A mechanical arm wireless energy transmission method and system with direction neutralization strategy

Abstract

The application discloses a kind of mechanical arm wireless energy transmission method and system with direction neutralization strategy, it is related to wireless energy transmission technical field.Passive sensing module receives magnetic field signal and combines node feedback, extract node three-dimensional coordinate, direction and energy state, construct three-dimensional space layout diagram;Low-power intelligent control module judges energy supply scene accordingly, single-source scene calculates the optimal alignment posture of coil, multi-source scene divides energy supply area by clustering, calculates geometric center and adjusts direction according to node energy, power weight, avoids barrier scene and selects suboptimal path without shelter;Control module issues instruction to mechanical arm servo system and coil driving module, adjusts coil to target posture and structure;Compare data with optimal value, low-power node is preferentially stored energy, and energy sufficient node is promptly corrected posture.The method improves transmission efficiency and energy supply reliability by passive sensing, direction neutralization strategy and dynamic correction, adapts to the demand of multi-scene wireless energy transmission.

Description

Technical Field

[0001] This invention relates to the field of wireless power transfer technology, and specifically to a method and system for wireless power transfer of a robotic arm with a direction neutralization strategy. Background Technology

[0002] Wireless power transfer technology, especially magnetically coupled resonant power transfer, has been widely adopted in various scenarios such as wireless charging for consumer electronics, power supply for industrial sensor networks, power supply for medical implantable devices, and extended battery life for mobile robots, thanks to its advantages of high transmission efficiency and good electromagnetic compatibility. In magnetically coupled wireless power transfer systems, the spatial attitude matching degree (including relative angle, parallelism, and offset) between the transmitting and receiving coils directly determines the mutual inductance coefficient between the coils, which in turn has a decisive impact on the transmission efficiency. When the coil axes are completely coincident and parallel, the mutual inductance coefficient is at its maximum, and the transmission efficiency can reach its peak. However, when there is an angular deviation or positional offset, the mutual inductance coefficient will decrease significantly, and may even lead to power interruption.

[0003] In current multi-node wireless power supply networks, the magnetic coupling coils of most nodes are fixedly installed, and their position and orientation cannot be dynamically adjusted. While this design may meet basic requirements in static, simple environments, real-world applications often involve dynamic changes, such as the movement of mobile robots, temporary obstruction by obstacles in industrial settings, and natural shifts in sensor nodes. Fixed coils struggle to adapt to these changes, leading to a significant reduction in power supply stability and reliability. Furthermore, as distributed networks expand, the demand for coordinated power supply from multiple transmitting nodes to a single receiving node becomes increasingly prominent. Existing fixed coil systems cannot achieve efficient aggregation of energy from multiple sources, limiting the network's application scope.

[0004] Chinese patent (publication number CN201610621338.8) discloses a method and device for dynamically adjusting the magnetic field of a coupling coil in wireless power transmission. This method optimizes efficiency by adjusting magnetic field parameters, but it does not involve a node position perception and environment modeling module. It relies entirely on preset coil position parameters and cannot identify obstacles in the transmission path. In multi-obstruction scenarios such as industrial workshops, magnetic field coupling interruption problems are likely to occur.

[0005] Chinese patent (publication number CN202511563014) discloses a multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system. This technology constructs a passive Internet of Things system model and optimizes the power allocation of multiple base stations using statistical average energy collection efficiency as an indicator. However, it does not have a robotic arm or adjustable coil structure and only achieves energy efficiency optimization through power allocation. It lacks coil attitude adjustment and spatial coupling path optimization mechanisms, and cannot solve the problem of magnetic field superposition disorder caused by the dispersed distribution of multiple base stations. Furthermore, it does not establish a differentiated correction strategy based on the energy state of nodes. Low-power nodes still allocate power according to a uniform rule, which limits energy storage efficiency.

[0006] In summary, although existing technologies have explored the integration of wireless power transmission with robotic arms and dynamic adjustment, they still suffer from problems such as poor adaptability to complex environments, insufficient multi-node coordination capabilities, lack of intelligent sensing capabilities, and limitations in the rigidity of coil structures. A method that can solve these problems has become the key to breakthroughs in this field. Summary of the Invention

[0007] Based on the above-mentioned technical problems, this application discloses a wireless energy transfer method for a robotic arm with a direction neutralization strategy, specifically as follows:

[0008] The passive sensing module receives the magnetic field signals generated by the energy transmission of surrounding nodes, extracts the three-dimensional coordinates, relative directions and energy state information of each energy transmission node, and constructs a three-dimensional spatial layout map of the surrounding environment and node distribution.

[0009] Based on the three-dimensional spatial layout diagram and node energy status information, the low-power intelligent control module determines the power supply scenario type and generates adjustment decisions. If it is a single-source power supply scenario, the optimal alignment posture between the magnetic coupling coil and the target power supply node is calculated.

[0010] In the case of a multi-source power supply scenario, the system divides the area into power supply regions using a low-power clustering algorithm, calculates the geometric center coordinates of the power supply regions, assigns weights based on the remaining energy and output power capabilities of each transmitting node, and determines the telescopic and folding structure parameters of the coil to adapt to the current transmission distance.

[0011] If it is an obstacle avoidance adjustment scenario, the unobstructed suboptimal coupling path is selected through the three-dimensional spatial layout diagram, and the coil attitude and telescopic folding structure parameters are determined with the goal of minimizing energy consumption.

[0012] Based on the adjustment decision, the low-power intelligent control module sends a first control command to the servo drive system of the multi-degree-of-freedom robotic arm and a second control command to the drive module of the magnetic coupling coil to adjust to the target structural state.

