition perception and power control system is used to collect raw data of working load torque and space dust concentration in real time; through working condition fusion processing, the raw data is fused and calculated into real-time working condition characteristic parameters, and the first-level adaptive power output command is dynamically generated based on the real-time working condition characteristic parameters. The intelligent identification and dust removal configuration system is used to receive real-time operating condition characteristic parameters through a two-way data channel, and at the same time automatically obtain the rated power identifier of the external dust removal module through power level identification; based on the obtained real-time operating condition characteristic parameters and rated power identifier, it generates matching second-stage dust collector operation configuration instructions. An integrated adaptation and motion execution system is used to synchronously receive the first-level power output command and the second-level dust collector operation configuration command via the system bus; the two sets of commands are integrated and calculated, and the built-in power source is activated to drive the robot's mobile chassis and motion execution mechanism to execute the third-level adaptive motion trajectory and operating speed that matches the current power state and dust removal state.

[0026] The working principle and beneficial effects of the above technical solution are as follows: (Refer to Appendix for details.) Figure 2 This embodiment synchronously collects load torque and dust concentration data and integrates them into unified operating condition characteristic parameters, enabling the system to simultaneously sense changes in mechanical load and environmental quality. Based on these parameters, a power output command is generated, so that the robot's power output is no longer a fixed or preset curve, but can be adjusted in real time according to the working resistance and dust pollution level, providing sufficient power under heavy load or high pollution conditions, and avoiding energy waste under light load or low pollution conditions. This embodiment automatically acquires the rated power of the external dust removal module through a power level identification unit and generates dust removal configuration commands based on real-time operating condition characteristic parameters. This enables the system to automatically identify the capacity of the connected dust removal equipment and dynamically adjust its operating mode according to the current dust concentration. It eliminates the manual configuration step, ensuring that the workload of the dust removal module always matches the level of environmental pollution, guaranteeing dust removal effectiveness while avoiding excessive energy consumption. An integrated calculation module synchronously processes power output commands and dust removal configuration commands, mapping the power state and dust removal state together into control parameters for motion trajectory and operating speed. This allows the robot's motion planning to go beyond a single factor like path or load, integrating energy allocation and environmental maintenance needs, thereby achieving a balance between motion stability, operating efficiency, and environmental adaptability under complex operating conditions. The three systems form a serial information and control flow through data channels and a system bus, creating a real-time response closed loop for condition perception, resource allocation, and motion execution. It can automatically adjust the entire process from data acquisition and command generation to action execution based on continuously changing mechanical and environmental conditions during operation, thereby improving the overall adaptability, robustness, and automation of the dismantling operation.

[0027] In summary, this embodiment enables the power system, dust removal system, and motion system to no longer operate as independent units, but to achieve deep collaboration through a unified data interface and control logic. The collaboration mechanism reduces internal system friction and response delay, optimizes energy distribution and equipment utilization while ensuring operational performance, and ultimately improves the overall energy efficiency and task completion quality of the system.

[0028] Example 2: Figure 3 As shown, based on Embodiment 1, the working condition sensing and power control system provided in this embodiment of the invention includes: The working condition fusion processing subsystem is used to convert the raw data of the collected working load torque and the raw data of the spatial dust concentration according to the preset motor rated torque value and dust concentration safety threshold, and output dimensionless standardized load coefficient and standardized dust coefficient. The coefficient pairing processing system is used to pair the standardized load coefficient and the standardized dust coefficient under the synchronous timestamp with the work cycle as the period, forming a two-dimensional joint working condition vector with the standardized load coefficient and the standardized dust coefficient as components. The coupling mapping calculation subsystem is used to perform working condition coupling degree mapping calculation on the two components in the two-dimensional joint working condition vector, and outputs a scalar value that represents the comprehensive state of the current working intensity and environmental dust load. The scalar value is used as a real-time working condition characteristic parameter for the generation of the first-level adaptive power output command.

