Intelligent logistics transportation platform based on regular hexagon modular omnidirectional splicing and control method thereof

Abstract

The invention provides an intelligent logistics transportation platform based on regular hexagon modular omnidirectional splicing and a control method thereof, and relates to the field of intelligent logistics transportation control, and the method comprises the steps: collecting strain voltage data of a piezoelectric sensor array, calculating a stress distribution vector between adjacent transportation units, and generating flexible connection characteristics between the transportation units; establishing a distributed elastic control network based on flexible connection characteristics, calculating an elastic potential energy function of each regular hexagonal transportation unit, and dynamically adjusting the connection rigidity of adjacent transportation units; when a moving obstacle is detected, a virtual repulsive force field is generated based on an elastic potential energy function, and the transportation unit array is driven to generate local deformation; and calculating the deformation restoring force of the transportation unit array based on the local deformation, and controlling the omni-directional driving device of each transportation unit to generate a cooperative driving force matched with the deformation restoring force so as to restore the original structure. The flexible obstacle avoidance function of the transportation unit array is achieved, and the overall stability and task continuity of the logistics transportation system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics transportation control, and more specifically, to an intelligent logistics transportation platform and its control method based on hexagonal modular omnidirectional splicing. Background Technology

[0002] With the rapid growth of modern logistics demands and the in-depth development of intelligent technologies, logistics transportation systems are evolving towards higher efficiency, greater flexibility, and greater intelligence. Traditional logistics transportation methods typically rely on fixed conveyor systems or transportation equipment with a single drive mode, making it difficult to adapt to dynamic and complex transportation environments and diverse logistics tasks. In recent years, modular transportation units have received widespread attention due to their scalability and flexibility. Modular transportation technology combines multiple functional units according to certain rules to form an adaptive transportation system, which not only improves the efficiency of logistics transportation but also meets the needs of complex path planning and adaptation to diverse environments. Among them, modular units based on omnidirectional drive technology, due to their ability to achieve precise multi-directional movement and flexible path adjustment, have become an important development direction for intelligent logistics transportation systems.

[0003] However, several technical bottlenecks remain in the research and application of existing modular transportation systems. First, the physical connections between modular transportation units are typically rigid or simple flexible connections, making it difficult to dynamically adjust the connection stiffness between units. This results in the system struggling to achieve flexible adjustment and efficient motion control when facing complex environments (such as obstacles and external impacts). Second, existing technologies for obstacle avoidance in modular transportation units generally rely on traditional path planning algorithms, lacking the ability to dynamically control the coordinated deformation between transportation units. This often leads to low obstacle avoidance efficiency or system instability. Furthermore, existing modular transportation systems typically cannot quickly restore their original structure after obstacle avoidance, affecting the overall stability and mission continuity of the system. Therefore, how to achieve dynamic connection stiffness adjustment, flexible obstacle avoidance, and structural restoration in modular transportation systems has become a critical issue that urgently needs to be addressed in the field of modular intelligent logistics transportation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention is proposed. This invention provides an intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagonal structures. It can, to a certain extent, solve the problems in modular logistics transportation systems where the connection stiffness between transportation units cannot be dynamically adjusted, obstacle avoidance actions lack flexible coordination, and it is difficult to restore the original array structure after obstacle avoidance.

[0005] According to one aspect of the present invention, an intelligent logistics transportation platform based on hexagonal modular omnidirectional splicing and its control method are provided, comprising:

[0006] Strain voltage data of the piezoelectric sensing array at the edge of the regular hexagonal transport unit is collected, and the stress distribution vector between adjacent transport units is calculated based on the strain voltage data to generate flexible connection features between transport units.

[0007] A distributed elastic control network is established based on the flexible connection characteristics, the elastic potential energy function of each regular hexagonal transport unit is calculated, and the connection stiffness of adjacent transport units is dynamically adjusted based on the elastic potential energy function.

[0008] When the piezoelectric sensing array detects a moving obstacle in the environment, it generates a virtual repulsive field based on the elastic potential energy function, which drives the transport unit array to produce local deformation.