[0013] The power management module converts energy into high-frequency AC power that meets the requirements of magnetic coupling transmission according to instructions and outputs it. At the same time, it collects the status data of transmission efficiency and output voltage and feeds them back to the low-power intelligent control module.

[0014] By comparing the current state data with the target optimal value through the low-power intelligent control module, if there is a deviation, the low-power node prioritizes energy storage and only starts the multi-degree-of-freedom robotic arm to adjust the coil posture when necessary; the node with sufficient energy corrects the coil posture in time to ensure transmission efficiency, thus achieving dynamic correction.

[0015] Preferably, the extraction of the three-dimensional coordinates of each energy transmission node specifically involves: calculating the relative distance between nodes by measuring the amplitude changes of the magnetic field signal at different times using a magnetic field signal intensity positioning algorithm, as shown in the formula:

[0016]

[0017] in, This represents the relative distance between the passive sensing module and the energy transmission node. The spatial attenuation coefficient, The magnetic field signal transmission power of the transmitting node. For the transmitting antenna gain, For the receiver antenna gain, The wavelength of the magnetic field signal. The power of the magnetic field signal received by the passive sensing module.

[0018] Preferably, the optimal alignment attitude is specifically determined by maximizing the coupling coefficient between the magnetically coupled coils to determine the optimal alignment attitude for the target, as shown in the formula:

[0019]

[0020] in The coupling coefficient is... For the mutual inductance between the transmitting coil and the receiving coil, For the self-inductance of the transmitting coil, For the self-inductance of the receiving coil; preset the optimal coupling range ( ),when The value reaches ( When the values ​​are between 0 and 1, the coil is determined to have reached the optimal alignment posture.

[0021] Preferably, determining the telescopic folding structure parameters for coil adaptation to the current transmission distance specifically involves: dividing spatially proximate transmitting nodes into power supply areas using a low-power clustering algorithm; calculating the geometric center coordinates of these power supply areas; establishing a coordinate system with the node itself as the origin; solving for the azimuth and elevation angles corresponding to the geometric center as the reference adjustment direction for the coil; and then assigning weights based on the remaining energy and output power capability of each transmitting node, using the following formula:

[0022]

[0023] in, For the first The weight of each transmitting node, For weighting coefficients, For the first The remaining energy of each transmitting node, For the first Maximum energy storage capacity of each launch node For the first The actual output power of each transmitting node For the first Maximum output power of each transmitting node;

[0024] Based on the weight allocation results, a preset fine-tuning angle range is defined. The parameters of the telescopic and folding structure of the coil are dynamically calculated to adapt to the current transmission distance, and the fine-tuning angle of the reference adjustment direction is determined. The formula is:

[0025]

[0026] in, The maximum weighting coefficient among all transmitting nodes. This is the minimum weighting coefficient among all transmitting nodes.

[0027] Preferably, the low-power clustering algorithm is implemented using the K-means clustering algorithm, and the iterative update formula for the cluster centers is:

[0028]

[0029] in, For the first The energy supply area is in the first The cluster center coordinates of the next iteration. For the first The number of transmission nodes within a power supply area For the first In the nth iteration, belonging to the th A set of transmitter nodes in a power supply area For the first The three-dimensional coordinates of each transmitting node.

[0030] Preferably, the telescopic folding structure parameters are specifically defined as follows: the relationship between the telescopic folding structure parameters and the transmission distance is matched using a linear fitting model, and the formula is:

[0031]

[0032] in The minimum diameter of the coil, The maximum diameter of the coil. dmin represents the maximum transmission distance, and dmin represents the minimum transmission distance. This represents the actual transmission distance.

[0033] Preferably, the minimum energy consumption is specifically calculated by minimizing the sum of the robotic arm drive energy consumption and the coil transmission energy consumption, with the total energy consumption being... The formula is:

[0034]

[0035] in, Energy consumption for robotic arm drive For the first robotic arm Power of each joint The working time of the first joint. Energy consumption for coil transmission For load power, For transmission time, For energy transfer efficiency.

[0036] Preferably, the energy transmission process specifically involves: dynamically adjusting the frequency of the high-frequency alternating current based on the transmission distance, using the following formula:

[0037]

[0038] in, The frequency of high-frequency alternating current. As the reference frequency, This is the frequency attenuation coefficient. As a reference transmission distance, This represents the actual transmission distance.

[0039] Preferably, the dynamic correction process specifically involves: after the robotic arm wireless energy transmission node completes coil attitude and structure adjustment and enters the stable energy transmission stage, the low-power intelligent control module actively triggers a data acquisition command to send data acquisition requests to the signal acquisition module and power management and energy storage module of the magnetic coupling coil, respectively, to collect transmission status data and node energy status data.

[0040] The system presets the target optimal value range and the optimal output voltage range for energy transmission. It then performs deviation judgment and node energy status determination on the collected data and compares them with the target optimal value range and the optimal output voltage range. If the optimal range conditions are not met, a dynamic correction process is triggered.

[0041] Based on the node energy state determination results, the low-power intelligent control module executes a differentiated dynamic correction strategy. For low-power nodes, the principle is to prioritize energy storage and minimize correction energy consumption, performing energy storage assurance and deviation assessment. The formula is as follows:

[0042]

[0043] in, The deviation coefficient, This represents the average value of the optimal transmission efficiency. The optimal average output voltage. The safe temperature threshold for the coil, , These are the current transmission efficiency, output voltage, and coil temperature, respectively. For low-power nodes where the deviation exceeds the preset deviation threshold, the low-power intelligent control module uses a step-by-step fine-tuning strategy to adjust the robotic arm.