[0029] The working principle and beneficial effects of the above technical solution are as follows: This embodiment realizes comprehensive monitoring of work load and environmental dust concentration, and adaptive control of power output. The working condition fusion processing subsystem transforms the raw data of work load torque and spatial dust concentration into dimensionless standardized coefficients, eliminating the interference of different physical dimensions on data comparability and establishing a benchmark for unified processing of multi-source working condition data. The coefficient pairing processing subsystem, with the work cycle as the period, pairs the standardized load coefficient and dust coefficient under the synchronous timestamp into a two-dimensional joint working condition vector, realizing precise alignment of mechanical work intensity and environmental dust load in the time dimension, and providing a structured data foundation for coupling analysis. The coupling mapping calculation subsystem generates scalar characteristic parameters characterizing the comprehensive state of work intensity and dust load by performing coupling degree mapping calculation on the two components in the joint working condition vector; it breaks through the limitations of single-dimensional working condition evaluation and can dynamically reflect the correlation characteristics between load changes and dust concentration fluctuations.

[0030] In summary, this embodiment can analyze the complex operating conditions of machinery in a dusty environment in real time, providing the power control system with characteristic parameters that combine load adaptability and environmental awareness. This multi-source information fusion processing mechanism enables power output commands to simultaneously respond to the machinery's operational needs and environmental safety constraints, enhancing the early warning and control capabilities for dust exceeding standards while ensuring operational efficiency.

[0031] Example 3: Based on Example 2, the coupling mapping calculation subsystem provided in this embodiment of the invention includes: The first influence factor lookup component is used to extract the standardized load coefficient and standardized dust coefficient from the two-dimensional joint working condition vector, and input the two coefficients into the coupling influence factor lookup table. The coupling influence factor lookup table stores nonlinear relationship data calibrated through preset experiments, and outputs a scalar corresponding to the current combination of standardized load coefficient and standardized dust coefficient. The scalar is the first coupling influence factor. The second influence factor lookup component is used to determine the second coupling influence factor from the built-in dust weight curve based on the value of the standardized dust coefficient in the two-dimensional joint working condition vector. The exponentiation component is used to perform exponentiation with the first coupling influence factor as the base and the second coupling influence factor as the exponent. The result of the exponentiation is used as the output scalar value, which is a real-time operating condition characteristic parameter that characterizes the overall state.

[0032] The working principle and beneficial effects of the above technical solution are as follows: The subsystem of this embodiment can transform the two-dimensional joint working condition vector into scalar characteristic parameters with clear physical meaning; it integrates experimental calibration data of load-dust interaction effect with dynamic adjustment mechanism of dust weight, and its nonlinear fusion characteristics are more in line with the physical nature of working condition coupling in actual operation. The final output scalar value can be used as a direct basis for dynamic adaptive control, realizing a comprehensive quantitative assessment of work intensity and environmental load under combined working conditions.

[0033] Example 4: Based on Example 3, the second impact factor search component provided in this embodiment of the invention includes: The calibration data establishment sub-component is used to extract the standardized dust coefficient from the two-dimensional joint working condition vector and input the standardized dust coefficient into the dust weight benchmark curve. The dust weight benchmark curve is established from the initial calibration data, which characterizes the basic dust influence relationship and outputs the benchmark influence factor corresponding to the standardized dust coefficient. The proportional deviation acquisition sub-component is used to access the system's historical running data stack, obtain the historical real-time operating condition characteristic parameters output by the exponentiation component in the previous job cycle, and the corresponding historical first coupling influence factor; compare the proportional deviation of the two sets of historical data by the historical real-time operating condition characteristic parameters and the historical first coupling influence factor, and generate a dynamic correction parameter. The scaling subcomponent is used to scale the baseline influence factor according to the dynamic correction parameters, and the scaled result is used as the final output scalar value. The scalar value is the second coupling influence factor.

[0034] The working principle and beneficial effects of the above technical solution are as follows: This embodiment realizes the transformation of the dust influence factor from static calibration to dynamic adaptation. By introducing a feedback correction mechanism based on historical operating data, the system can overcome the inherent deviation between the initial calibration curve and the actual operating environment, enabling the second coupled influence factor to continuously approximate the dust weight characteristics under real operating conditions; it also improves the long-term adaptability of the coupled mapping calculation subsystem to environmental changes, ensuring the accuracy and robustness of the real-time operating condition characteristic parameter output.