[0009] Based on the local deformation calculation of the deformation recovery force of the transport unit array, the omnidirectional drive device of each transport unit is controlled to generate a cooperative driving force that matches the deformation recovery force, so that the transport unit array can restore its original structure after obstacle avoidance.

[0010] Furthermore, the piezoelectric sensing array includes eight spaced-apart lead zirconate titanate piezoelectric ceramic sensing elements; electrodes are formed on both sides of the elements by conductive silver paste, and the sensing elements are fixed to the edge of the transport unit by a polyimide flexible substrate.

[0011] Furthermore, the establishment of the flexible connection features includes: performing Gaussian filtering noise reduction on the acquired strain-voltage data; converting the noise-reduced strain-voltage data into stress values ​​based on the piezoelectric constant matrix to generate discrete stress sampling points; generating continuous edge stress distribution curves for the discrete stress sampling points using a cubic spline interpolation algorithm; calculating a stress distribution vector based on the stress distribution curves, wherein the stress distribution vector includes both stress magnitude and direction; and generating a flexible connection feature vector from the stress distribution vector of each edge, wherein the flexible connection feature vector includes strength parameters, direction parameters, and dynamic feature parameters, forming an 18-dimensional vector structure.

[0012] Furthermore, based on the flexible connection feature vector, local elastic control units are established for each of the six edges of the regular hexagonal transport unit;

[0013] The local elastic control unit includes an elastic deformation monitoring module and a stiffness adjustment execution module;

[0014] The elastic deformation monitoring module receives the flexible connection feature vector of the corresponding edge and monitors the elastic deformation state of the edge in real time; the stiffness adjustment execution module includes a magnetorheological fluid driving device, which adjusts the viscosity of the magnetorheological fluid by changing the magnetic field strength to achieve dynamic adjustment of the connection stiffness.

[0015] Furthermore, network nodes are formed based on the local elastic control units, and a distributed elastic control network is constructed according to the physical connection relationship of the transportation units.

[0016] Furthermore, a local elastic potential energy matrix is ​​constructed based on the distributed elastic control network, the flexible connection feature vector is extracted and normalized to generate weight coefficients, the matrix is ​​symmetricized and summed to generate an elastic potential energy function that characterizes the overall elastic potential energy of the transportation unit.

[0017] Furthermore, a virtual repulsive field is generated based on the elastic potential energy function, and a potential energy function is established with the center of the obstacle as the pole. , is represented as:

[0018]

[0019] Among them, Let be the distance from a point in space to the center of the obstacle matter. It is the azimuth angle. The fundamental potential field strength coefficient, The parameter represents the range of influence of the potential field. For directional modulation coefficients, The direction angle of the obstacle's velocity.

[0020] Furthermore, based on the aforementioned potential energy function and the elastic potential energy of the transport unit... The combined potential energy function is obtained by superposition. The formula is expressed as:

[0021]

[0022]

[0023] in, For adaptive weighting function, This is the critical distance parameter. For smooth transition parameters.

[0024] Furthermore, a structural deformation energy function is constructed based on the pose deviation of the transport unit, expressed by the formula:

[0025]

[0026] in, The total number of transport units. This is the weighting coefficient for the positional deviation. For the first Each transport unit Positional deviation of direction For the first Each transport unit Positional deviation of direction For the first angular deviation of each transport unit This is a weighting coefficient for attitude deviation, used to balance the contribution of angle deviation. Adjacent units and The coupling coefficient between them For the first The pose deviation matrix of each transport unit For the first The pose deviation matrix of each transport unit.

[0027] Furthermore, the deformation restoring force is determined based on the negative gradient of the structural deformation energy function, and the total driving force is determined by considering the dynamic characteristics of the transport unit and combining inertial force, Coriolis force and gravity compensation.

[0028] Based on the total driving force, the pseudo-inverse operation of the Jacobian matrix is ​​used to distribute the total driving force to each driving wheel, as expressed by the formula:

[0029]

[0030] in, For the first The output force of each drive wheel For the first Jacobian matrix of each driving wheel It is the pseudo-inverse of the Jacobian matrix.