[0044] With sufficient energy, nodes are designed to quickly eliminate deviations and maintain optimal transmission efficiency. The correction process adopts a real-time response and precise adjustment mode. Combining the three-dimensional spatial layout map with the current transmission parameters, the source of deviation is located through the parameter and attitude mapping model. Based on the real-time position data of the magnetic coupling coil, the target angle of the optimal alignment attitude is calculated.

[0045] After attitude correction is completed, transmission status data are continuously collected for multiple monitoring cycles. If all parameters are within the target optimal value range, the correction is considered successful and the system enters the normal monitoring mode. If deviations still exist, the source of the deviations is re-analyzed, and the correction strategy is adjusted until the deviations are eliminated.

[0046] The aforementioned wireless power transfer system for robotic arms with a direction neutralization strategy is specifically as follows:

[0047] The retractable / foldable magnetic coupling coil module is equipped with a low-power drive module and receives instructions from the low-power control module. According to the target node distance and coupling requirements, it changes its own parameters through the extension or folding of the mechanical structure. Through the design of combining flexible conductive materials with rigid support structure, it can achieve dynamic adjustment of diameter and area and a certain degree of shape deformation.

[0048] The multi-degree-of-freedom robotic arm module is equipped with a high-precision, low-power servo drive system. It achieves full attitude adjustment through a low-power 6-axis robotic arm structure, enabling the coil to rotate, tilt, translate, and perform other attitude adjustments in three-dimensional space. The adjustment accuracy meets the requirements of magnetic coupling alignment, and its energy consumption is entirely supplied by the node energy storage module.

[0049] The passive sensing module receives magnetic field signals generated during energy transmission from surrounding nodes and combines them with signal strength detection to obtain node distance information. It works in conjunction with the communication module to receive position feedback commands from surrounding nodes, achieve angle and direction positioning, and output the three-dimensional coordinates of other surrounding nodes. It identifies metallic obstacles in the environment by changing magnetic field signals, providing obstacle avoidance data for coil adjustment. In passive scenarios, it can be driven by the energy received by the coil.

[0050] The low-power intelligent control module is based on an ultra-low-power MCU. It receives spatial data from the passive sensing module, transmission status data from the coil module, and collaborative information from the communication module. It generates control commands through a built-in low-power algorithm to drive the robotic arm and coil to complete posture and structural adjustments, thereby optimizing the configuration of energy transmission parameters and ensuring maximum energy utilization.

[0051] The power management module relies on the energy received by the coil to achieve charging and discharging. For the transmitting node, the module converts the electrical energy from the energy storage module or external sources into high-frequency AC power that meets the requirements of magnetic coupling transmission. For the receiving node, the module rectifies the high-frequency electrical energy received by the coil into DC power, and after voltage regulation, it supplies power to the load, control module, robotic arm and other components.

[0052] The low-power collaborative communication module uses ultra-low power wireless communication technology to realize information exchange between nodes. The communication content includes node identity information, energy status, location information and control commands, providing communication support for multi-node collaborative power supply, conflict avoidance and network scheduling. The communication process adopts an intermittent working mode to reduce energy consumption.

[0053] Compared with the prior art, the technical solution of this application has the following technical effects:

[0054] This invention achieves 3D environmental modeling by linking a passive sensing module with a low-power collaborative communication module, realizing the integration of node localization, obstacle differentiation and spatial layout construction. Furthermore, by linking the 3D spatial layout map with the power supply scenario judgment module, it reduces node standby power consumption, reduces magnetic field coupling interruption rate, significantly improves environmental adaptability, breaks through the bottleneck of passive sensing, and enhances environmental adaptability and low power consumption capabilities.

[0055] This invention achieves regionalized management of multi-source power supply through a low-power clustering algorithm and node energy and power weight allocation. By linking the benchmark adjustment direction fine-tuning mechanism with the transmission efficiency monitoring module, it reduces the fluctuation of multi-source power supply efficiency, shortens the switching time of power supply nodes, and significantly improves power supply reliability, thus solving the problem of multi-source power supply efficiency imbalance in the prior art.

[0056] This invention achieves dual control of attitude and structure through the collaboration of a multi-degree-of-freedom robotic arm and a retractable and foldable magnetically coupled coil, enabling three-dimensional attitude adjustment and dynamic angle adaptation of the coil. By linking the transmission distance, coil parameters, and the robotic arm servo drive system, it reduces the efficiency loss of medium- and long-distance transmission, shortens the coil adaptation and adjustment time, and significantly improves the coverage capability of multiple scenarios. It breaks through the problems of fixed coil structure and single attitude adjustment in existing technologies, and expands the adaptation range of multiple scenarios.

[0057] This invention achieves a balance between energy consumption and efficiency through node energy status determination and differentiated dynamic correction strategies. By linking the dynamic correction log feedback mechanism with the strategy optimization module, it reduces the proportion of correction energy consumption, shortens the correction lag time, and significantly improves the robustness of system operation.

[0058] This invention achieves coordinated power supply tasks and avoids node conflicts through multi-node master-slave scheduling in collaboration with a low-power intelligent control module and a low-power collaborative communication module. Furthermore, it reduces the occurrence rate of power supply conflicts and shortens fault response time through an anomaly warning mechanism and linkage with an emergency protection module, thereby achieving long-term stable operation and significantly reducing maintenance costs.

[0059] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0060] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0062] Based on the description of the figures and their corresponding technical content in the document, the titles of the figures are as follows:

[0063] Figure 1 This is a flowchart of a wireless power transfer method for a robotic arm with a direction neutralization strategy.

[0064] Figure 2 This is a general architecture diagram of a wireless power transfer method for a robotic arm with a direction neutralization strategy;

[0065] Figure 3 This is a flowchart of a wireless power transfer system for a robotic arm with a direction neutralization strategy.