[0035] Example 5: Figure 4 As shown, based on Embodiment 1, the intelligent identification and dust removal configuration system provided in this embodiment of the invention includes: The content mapping subsystem is used to receive real-time operating condition characteristic parameters, and output the dust removal demand benchmark value corresponding to the real-time operating condition characteristic parameters through dust removal demand benchmark mapping; the rated power identifier performs power-capacity mapping and outputs the rated air volume value and rated negative pressure value of the dust collector corresponding to the rated power identifier. The instruction matching subsystem is used to compare the dust removal demand baseline value with the rated air volume value and rated negative pressure value of the dust collector, respectively, to generate the air volume demand ratio coefficient and the negative pressure demand ratio coefficient; then, based on the built-in matching rule set, using the two ratio coefficients as input, it queries to obtain the optimal matching fan speed instruction value and valve opening instruction value. The instruction encapsulation subsystem is used to format and encapsulate the fan speed instruction value and the valve opening instruction value to form the second-stage dust collector operation configuration instruction.

[0036] The working principle and beneficial effects of the above technical solution are as follows: This embodiment achieves refined dynamic control of the dust removal process by integrating three subsystems: content mapping, instruction matching, and instruction encapsulation. The content mapping subsystem converts real-time operating condition characteristic parameters into dust removal demand baseline values ​​and matches the corresponding rated airflow and negative pressure values ​​of the dust collector based on the rated power identifier, providing a baseline for subsequent control. The instruction matching subsystem generates airflow and negative pressure demand ratio coefficients by comparing the dust removal demand baseline values ​​with the rated airflow and negative pressure values, and maps the optimal fan speed and valve opening commands adapted to the current operating conditions based on a built-in rule set. The instruction encapsulation subsystem standardizes and encapsulates the instructions, forming operating configuration commands that can be directly issued to the dust removal equipment.

[0037] In summary, this embodiment can dynamically adjust the operating parameters of the dust removal equipment according to real-time operating conditions, achieve synergistic optimization of dust removal efficiency and energy consumption, improve system response accuracy and operational stability, reduce the need for manual intervention, and enhance adaptability to operating conditions.

[0038] Example 6: Based on Example 5, the instruction matching subsystem provided in this embodiment of the invention includes: The level determination component is used to determine the level of two proportional coefficients based on the set discrete level threshold, and output the corresponding air volume demand level code and negative pressure demand level code; the two level codes are concatenated according to the preset format to generate a joint operating condition status code. The retrieval and matching component is used to input the generated joint operating condition status code into a rule base with multiple pre-stored control parameter combinations for retrieval and matching. Each control parameter combination in the rule base consists of a pair of intermediate control parameters and is associated with a unique joint operating condition status code. The rule base retrieves entries that are completely consistent with the input joint operating condition status code and outputs the pair of intermediate control parameters corresponding to the entry, namely the intermediate speed parameter and the intermediate opening parameter. The conversion program component is used to input the retrieved intermediate speed parameters and intermediate opening parameters into the fan speed command converter and valve opening command conversion program, respectively, and output the fan speed command value and valve opening command value according to the input intermediate speed parameters and intermediate opening parameters.

[0039] The working principle and beneficial effects of the above technical solution are as follows: The instruction matching subsystem of this embodiment achieves automated matching and output of fan and valve control parameters through the collaborative work of various modules. The level determination component discretizes the proportional coefficient into airflow demand level codes and negative pressure demand level codes, and generates a joint operating condition status code, completing the standardized representation of the operating condition status. The retrieval and matching component retrieves the corresponding intermediate control parameter combination from the rule base based on the joint operating condition status code, ensuring accurate matching between control parameters and operating condition status. The conversion program component converts the intermediate parameters into directly executable fan speed command values ​​and valve opening command values, forming a complete control instruction output chain. This embodiment, through a structured encoding, retrieval, and conversion process, achieves efficient and accurate mapping from operating condition status to control instructions, improving the response speed and stability of the control system.