[0031] Compared with existing technologies, this invention generates flexible connection features by collecting piezoelectric sensor array data from the edges of regular hexagonal transport units and dynamically adjusts the connection stiffness between transport units based on a distributed elastic control network, significantly improving the system's adaptability in complex environments. By generating a virtual repulsive field based on the elastic potential energy function, it achieves flexible obstacle avoidance of the transport unit array, which is more efficient and smoother than traditional path planning methods. In addition, by calculating the deformation recovery force and controlling the cooperative driving force, the transport unit array can quickly restore its original structure after obstacle avoidance, significantly improving the overall stability and task continuity of the logistics transportation system and effectively solving the technical problems of dynamic adjustment, flexible obstacle avoidance, and deformation recovery in existing modular transportation systems. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0033] Figure 1This is a system block diagram of an intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to an embodiment of the present invention. Detailed Implementation

[0034] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0035] Figure 1 This is a system block diagram of an intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to an embodiment of the present invention. Figure 1 As shown, an intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons include:

[0036] S1: Collect strain voltage data of the piezoelectric sensing array at the edge of the regular hexagonal transport unit, and calculate the stress distribution vector between adjacent transport units based on the strain voltage data to generate flexible connection features between transport units.

[0037] Each edge of the hexagonal transport unit is equipped with six piezoelectric sensor arrays. Each piezoelectric sensor array contains eight spaced-apart piezoelectric sensing elements made of lead zirconate titanate piezoelectric ceramic material. Each piezoelectric sensing element measures 8mm × 8mm × 0.5mm, and electrodes are formed on both sides of the piezoelectric ceramic using conductive silver paste. The piezoelectric sensing elements are fixed to the edge of the transport unit by a flexible substrate made of polyimide material with a thickness of 0.1mm.

[0038] When the regular hexagonal transport unit moves and stress is generated between adjacent units, the piezoelectric sensing element is deformed by the stress and generates induced charge on the electrode surface under the piezoelectric effect, thereby generating strain voltage between the electrodes; the strain voltage is sampled by the signal conditioning circuit with a sampling frequency of 1000Hz and a sampling accuracy of 16 bits.

[0039] The stress distribution vector between adjacent transport units is calculated based on strain voltage data, specifically as follows:

[0040] The acquired strain voltage data were subjected to Gaussian filtering for noise reduction, with the filter window size set to 5. Based on the piezoelectric constant matrix of the piezoelectric sensing element, the filtered strain voltage data were converted into stress values, resulting in 48 discrete stress sampling points. These discrete stress sampling points were then fitted using a cubic spline interpolation algorithm to generate a continuous edge stress distribution curve. The stress distribution vector was calculated based on the continuous stress distribution curve, where the stress distribution vector contains two components: stress magnitude and stress direction.

[0041] The flexible connection features between transport units are generated based on the stress distribution vector between adjacent transport units. Specifically:

[0042] The magnitude and orientation angle of the edge stress distribution vector are calculated. The magnitude is used as the strength parameter of the flexible connection, and the orientation angle is used as the orientation parameter of the flexible connection. At the same time, the rate of change of the stress distribution vector with time is calculated as the dynamic characteristic parameter of the flexible connection. The strength parameter, orientation parameter and dynamic characteristic parameter of the flexible connection are combined to form the characteristic vector of the flexible connection, which is used to establish a distributed elastic control network.

[0043] The specific process of combining the flexible connection strength parameters, orientation parameters, and dynamic characteristic parameters to form the flexible connection feature vector is as follows:

[0044] The stress distribution vector detected at each edge is represented as a vector in two-dimensional space. The magnitude of the vector is normalized based on the stress value generated by the self-weight of the regular hexagonal transport unit, and the direction angle ranges from 0 to 360 degrees. When the magnitude of the detected stress distribution vector is greater than 80% of the maximum load-bearing stress of the transport unit, the flexible connection strength parameter in that direction is set to 1, and otherwise it is set to 0, forming a 6-dimensional strength parameter vector.

[0045] For the orientation parameters, the orientation angle values ​​of the stress distribution vectors of the six edges are directly used to construct a 6-dimensional orientation parameter vector. When calculating the dynamic characteristic parameters, if the rate of change of the stress distribution vector in two adjacent sampling periods exceeds half of the maximum allowable rate of change of stress of the transportation unit, the dynamic characteristic parameter of the corresponding edge is set to 1, otherwise it is set to 0, thus obtaining a 6-dimensional dynamic characteristic parameter vector.