[0066] Figure 4 This is a diagram of the overall experimental architecture for testing this method and system in an industrial workshop.

[0067] Figure 5 A comparison chart of key performance indicators of the three schemes in the experiment testing this method and system;

[0068] Figure 6The diagram shows the probability distribution of energy transfer efficiency for the three schemes in the experiment testing the method and system.

[0069] Figure 7 This is a comparison chart of the evasion response times of the three schemes in the experiment testing this method and system. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0071] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0072] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0073] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0074] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0075] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0076] Example 1 describes a wireless power transfer method for a robotic arm with a direction neutralization strategy, such as... Figure 1 As shown, it specifically includes:

[0077] The passive sensing module receives magnetic field signals generated by energy transmission from surrounding nodes, extracts the three-dimensional coordinates, relative directions, and energy state information of each energy transmission node, and constructs a three-dimensional spatial layout map of the surrounding environment and node distribution.

[0078] Based on the three-dimensional spatial layout diagram and node energy status information, the low-power intelligent control module determines the power supply scenario type and generates adjustment decisions. If it is a single-source power supply scenario, the optimal alignment posture between the magnetic coupling coil and the target power supply node is calculated.

[0079] In the case of a multi-source power supply scenario, the system divides the area into power supply regions using a low-power clustering algorithm, calculates the geometric center coordinates of the power supply regions, assigns weights based on the remaining energy and output power capabilities of each transmitting node, and determines the telescopic and folding structure parameters of the coil to adapt to the current transmission distance.

[0080] If it is an obstacle avoidance adjustment scenario, the unobstructed suboptimal coupling path is selected through the three-dimensional spatial layout diagram, and the coil attitude and telescopic folding structure parameters are determined with the goal of minimizing energy consumption.

[0081] Based on the adjustment decision, the low-power intelligent control module sends a first control command to the servo drive system of the multi-degree-of-freedom robotic arm and a second control command to the drive module of the magnetic coupling coil to adjust to the target structural state.

[0082] The power management module converts energy into high-frequency AC power that meets the requirements of magnetic coupling transmission according to instructions and outputs it. At the same time, it collects the status data of transmission efficiency and output voltage and feeds them back to the low-power intelligent control module.

[0083] By comparing the current state data with the target optimal value through the low-power intelligent control module, if there is a deviation, the low-power node prioritizes energy storage and only starts the multi-degree-of-freedom robotic arm to adjust the coil posture when necessary; the node with sufficient energy corrects the coil posture in time to ensure transmission efficiency, thus achieving dynamic correction.

[0084] Furthermore, the passive sensing module is the core of data input. It does not require external power supply. By receiving magnetic field signals and node feedback, it obtains the three-dimensional coordinates, relative direction and energy state of the energy transmission node, and simultaneously constructs a three-dimensional spatial layout map containing obstacle information, providing environmental and node data support for subsequent scene decision-making.

[0085] Furthermore, in the execution and transmission phase, the robotic arm drives the coil to complete three-dimensional posture adjustment, including rotation, tilting, and translation. The coil can extend and retract its diameter by 5cm to 30cm and fold its angle from 0° to 90° according to the instructions. The two work together to ensure that the coil is in the optimal coupling state, and then energy transmission is started, while the transmission parameters are collected in real time.

[0086] Furthermore, the extraction of the three-dimensional coordinates of each energy transmission node specifically involves: using a magnetic field signal intensity positioning algorithm, calculating the relative distance between nodes by measuring the amplitude changes of the magnetic field signal at different times, as shown in the formula:

[0087]

[0088] in, This represents the relative distance between the passive sensing module and the energy transmission node. The spatial attenuation coefficient, The magnetic field signal transmission power of the transmitting node. For the transmitting antenna gain, For the receiver antenna gain, The wavelength of the magnetic field signal. The power of the magnetic field signal received by the passive sensing module.

[0089] Furthermore, the optimal alignment posture is specifically determined by maximizing the coupling coefficient between the magnetically coupled coils to determine the optimal alignment posture for the target, as shown in the formula:

[0090]

[0091] in The coupling coefficient is... For the mutual inductance between the transmitting coil and the receiving coil, For the self-inductance of the transmitting coil, For the self-inductance of the receiving coil; preset the optimal coupling range ( ),when The value reaches ( When the values ​​are between 0 and 1, the coil is determined to have reached the optimal alignment posture.

[0092] Furthermore, the determination of the telescopic folding structure parameters for coil adaptation to the current transmission distance specifically involves: dividing spatially proximate transmitting nodes into power supply areas using a low-power clustering algorithm; calculating the geometric center coordinates of these power supply areas; establishing a coordinate system with the node itself as the origin; solving for the azimuth and elevation angles corresponding to the geometric center as the reference adjustment direction for the coil; and then assigning weights based on the remaining energy and output power capability of each transmitting node, using the following formula:

[0093]

[0094] in, For the first The weight of each transmitting node, For weighting coefficients, For the first The remaining energy of each transmitting node, For the first Maximum energy storage capacity of each launch node For the first The actual output power of each transmitting node For the first Maximum output power of each transmitting node;

[0095] Based on the weight allocation results, a preset fine-tuning angle range is defined. The parameters of the telescopic and folding structure of the coil are dynamically calculated to adapt to the current transmission distance, and the fine-tuning angle of the reference adjustment direction is determined. The formula is:

[0096]

[0097] in, The maximum weighting coefficient among all transmitting nodes. This is the minimum weighting coefficient among all transmitting nodes.

[0098] Furthermore, the low-power clustering algorithm specifically involves implementing a low-power clustering algorithm using the K-means clustering algorithm, with the iterative update formula for the cluster centers being:

[0099]

[0100] in, For the first The energy supply area is in the first The cluster center coordinates of the next iteration. For the first The number of transmission nodes within a power supply area For the first In the nth iteration, belonging to the th A set of transmitter nodes in a power supply area For the first The three-dimensional coordinates of each transmitting node.