[0040] Example 7: As Figure 5 As shown, based on Embodiment 1, the integrated adaptation and motion execution system provided in this embodiment of the invention includes: The feature vector integration subsystem is used to parse the power output level parameters from the first-level adaptive power output command and the dust collector operating status parameters from the second-level dust collector operation configuration command; combine the two parameters into an integrated feature vector; and map the integrated feature vector to the corresponding cooperative mode code. The operation parameter calculation subsystem is used to activate the corresponding motion parameter calculation rules according to the cooperative mode encoding. It takes the specific values ​​in the integrated feature vector as input and calculates the key point sequence of the motion trajectory and the benchmark value of the operation speed through the built-in algorithm. The packaging and conversion subsystem is used to package and convert the key point sequence of the motion trajectory and the working speed reference value into the third-level motion control instructions for driving the actuator, including adaptive motion trajectory and working speed, according to the protocol specifications of the robot chassis and motion actuator control interface.

[0041] The working principle and beneficial effects of the above technical solution are as follows: The integrated adaptation and motion execution system of this embodiment achieves accurate conversion from multi-level instructions to motion control instructions through a modular process. The feature vector integration subsystem integrates power output level parameters and dust collector operating status parameters into an integrated feature vector and maps it to a cooperative mode code, completing a unified representation of complex working conditions. The operating parameter calculation subsystem calls the corresponding calculation rules according to the cooperative mode code, and generates a sequence of key points of the motion trajectory and a reference value of the operating speed based on the feature vector values, realizing dynamic adaptation of motion parameters. The packaging and conversion subsystem converts the calculated trajectory and speed parameters into directly driveable third-level motion control instructions according to the actuator interface protocol, ensuring the compatibility of the instruction format with the execution equipment. This embodiment achieves reliable conversion from multi-source instructions to standardized motion control instructions through the serial processing of feature integration, parameter calculation, and instruction packaging, improving the adaptability and control accuracy of the motion execution system to complex working conditions.

[0042] Example 8: Based on Example 7, the operating parameter calculation subsystem provided in this embodiment of the invention includes: The parameter output component is used to output the trajectory curvature parameters by calculating the proportional relationship between the power output level parameters and the curvature reference value and superimposing a fixed curvature compensation amount; at the same time, it outputs the trajectory point spacing parameters by calculating the proportional relationship between the power output level parameters and the spacing reference value and superimposing a fixed spacing compensation amount. The iterative rotation component is used to iteratively rotate the movement direction angle based on the coordinate data of the starting point of the operation, combined with the trajectory curvature parameter, and determine the displacement step size of each iteration according to the trajectory point spacing parameter. The coordinate iteration steps are executed cyclically to output a series of key point coordinate data arranged in sequence, forming a key point sequence. A fixed component is superimposed to extract the dust collector operating status parameters from the integrated feature vector. The dust collector operating status parameters are multiplied by the speed influence coefficient stored in the system and a fixed factor offset is superimposed to output the speed adjustment factor. The speed adjustment factor is multiplied by the rated operating speed value to output the operating speed reference value.

[0043] The working principle and beneficial effects of the above technical solution are as follows: The operating parameter calculation subsystem of this embodiment achieves accurate generation of motion trajectory parameters and operating speed reference values ​​through the collaborative efforts of various components. The parameter output component, based on the proportional relationship between the power output level parameters and the reference value, outputs trajectory curvature parameters and trajectory point spacing parameters after superimposing compensation, providing basic geometric constraints for trajectory generation. The iterative rotation component uses the starting point coordinates as the initial state, iteratively updates the direction angle in combination with the curvature parameters, determines the displacement step size based on the point spacing parameters, and generates an ordered sequence of key point coordinates through iterative calculation, completing the geometric construction of the motion trajectory. The superimposed fixing component calculates the speed adjustment factor using the dust collector operating status parameters and the speed influence coefficient, superimposes the offset, and combines it with the rated operating speed to output an operating speed reference value adapted to the working conditions. This embodiment, through the cascaded processing of parameter calculation, iterative generation, and speed adaptation, achieves quantitative conversion from working condition characteristics to the motion trajectory key point sequence and operating speed reference value, ensuring accurate matching of motion parameters and dynamic working conditions.