[0046] Finally, the three 6-dimensional parameter vectors are concatenated in sequence to form an 18-dimensional flexible connection feature vector. The first 6 dimensions represent the connection strength state of the six edges, the middle 6 dimensions represent the stress direction distribution, and the last 6 dimensions represent the dynamic characteristics of stress changes. Once this feature vector is generated, it is used as the input parameter of the distributed elastic control network.

[0047] S2: Establish a distributed elastic control network based on the flexible connection characteristics, calculate the elastic potential energy function of each regular hexagonal transport unit, and dynamically adjust the connection stiffness of adjacent transport units based on the elastic potential energy function.

[0048] Based on the acquired flexible connection feature vector, a local elastic control unit is established for each of the six edges of each regular hexagonal transport unit. Each local elastic control unit includes an elastic deformation monitoring module and a stiffness adjustment execution module. The elastic deformation monitoring module receives the flexible connection feature vector of the corresponding edge, and the stiffness adjustment execution module adopts a magnetorheological fluid driving device to dynamically adjust the connection stiffness by changing the magnetic field strength to adjust the viscosity coefficient of the magnetorheological fluid.

[0049] The specific process of establishing local elastic control units for the six edges of each regular hexagonal transport unit is as follows: An elastic control module with a built-in microcontroller is set up at each edge. The microcontroller adopts a dual-core architecture, with one core dedicated to elastic deformation monitoring and the other core responsible for stiffness adjustment control. When the elastic deformation monitoring core receives data from the piezoelectric sensor array, it performs real-time data analysis through an on-chip DSP processor and stores the analysis results in a cache. If the detected elastic deformation exceeds the preset range, an interrupt signal is triggered, activating the stiffness adjustment core. The stiffness adjustment core is connected to the magnetorheological fluid drive circuit via an SPI bus, where the magnetorheological fluid drive circuit includes an H-bridge power... The system includes an amplifier and Hall sensor feedback circuit. When the connection stiffness needs to be changed, the microcontroller outputs a PWM signal to control the H-bridge circuit to adjust the excitation current of the magnetic field coil. At the same time, the Hall sensor monitors the magnetic field strength in real time, forming a closed-loop control. Each edge's magnetorheological fluid cavity is filled with high-performance magnetorheological fluid. The initial viscosity of the magnetorheological fluid is 0.3 Pa·s, and the viscosity can increase to a thousand times under the action of an external magnetic field. When adjacent transport units move relative to each other, the magnetorheological fluid generates a viscosity-related damping force under shear action, thereby realizing the dynamic adjustment of the edge connection stiffness. The six edge elastic control units communicate with each other through a CAN bus to ensure the coordination of stiffness adjustment.

[0050] Furthermore, network nodes are formed based on the established local elastic control units, and a distributed elastic control network is constructed according to the physical connection relationship of the transportation units. Each local elastic control unit serves as a network node, and the connection relationship between adjacent transportation units serves as a network edge. The weight coefficient of the network edge is determined by the flexible connection feature vector of the corresponding edge. When the strength parameter in the flexible connection feature vector is 1, the weight coefficient of the corresponding network edge increases, and vice versa. The topology of the network is dynamically updated according to the connection status of the transportation units.

[0051] The construction of the distributed resilient control network includes: treating each hexagonal transport unit as a node in the network, with each node containing a unique identification code and six edge ports. The identification code is generated through an RFID tag built into the unit. When the edges of adjacent transport units are physically connected, a communication link is established through the near-field communication module in the edge port, and identification codes are exchanged. If two transport units successfully establish a communication link, a directed edge is generated in the distributed network, with the direction of the directed edge pointing from the node with the smaller identification code to the node with the larger identification code. Each directed edge in the network has three attribute parameters: connection strength value, stress direction value, and dynamic characteristic value. These attribute parameters are updated in real time based on the flexible connection feature vectors of the corresponding edges. When any node in the network detects a change in its own flexible connection feature, it will broadcast the change information to all nodes in its second-order neighborhood through a distributed consensus algorithm, so that the relevant nodes can update the network topology information synchronously. If the connection between a certain transport unit and its neighboring unit is broken, the corresponding network node will trigger the topology reconstruction mechanism, delete the directed edge corresponding to the broken connection, and notify the relevant nodes to update their adjacency matrix. The entire network ensures the timing consistency of each node through a distributed clock synchronization protocol, ensuring the real-time performance and consistency of the network topology information.