[0101] Furthermore, the telescopic folding structure parameters are specifically defined as follows: the relationship between the telescopic folding structure parameters and the transmission distance is matched using a linear fitting model, and the formula is:

[0102]

[0103] in The minimum diameter of the coil, The maximum diameter of the coil. dmin represents the maximum transmission distance, and dmin represents the minimum transmission distance. This represents the actual transmission distance.

[0104] Furthermore, the minimum energy consumption specifically refers to: calculating the minimum energy consumption with the goal of minimizing the sum of the robotic arm drive energy consumption and the coil transmission energy consumption, and the total energy consumption... The formula is:

[0105]

[0106] in, Energy consumption for robotic arm drive For the first robotic arm Power of each joint The working time of the first joint. Energy consumption for coil transmission For load power, For transmission time, For energy transfer efficiency.

[0107] Furthermore, the energy transmission process specifically involves: dynamically adjusting the frequency of the high-frequency alternating current based on the transmission distance, using the following formula:

[0108]

[0109] in, The frequency of high-frequency alternating current. As the reference frequency, This is the frequency attenuation coefficient. As a reference transmission distance, This represents the actual transmission distance.

[0110] Furthermore, the dynamic correction process is as follows: after the robotic arm wireless energy transmission node completes coil attitude and structure adjustment and enters the stable energy transmission stage, the low-power intelligent control module actively triggers the data acquisition command to send data acquisition requests to the signal acquisition module and power management and energy storage module of the magnetic coupling coil respectively, and collects transmission status data and node energy status data.

[0111] The system presets the target optimal value range and the optimal output voltage range for energy transmission. It then performs deviation judgment and node energy status determination on the collected data and compares them with the target optimal value range and the optimal output voltage range. If the optimal range conditions are not met, a dynamic correction process is triggered.

[0112] Based on the node energy state determination results, the low-power intelligent control module executes a differentiated dynamic correction strategy. For low-power nodes, the principle is to prioritize energy storage and minimize correction energy consumption, performing energy storage assurance and deviation assessment. The formula is as follows:

[0113]

[0114] in, The deviation coefficient, This represents the average value of the optimal transmission efficiency. The optimal average output voltage. The safe temperature threshold for the coil, , These are the current transmission efficiency, output voltage, and coil temperature, respectively. For low-power nodes where the deviation exceeds the preset deviation threshold, the low-power intelligent control module uses a step-by-step fine-tuning strategy to adjust the robotic arm.

[0115] With sufficient energy, nodes are designed to quickly eliminate deviations and maintain optimal transmission efficiency. The correction process adopts a real-time response and precise adjustment mode. Combining the three-dimensional spatial layout map with the current transmission parameters, the source of deviation is located through the parameter and attitude mapping model. Based on the real-time position data of the magnetic coupling coil, the target angle of the optimal alignment attitude is calculated.

[0116] After attitude correction is completed, transmission status data are continuously collected for multiple monitoring cycles. If all parameters are within the target optimal value range, the correction is considered successful and the system enters the normal monitoring mode. If deviations still exist, the source of the deviations is re-analyzed, and the correction strategy is adjusted until the deviations are eliminated.

[0117] This embodiment details a wireless energy transfer method for a robotic arm with a direction neutralization strategy. A passive sensing module receives magnetic field signals and, combined with node feedback, extracts the three-dimensional coordinates, orientation, and energy state of each node to construct a three-dimensional spatial layout map. A low-power intelligent control module determines the power supply scenario based on this data. In a single-source scenario, the optimal alignment posture of the coil is calculated. In a multi-source scenario, power supply areas are divided through clustering, the geometric center is calculated, and the orientation is fine-tuned according to the node's energy and power weights. In an obstacle avoidance scenario, the optimal unobstructed path is selected. The control module sends commands to the robotic arm servo system and the coil drive module to adjust the coil to the target posture and structure. By comparing the data with the optimal value, nodes with low power are prioritized for energy storage, while nodes with sufficient energy promptly correct their posture.

[0118] Example 2: This example details a wireless power transfer system for a robotic arm with a direction neutralization strategy, specifically as follows:

[0119] The system comprises a retractable and foldable magnetically coupled coil module, a multi-degree-of-freedom robotic arm module, a passive sensing module, a low-power intelligent control module, a power management module, and a low-power collaborative communication module. Each module possesses independent energy receiving, energy transmission, sensing, attitude adjustment, and collaborative communication capabilities. In passive scenarios, node energy relies on energy interaction within the network and built-in energy storage modules for operation. Multiple nodes self-organize to form an efficient wireless energy transmission network.

[0120] The retractable / foldable magnetic coupling coil module is the core component for energy transmission and reception. Its design combines flexible conductive materials with a rigid support structure, allowing for dynamic adjustment of diameter / area and a certain degree of shape deformation. Equipped with a low-power drive module, the coil receives commands from the control module and, based on the target node distance and coupling requirements, alters its parameters through mechanical expansion or folding—reducing the area to enhance magnetic field concentration for short-distance transmission and expanding the area to increase coupling range for long-distance transmission. The coil incorporates voltage and current sensing elements, providing real-time feedback on transmission status data to ensure energy utilization efficiency.

[0121] The multi-degree-of-freedom robotic arm module achieves full attitude adjustment through a low-power 6-axis robotic arm structure, with the coil module fixed to the end effector of the robotic arm. The robotic arm is equipped with a high-precision, low-power servo drive system, enabling the coil to perform rotation, tilting, and translation in three-dimensional space, with adjustment accuracy meeting magnetic coupling alignment requirements. The robotic arm has a built-in position sensor that provides real-time feedback of the coil's current attitude data, providing a basis for closed-loop control. Its energy consumption is entirely supplied by the node energy storage module.