[0044] Example 9: Based on Example 8, the iterative rotation component provided in this embodiment of the invention includes: The initialization and incremental preparation subcomponent is used to set the coordinates of the starting point of the task to the current coordinates and to set an initial orientation angle. It converts the trajectory curvature parameter into an orientation angle increment value, which defines the amount of change in the orientation angle in each iteration. The single-iteration calculation sub-component is used to calculate the new orientation angle for this iteration by adding the current orientation angle and the orientation angle increment value; based on the trajectory point spacing parameter and the new orientation angle, the spacing is decomposed into two displacement components along the robot's forward direction on the X-axis and Y-axis of the planar coordinate system, respectively obtaining the X-axis displacement increment and the Y-axis displacement increment; The coordinate update and sequence construction sub-component is used to add the X component of the current coordinate to the calculated X-axis displacement increment to obtain the X coordinate of the new key point; add the Y component of the current coordinate to the calculated Y-axis displacement increment to obtain the Y coordinate of the new key point; add the generated new coordinate point to the key point sequence; update the coordinate of the new key point to the current coordinate of the next iteration; and update the new orientation angle calculated in this iteration to the current orientation angle of the next iteration; until the number of generated key points reaches a preset value, the complete key point sequence consisting of the coordinates of all key points generated in all iterations arranged in order is output.

[0045] The working principle and beneficial effects of the above technical solution are as follows: The iterative rotation component in this embodiment realizes the automatic generation of key point sequences based on curvature and spacing parameters through a structured process. The initialization and incremental preparation subcomponent sets the starting coordinates and initial direction angle, and converts the trajectory curvature parameters into direction angle increment values, providing an initial state and angle change benchmark for iterative calculation. The single iteration calculation subcomponent calculates the new direction angle by combining the current direction angle and direction angle increment value, and decomposes the X-axis and Y-axis displacement increments according to the trajectory point spacing parameters to complete the geometric parameter calculation of single-step motion. The coordinate update and sequence construction subcomponent updates the current coordinates by accumulating displacement increments, adds the generated key points to the sequence in sequence, and synchronously updates the direction angle and coordinate state for subsequent iterations, repeating the process until the preset number of key points is reached, and finally outputs a complete key point sequence. This embodiment realizes an automated process of generating trajectory key points step by step at fixed intervals from the starting point, along the curvature constraint path, through the series operation of initialization preparation, single-step iterative calculation, and state cyclic update, ensuring the continuity and geometric accuracy of the motion trajectory.

[0046] Example 10: Based on Example 9, the single-iteration calculation sub-component provided in this embodiment of the invention includes: The orientation correction coefficient calculation module is used to obtain a dynamic reference orientation angle maintained in real time according to the current operation stage, using the new orientation angle generated in this iteration; the difference between the new orientation angle and the dynamic reference orientation angle is calculated to obtain an orientation angle deviation value; the orientation angle deviation value is input into the pre-stored deviation-correction relationship dataset in the system for matching, and an orientation correction coefficient is output. The axial weight generation module is used to multiply the obtained direction correction coefficient by the trajectory point spacing parameter to obtain an intermediate adjustment parameter; the intermediate adjustment parameter is scaled according to the built-in scaling reference value to output a scaling factor; and the required X-axis weight and Y-axis weight values ​​are simultaneously calculated according to the weight and constraint rules of the scaling factor. The X-axis displacement increment calculation module is used to multiply the trajectory point spacing parameter with the obtained X-axis weight value to obtain an initial X-axis displacement; the initial X-axis displacement is compared with the system's pre-stored minimum X-axis displacement threshold, and the larger one is taken as the X-axis displacement increment.

[0047] The Y-axis displacement increment calculation module is used to multiply the trajectory point spacing parameter with the obtained Y-axis weight value to obtain an initial Y-axis displacement; the initial Y-axis displacement is compared with the minimum Y-axis displacement threshold, and the larger one is taken as the Y-axis displacement increment.