[0122] The multi-source sensing module employs a design that combines energy signal sensing with wireless signal feedback, resulting in controllable energy consumption. This module receives magnetic field signals generated during energy transmission from surrounding nodes and uses signal strength detection to obtain node distance information. Through linkage with the communication module, it receives position feedback commands from surrounding nodes, achieving angle and direction positioning and outputting the 3D coordinates and orientation information of other surrounding nodes. Simultaneously, the module identifies metallic obstacles in the environment through changes in the magnetic field signal, providing obstacle avoidance data for coil adjustments. In passive scenarios, it can be driven by the weak energy received by the coil.

[0123] The low-power intelligent control module uses an ultra-low-power MCU as its core, integrating data processing, decision control, and algorithm execution functions, with energy consumption controlled at the microwatt level. The control module receives spatial data from the passive sensing module, transmission status data from the coil module, and collaborative information from the communication module. Through built-in low-power algorithms (including position calculation algorithms, orientation neutralization decision algorithms, and attitude control algorithms), it generates control commands to drive the robotic arm and coils to complete attitude and structural adjustments. At the same time, it optimizes the configuration of energy transmission parameters to ensure maximum energy utilization.

[0124] The power management module is the core energy guarantee module. In passive scenarios, it has no external energy interface and relies entirely on the energy received by the coil for charging and discharging. In scenarios with external power supply, it can be connected to an external power source. For transmitting nodes, this module converts the electrical energy from the energy storage module or external source into high-frequency AC power that meets the requirements of magnetic coupling transmission and adjusts the output power according to control commands. For receiving nodes, this module rectifies the high-frequency electrical energy received by the coil into DC power, and after voltage regulation, supplies power to the load, control module, robotic arm, and other components, while storing excess energy in energy storage elements. The module has built-in overload and overvoltage protection mechanisms and low battery warning functions to ensure stable network operation.

[0125] Low-power collaborative communication module: Employing ultra-low-power wireless communication technology, this module enables information exchange between nodes, with all communication energy supplied by the energy storage module. Communication content includes node identity information, energy status (remaining power of the transmitting node, power requirement of the receiving node), location information, and control commands. This provides communication support for multi-node collaborative power supply, conflict avoidance, and network scheduling. The communication process employs an intermittent operating mode to reduce energy consumption.

[0126] This embodiment details a wireless energy transmission system for a robotic arm with a direction neutralization strategy, comprising seven modules: a retractable and foldable magnetically coupled coil, a multi-degree-of-freedom robotic arm, passive sensing, low-power intelligent control, power management, and low-power collaborative communication. The passive sensing module constructs a 3D layout map and identifies obstacles based on magnetic field signals and node feedback. The coil module dynamically adjusts its diameter and area using a drive module and a special design. The robotic arm achieves full-attitude adjustment of the coil through a high-precision servo system, with energy consumed from node energy storage. The passive sensing module obtains node distance and orientation positioning and identifies metallic obstacles. The intelligent control module uses an ultra-low-power MCU as its core, generating instructions based on data from multiple modules to optimize transmission. The power management module converts electrical energy for the transmitting and receiving nodes. The collaborative communication module uses low-power wireless technology to achieve node information exchange, supporting multi-node collaborative power supply and scheduling.

[0127] Example 3, based on Example 1 or 2, describes in detail the experiment of testing the method and system in an industrial workshop, such as... Figure 4As shown, the experimental scenario uses the joint power supply of a 6-axis robotic arm in this workshop. Three wireless power transmission nodes, numbered T1, T2, and T3, are distributed within the workshop. This system is mounted on the end effector of the robotic arm as a receiving node. The system needs to complete a cyclical action of grasping a workpiece, moving it to the processing position, and resetting. Each cycle lasts 120 seconds. The test environment includes metal shelves as obstacles, and the transmission distance fluctuates between 0.3 and 2.5 meters. The specific experimental setup is as follows:

[0128] The AD22151 magnetic field induction chip is used to receive T1-T3 magnetic field signals (frequency 150kHz), and a LoRa low-power collaborative communication module is used to obtain node feedback to construct a 3D layout map. A coil module is constructed using flexible copper foil coils and ABS rigid supports. The drive module is controlled by a stepper motor to achieve 5-30cm diameter extension and 0-90° folding. The robotic arm module uses an MG996R servo motor, and the power is drawn from an 18650 energy storage battery. The control module uses an STM32L476 ultra-low power MCU with built-in K-means clustering and weight allocation algorithms. 600 rounds of comparative experiments were conducted using existing common methods, FWC and TRM.

[0129] Table 1 shows the comparative experimental results.

[0130]

[0131] As shown in Table 1 above, the method and system achieve an average success rate of 99.2% in single-cycle power supply and an average energy transmission efficiency of 88.3%. Furthermore, the energy consumption of the robotic arm adjustment is as low as 4.1%, and the average response time for obstacle avoidance is only 0.6s, all of which are higher than the other two existing methods. The method and system demonstrate outstanding performance in terms of power supply success rate, transmission efficiency, energy consumption, and response.

[0132] according to Figure 5 , 6 It can be seen that the proposed method and system have significant advantages over existing methods in terms of single-cycle power supply success rate and average energy transmission efficiency. Moreover, the probability density distribution is more uniform and stable, and the energy consumption of the robotic arm adjustment is much lower than that of the other two methods, which verifies the breakthrough of the proposed method and system in terms of power supply success rate and transmission efficiency.

[0133] according to Figure 7 It can be seen that the performance of this method and system in terms of obstacle avoidance response time is generally lower than that of the FWC method and the TRM method. Moreover, the distribution of this method and system is more uniform and the stability is stronger, which verifies the breakthrough of this method and system in terms of transmission efficiency, energy consumption and response.

[0134] This embodiment describes in detail the experiment conducted in an industrial workshop to test the method and system. The experiment fully verifies that the method and system can avoid metal shelves in advance through passive sensing, avoid the impact of single-source failures through multi-source directional neutralization strategy, greatly improve transmission efficiency through dynamic coil adjustment and precise alignment of robotic arms, and reduce energy consumption through low-power sensing and differentiated correction, thus achieving significantly leading energy transmission efficiency, operational stability, reliability and energy saving.

[0135] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter changes made to these embodiments within the spirit and principles of the present invention, without departing from the principles and spirit of the present invention, through conventional substitutions or to achieve the same function, fall within the scope of protection of the present invention.

Claims

1. A method for wireless power transfer with a direction neutralizing strategy for a robotic arm, the method comprising: determining a direction of a robotic arm; and adjusting a direction of a wireless power transfer device based on the determined direction of the robotic arm. include: The passive sensing module receives magnetic field signals generated by energy transmission from surrounding nodes, extracts and obtains the three-dimensional coordinates, relative directions, and node energy status information of each energy transmission node. The node energy status information is used to characterize the remaining energy, output capacity, or energy storage status of the corresponding node, and a three-dimensional spatial layout map of the surrounding environment and node distribution is constructed based on the three-dimensional coordinates and relative directions. Based on the three-dimensional spatial layout diagram and node energy status information, the low-power intelligent control module determines the type of power supply scenario and generates adjustment decisions. If it is a single-source power supply scenario, the optimal alignment posture between the magnetic coupling coil and the target power supply node is calculated. In the case of a multi-source power supply scenario, the system divides the area into power supply regions using a low-power clustering algorithm, calculates the geometric center coordinates of the power supply regions, assigns weights based on the remaining energy and output power capabilities of each transmitting node, and determines the telescopic and folding structure parameters of the coil to adapt to the current transmission distance. If it is an obstacle avoidance adjustment scenario, the unobstructed suboptimal coupling path is selected through the three-dimensional spatial layout map, and the coil attitude and telescopic folding structure parameters are determined with the goal of minimizing energy consumption. Based on the adjustment decision, the low-power intelligent control module sends a first control command to the servo drive system of the multi-degree-of-freedom robotic arm and a second control command to the drive module of the magnetic coupling coil to adjust to the target structural state. The power management module converts energy into high-frequency AC power that meets the requirements of magnetic coupling transmission according to instructions and outputs it. At the same time, it collects the status data of transmission efficiency and output voltage and feeds them back to the low-power intelligent control module. The low-power intelligent control module compares the current state data with the target optimal value. If there is a deviation, the low-power node prioritizes energy storage and starts the multi-degree-of-freedom robotic arm to adjust the coil posture according to the preset state. Nodes with sufficient energy promptly correct the coil orientation to ensure transmission efficiency, achieving dynamic correction.

2. The method according to claim 1, characterized in that, The extraction of the three-dimensional coordinates of each energy transmission node specifically involves: using a magnetic field signal intensity positioning algorithm, calculating the relative distance between nodes by measuring the amplitude changes of the magnetic field signal at different times, as shown in the formula: ； in, c This represents the relative distance between the passive sensing module and the energy transmission node. The spatial attenuation coefficient, The magnetic field signal transmission power of the transmitting node. For the transmitting antenna gain, For the receiver antenna gain, The wavelength of the magnetic field signal. The power of the magnetic field signal received by the passive sensing module.

3. The method according to claim 1, characterized in that, The optimal alignment attitude is specifically determined by maximizing the coupling coefficient between the magnetic coupling coils to determine the optimal alignment attitude for the target, as shown in the formula: ； in The coupling coefficient is... For the mutual inductance between the transmitting coil and the receiving coil, For the self-inductance of the transmitting coil, For the self-inductance of the receiving coil; preset the optimal coupling range ( ),when The value reaches ( When the values ​​are between 0 and 1, the coil is determined to have reached the optimal alignment posture.

4. The method according to claim 1, characterized in that, The determination of the telescopic folding structure parameters for coil adaptation to the current transmission distance specifically involves: dividing spatially proximate transmitting nodes into power supply areas using a low-power clustering algorithm; calculating the geometric center coordinates of each power supply area; establishing a coordinate system with the node itself as the origin; solving for the azimuth and elevation angles corresponding to the geometric center as the reference adjustment direction for the coil; and then assigning weights based on the remaining energy and output power capability of each transmitting node, using the following formula: ； in, For the first The weight of each transmitting node, For weighting coefficients, For the first The remaining energy of each transmitting node, For the first Maximum energy storage capacity of each launch node For the first The actual output power of each transmitting node For the first Maximum output power of each transmitting node; Based on the weight allocation results, a preset fine-tuning angle range is defined. The parameters of the telescopic and folding structure of the coil are dynamically calculated to adapt to the current transmission distance, and the fine-tuning angle of the reference adjustment direction is determined. The formula is: ； in, The maximum weighting coefficient among all transmitting nodes. This is the minimum weighting coefficient among all transmitting nodes.

5. The method according to claim 4, characterized in that, The low-power clustering algorithm is specifically implemented using the K-means clustering algorithm, and the iterative update formula for the cluster centers is: ； in, For the first The energy supply area is in the first The cluster center coordinates of the next iteration. For the first The number of transmission nodes within a power supply area For the first In the nth iteration, belonging to the th A set of transmitter nodes in a power supply area For the first The three-dimensional coordinates of each transmitting node.