[0048] The working principle and beneficial effects of the above technical solution are as follows: The single-iteration calculation sub-component in this embodiment achieves precise allocation of displacement increments under curvature guidance through a multi-layer calculation and adjustment mechanism. The direction correction coefficient calculation module, based on the deviation between the new direction angle and the dynamic reference direction angle, outputs the direction correction coefficient by matching the pre-stored deviation-correction relationship dataset, transforming the change in direction angle into a quantifiable adjustment parameter. The axial allocation weight generation module multiplies the direction correction coefficient with the trajectory point spacing parameter to obtain intermediate adjustment parameters, generates a scaling factor after scaling, and synchronously calculates the X-axis and Y-axis allocation weight values ​​according to the weight constraint rules, realizing the dynamic allocation of displacement resources in the planar coordinate system. The X-axis displacement increment calculation module calculates the initial X-axis displacement by combining the trajectory point spacing parameter and the X-axis allocation weight value, and takes the larger value by comparing it with the minimum X-axis displacement threshold to ensure that the X-axis displacement increment meets the minimum motion requirements of the system. The Y-axis displacement increment calculation module processes the Y-axis displacement with the same logic, outputting the Y-axis displacement increment by multiplying the weights and comparing it with the threshold. This embodiment, from directional deviation identification to correction coefficient matching, and then to axial weight allocation and displacement threshold protection, jointly ensures that the displacement increment generated in each iteration not only meets the directional adjustment requirements brought about by curvature changes, but also satisfies the system's constraints on the minimum displacement of each axis, thereby supporting the trajectory key point sequence to maintain geometric rationality and motion feasibility in continuous iteration.

[0049] Example 11: Based on Example 10, the axial weight generation module provided in this embodiment of the invention includes: The evolution factor generation submodule is used to multiply the scaling factor with the X-axis weight allocation value to obtain a basic product; add an X-axis trend compensation value dynamically generated from historical weight fluctuation data within the system to the basic product, and output the final X-axis evolution factor; perform the same non-linear superposition operation logic on the same scaling factor and the Y-axis weight allocation value of the previous period, and use the generated Y-axis trend compensation value to output the final Y-axis evolution factor. The intermediate weight value generation submodule is used to calculate the absolute value of the difference between two evolution factors to obtain the evolution difference value; the evolution difference value is compared with the difference threshold: if the evolution difference value is less than or equal to the difference threshold, the X-axis evolution factor is output as the initial X-axis weight, and the Y-axis evolution factor is output as the initial Y-axis weight; If the evolutionary difference value is greater than the difference threshold, the rebalancing procedure is initiated: taking the Y-axis evolutionary factor as a benchmark, an adjustment amount determined by the evolutionary difference value and the preset balance coefficient is subtracted from it to obtain a new Y-axis value; at the same time, taking the X-axis evolutionary factor as a benchmark, the same adjustment amount is added to it to obtain a new X-axis value; the new X-axis value and Y-axis value are output as the initial X-axis weight and the initial Y-axis weight, respectively. The final weight generation submodule is used to add the initial X-axis weight and the initial Y-axis weight to obtain the real-time weight sum; divide the system-defined constant weight sum setting value by the real-time weight sum to calculate a normalized scaling factor; multiply the initial X-axis weight by the normalized scaling factor to obtain and output the final X-axis weight allocation value; and multiply the initial Y-axis weight by the normalized scaling factor to obtain and output the final Y-axis weight allocation value.

[0050] The working principle and beneficial effects of the above technical solution are as follows: The evolutionary factor is the basic predicted value for weight generation. It integrates the current weight ratio, historical trend information, and system gain to generate an axial weight prediction value with trend-following characteristics. The intermediate weight value is the result of the evolutionary factor after difference suppression and rebalancing. Its function is to ensure that the difference between the X-axis and Y-axis weights does not exceed the allowable range, preventing system imbalance or instability caused by excessively strong single-axis trends. Its core function is to realize trend extrapolation, enabling weight allocation to adapt to recent change inertia and enhancing the continuity and smoothness of the system. This embodiment, from the generation of evolutionary factors with integrated trend compensation to the weight balance adjustment based on difference thresholds, and then to the normalization scaling with a constant sum as the target, jointly achieves stable calculation of axial weight allocation under trend following, difference suppression, and total constraint, providing weight input that meets the overall coordination requirements of the system for displacement increment calculation.