6. The method according to claim 4, characterized in that, The telescopic and folding structure parameters are specifically defined as follows: the relationship between the telescopic and folding structure parameters of the coil and the transmission distance is matched using a linear fitting model, and the formula is as follows: ； in The minimum diameter of the coil, The maximum diameter of the coil. For the maximum transmission distance, For minimum transmission distance, This represents the actual transmission distance.

7. The method according to claim 1, characterized in that, The minimum energy consumption is specifically calculated by minimizing the sum of the energy consumption of the robotic arm drive and the energy consumption of the coil transmission. The total energy consumption is... The formula is: ； in, Energy consumption for robotic arm drive For the first robotic arm Power of each joint The working time of the first joint. Energy consumption for coil transmission For load power, For transmission time, For energy transfer efficiency, The number of joints in the multi-degree-of-freedom robotic arm.

8. The method according to claim 1, characterized in that, The energy transmission process is specifically as follows: The frequency of the high-frequency alternating current is dynamically adjusted according to the transmission distance, using the following formula: ； in, The frequency of high-frequency alternating current. As the reference frequency, This is the frequency attenuation coefficient. As a reference transmission distance, This represents the actual transmission distance.

9. The method according to claim 1, characterized in that, The dynamic correction process is as follows: after the robotic arm wireless energy transmission node completes coil attitude and structure adjustment and enters the stable energy transmission stage, the low-power intelligent control module actively triggers the data acquisition command to send data acquisition requests to the signal acquisition module and power management and energy storage module of the magnetic coupling coil respectively, and collects transmission status data and node energy status data. The system presets the target optimal value range and the optimal output voltage range for energy transmission. It then performs deviation judgment and node energy status determination on the collected data and compares them with the target optimal value range and the optimal output voltage range. If the optimal range conditions are not met, a dynamic correction process is triggered. Based on the node energy state determination results, the low-power intelligent control module executes a differentiated dynamic correction strategy. For low-power nodes, the principle is to prioritize energy storage and minimize correction energy consumption, performing energy storage assurance and deviation assessment. The formula is as follows: ； in, The deviation coefficient, This represents the average value of the optimal transmission efficiency. The optimal average output voltage. The safe temperature threshold for the coil, , These are the current transmission efficiency, output voltage, and coil temperature, respectively. For low-power nodes where the deviation exceeds the preset deviation threshold, the low-power intelligent control module uses a step-by-step fine-tuning strategy to adjust the robotic arm. With sufficient energy, nodes are designed to quickly eliminate deviations and maintain optimal transmission efficiency. The correction process adopts a real-time response and precise adjustment mode. Combining the three-dimensional spatial layout map with the current transmission parameters, the source of deviation is located through the parameter and attitude mapping model. Based on the real-time position data of the magnetic coupling coil, the target angle of the optimal alignment attitude is calculated. After attitude correction is completed, transmission status data are continuously collected for multiple monitoring cycles. If all parameters are within the target optimal value range, the correction is considered successful and the system enters the normal monitoring mode. If deviations still exist, the source of the deviations is re-analyzed, and the correction strategy is adjusted until the deviations are eliminated.

10. A wireless power transfer system for a robotic arm with a direction neutralization strategy, used to implement the method described in any one of claims 1-9, characterized in that, This includes a retractable and foldable magnetically coupled coil module, a multi-degree-of-freedom robotic arm module, a passive sensing module, a low-power intelligent control module, a power management module, and a low-power collaborative communication module, specifically: The retractable and foldable magnetic coupling coil module is equipped with a low-power drive module and receives instructions from the low-power control module. According to the target node distance and coupling requirements, it changes its own parameters through the extension or folding of the mechanical structure. Through the design of combining flexible conductive materials with rigid support structure, it can achieve dynamic adjustment of diameter and area and a certain degree of shape deformation. The multi-degree-of-freedom robotic arm module is equipped with a high-precision, low-power servo drive system. It achieves full attitude adjustment through a low-power 6-axis robotic arm structure, enabling the coil to rotate, tilt, and translate in three-dimensional space. The adjustment accuracy meets the requirements of magnetic coupling alignment, and its energy consumption is entirely supplied by the node energy storage module. The passive sensing module receives magnetic field signals generated during energy transmission from surrounding nodes and combines them with signal strength detection to obtain node distance information. It works in conjunction with the communication module to receive position feedback commands from surrounding nodes, achieve angle and direction positioning, and output the three-dimensional coordinates of other surrounding nodes. It identifies metallic obstacles in the environment by changing magnetic field signals, providing obstacle avoidance data for coil adjustment. In passive scenarios, it can be driven by the energy received by the coil. The low-power intelligent control module is based on an ultra-low-power MCU. It receives spatial data from the passive sensing module, transmission status data from the coil module, and collaborative information from the communication module. It generates control commands through a built-in low-power algorithm to drive the robotic arm and coil to complete posture and structural adjustments, thereby optimizing the configuration of energy transmission parameters and ensuring maximum energy utilization. The power management module relies on the energy received by the coil to achieve charging and discharging. For the transmitting node, the module converts the electrical energy from the energy storage module or external sources into high-frequency AC power that meets the requirements of magnetic coupling transmission. For the receiving node, the module rectifies the high-frequency electrical energy received by the coil into DC power, and after voltage regulation, it supplies power to the load, control module, and robotic arm components. The low-power collaborative communication module uses ultra-low power wireless communication technology to realize information exchange between nodes. The communication content includes node identity information, energy status, location information and control commands, providing communication support for multi-node collaborative power supply, conflict avoidance and network scheduling. The communication process adopts an intermittent working mode to reduce energy consumption.

Images