[0051] Example 12: Based on Example 11, the evolutionary factor generation submodule provided in this embodiment of the invention includes: The weight sequence acquisition unit is used to extract the final X-axis weight values ​​recorded in the last five working cycles from the circular buffer allocated by the system, forming an ordered historical weight sequence; obtain the difference between adjacent weight values ​​in the historical weight sequence, and output four sequentially arranged weight change amounts; The conformal direction recognition unit is used to identify the consistency of the sign direction of the weight changes: if all weight changes are non-negative, a positive trend indicator is output; if all weight changes are non-positive, a negative trend indicator is output; if the sign directions are inconsistent, a no significant trend indicator is output; at the same time, the average absolute value of the weight changes is obtained, and the average change amplitude is output. The trend indicator output unit is used to output the original trend strength value by multiplying the average change amplitude by a fixed gain coefficient when the trend indicator is positive; if the trend indicator is negative, it outputs the original trend strength value by multiplying the average change amplitude by another fixed gain coefficient; if the trend indicator is no significant trend, it outputs zero as the original trend strength value; and multiplies the original trend strength value by a scaling factor, and outputs the result as the X-axis trend compensation value used in the current period.

[0052] The working principle and beneficial effects of the above technical solution are as follows: Trend identification is a qualitative judgment of the direction of historical weight changes, used to determine whether to apply trend compensation and the direction of compensation. Its core function is trend detection and filtering, distinguishing between random fluctuations and continuous trends, avoiding overreaction of the system to noise, and simultaneously capturing the true trend for enhancement. In this embodiment, the change amount is extracted from the historical weight sequence, the direction and magnitude of the change are identified, and then a quantified trend compensation value is generated by adjusting the direction-related gain coefficient and scaling factor. The trend compensation value integrates the directional characteristics and magnitude information of historical weight changes, providing a trend correction input based on historical data for the calculation of evolution factors, enhancing the adaptability of weight allocation to historical evolution patterns.

[0053] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of this invention, this invention is also intended to include these modifications and variations.

Claims

1. A modular disassembly robot supporting adaptive configuration under working conditions, characterized in that, Include: The built-in power source is used to obtain DC power with controlled voltage and current through the status monitoring and power distribution processing of the battery management system. The DC power is used to drive the chassis movement and supply power to the external module interface. The quick-connect interface connects to an external dust removal module, which is used to obtain a clean airflow after multi-stage filtration and centrifugal separation within the module, forming a negative pressure dust suction and dust collection function. It also allows for plug-and-play connection of the air and electrical circuits to the chassis via the external module interface. The motion actuator is used to enhance and convert torque through the reducer and transmission mechanism to obtain a large torque, variable speed rotation or linear output that meets the terrain requirements, forming a compound motion of chassis travel, steering and attitude adjustment. The controller module connects to the built-in power source, the external dust removal module via a quick interface, and the motion actuators. It receives signals and commands from external operating terminals and sensors. Through centralized processing of command parsing, path planning, and multi-axis kinematics calculation, it obtains precise control commands for each actuator and interface, forming a coordinated control function for power scheduling of the built-in power source, coordinated action of motion mechanisms, and start / stop interlocking of external modules.

2. The modular disassembly robot supporting adaptive working condition configuration as described in claim 1, characterized in that, The controller module also includes: The working condition perception and power control system is used to collect raw data on the working load torque and the concentration of dust in the space in real time, and integrate and calculate them into real-time working condition characteristic parameters to generate the first-level adaptive power output command. The intelligent identification and dust removal configuration system is used to receive real-time operating condition characteristic parameters and automatically obtain the rated power identifier of the external dust removal module, thereby generating the second-stage dust collector operation configuration command. An integrated adaptation and motion execution system is used to simultaneously receive two sets of instructions and perform integrated calculations to drive the robot to execute a third-level adaptive motion trajectory and operating speed that matches the current power and dust removal status.

3. The modular disassembly robot supporting adaptive working condition configuration as described in claim 2, characterized in that, The operating condition perception and power control system includes: The operating condition fusion processing subsystem is used to standardize the raw data into dimensionless standardized load coefficients and standardized dust coefficients; The coefficient pairing processing subsystem is used to pair synchronized standardized coefficients to form a two-dimensional joint operating condition vector; The coupled mapping computation subsystem is used to compute the two-dimensional joint operating condition vector and output scalar values ​​as real-time operating condition characteristic parameters.

4. The modular disassembly robot supporting adaptive working condition configuration as described in claim 2, characterized in that, The intelligent identification and dust removal configuration system includes: The content mapping subsystem is used to map real-time operating condition characteristic parameters to dust removal demand baseline values, and to map rated power identifiers to dust collector rated air volume and rated negative pressure values. The instruction matching subsystem is used to compare the baseline value with the rated value to obtain the demand ratio coefficient, and based on this, query the matching rule set to obtain the instruction values ​​of fan speed and valve opening. The instruction encapsulation subsystem is used to encapsulate instruction values ​​into operation configuration instructions for the second-stage dust collector.

5. The modular disassembly robot supporting adaptive configuration under working conditions as described in claim 2, characterized in that, The integrated adaptation and motion execution system includes: The feature vector integration subsystem is used to parse the power output level parameters and dust collector operating status parameters from the first-level and second-level instructions, combine them into an integrated feature vector, and map them into a cooperative mode code. The operation parameter calculation subsystem is used to calculate the key point sequence of the motion trajectory and the baseline value of the operation speed according to the cooperative mode encoding. The packaging and conversion subsystem is used to convert key point sequences and velocity reference values ​​into third-level motion control commands for driving actuators.

6. The modular disassembly robot supporting adaptive working condition configuration as described in claim 5, characterized in that, The runtime parameter calculation subsystem includes: The parameter output component is used to calculate and output the trajectory curvature parameter and trajectory point spacing parameter based on the power output level parameter; The iterative rotation component is used to iteratively rotate the direction angle based on the starting point coordinates and direction angle of the operation, combined with the trajectory curvature parameter, and calculate the displacement based on the trajectory point spacing parameter, and cyclically output the key point sequence; The superimposed fixed components are used to calculate the speed adjustment factor based on the dust collector's operating status parameters, and then output the operating speed benchmark value.

7. The modular disassembly robot supporting adaptive working condition configuration as described in claim 6, characterized in that, The iterative rotation component includes: The initialization and incremental preparation subcomponent is used to set the initial coordinates and orientation angle and convert the trajectory curvature parameters into orientation angle increment values. A single-iteration calculation sub-component is used to calculate the new orientation angle and decompose it to obtain the X-axis and Y-axis displacement increments; The coordinate update and sequence construction sub-component is used to update coordinates and construct a keypoint sequence, which is then output after iterating to a preset number.

8. The modular disassembly robot supporting adaptive working condition configuration as described in claim 7, characterized in that, The single-iteration calculation subcomponent includes: The orientation correction coefficient calculation module is used to calculate the orientation angle deviation and query the dataset to output the orientation correction coefficient; The axial weight generation module is used to calculate the weight values ​​of the X-axis and Y-axis based on the direction correction coefficient and the trajectory point spacing parameter. The X-axis displacement increment calculation module is used to calculate the X-axis displacement increment and compare it with the minimum displacement threshold to take the larger one. The Y-axis displacement increment calculation module is used to calculate the Y-axis displacement increment and compare it with the minimum displacement threshold to take the larger one.

9. The modular disassembly robot supporting adaptive working condition configuration as described in claim 8, characterized in that, The axial weighting generation module includes: The evolution factor generation submodule is used to multiply the scaling factor and weight value and add the historical trend compensation value to output the X-axis and Y-axis evolution factors. The intermediate weight value generation submodule is used to compare the difference between two evolution factors. If the difference is greater than the threshold, the rebalancing procedure is started to adjust and the initial weight is output. The final weight generation submodule is used to normalize the initial weights and output the final X-axis and Y-axis weight values.

10. The modular disassembly robot supporting adaptive working condition configuration as described in claim 9, characterized in that, The evolution factor generation submodule includes: The weight sequence acquisition unit is used to extract historical weight sequences and calculate weight changes; The conformal direction recognition unit is used to identify the trend direction of the weight change and output the trend indicator and average change magnitude. The trend indicator output unit is used to obtain the original trend strength value by processing the average change amplitude of the trend indicator, and then multiply it by the scaling factor to output the X-axis trend compensation value for the current period.

Images