[0052] Furthermore, based on the network topology, the elastic potential energy function of each regular hexagonal transport unit is calculated, including:

[0053] The weight coefficients of the network edges connected to each transport unit are extracted to construct a local elastic potential energy matrix. The diagonal elements of the local elastic potential energy matrix represent the elastic potential energy of the transport unit itself, and the off-diagonal elements represent the interaction potential energy with adjacent transport units. When a transport unit deforms under external load, the elastic deformation energy is calculated based on Hooke's law and superimposed on the local elastic potential energy matrix. At the same time, the coupling effect between transport units is considered, and the elastic potential energy function is corrected by the second-order neighborhood influence factor.

[0054] Based on the current topological connection status of the transport units, a 6×6 local elastic potential energy matrix is ​​constructed. When a physical connection is established between an edge and an adjacent transport unit, the flexible connection feature vector of that edge is extracted, and the strength parameter, direction parameter, and dynamic feature parameter in the feature vector are mapped to the [0,1] interval to form normalized weight coefficients. If the connection edge of the two transport units experiences relative displacement, the elastic deformation energy is calculated based on the relative displacement measured by the displacement sensor and the current connection stiffness. At the same time, the strain rate information of that edge is multiplied by the weight coefficients to obtain the coupling factor. Simultaneously, a local elastic potential energy matrix is ​​constructed. When calculating the elastic potential energy, the elastic deformation energy of each edge is filled into the diagonal position of the matrix, and the off-diagonal elements are calculated according to the coupling factor, where the off-diagonal elements represent the potential energy coupling effect between adjacent edges. If a transportation unit has an indirect connection with other units in its second-order neighborhood, the influence path is determined by the network path search algorithm, and an attenuation coefficient is introduced to correct the potential energy contribution of the second-order neighborhood. When calculating the total elastic potential energy, the local elastic potential energy matrix is ​​first symmetricized, and all elements of the matrix are summed and multiplied by the unit characteristic scale coefficient to obtain a scalar function representing the overall elastic potential energy level of the transportation unit.

[0055] Specifically, the modification of the potential energy contribution of the second-order neighborhood by introducing a decay coefficient includes:

[0056] The breadth-first search algorithm based on graph theory identifies the second-order neighborhood range of the target transportation unit. When a first-order neighborhood node is found, the flexible connection feature of the direct connection edge between the node and the target unit is recorded. If a second-order neighborhood node is found, all possible paths from the node to the target unit are recorded at the same time, and each path contains a sequence of two connection edges.

[0057] When calculating the potential energy contribution of a second-order neighbor node to the target cell, the transfer coefficient of each connecting edge on the path is first calculated. This coefficient is proportional to the flexible connection strength parameter of the connecting edge and inversely proportional to the dynamic characteristic parameter of the connecting edge. If the angle between two connecting edges contained in a path is less than 60 degrees, an angle compensation factor is introduced to enhance the potential energy transfer effect. When the angle is greater than 120 degrees, an angle attenuation factor is introduced to weaken the potential energy transfer. For each possible path, the transfer coefficients of each connecting edge on the path are multiplied together to obtain the path attenuation coefficient. When there are multiple paths for the same second-order neighbor node, the path with the largest attenuation coefficient is selected as the main potential energy transfer channel. If the dynamic characteristic parameter of any connecting edge on a path exceeds the threshold, it indicates that the path is unstable. In this case, the attenuation coefficient of the entire path is set to zero to cut off the potential energy transfer.

[0058] Finally, the path attenuation coefficient is multiplied by the elastic potential energy of the second-order neighboring node to obtain the potential energy contribution of the node to the target unit, and the potential energy contributions of all second-order neighboring nodes are superimposed into the total elastic potential energy of the target unit.

[0059] Furthermore, based on the elastic potential energy function, the connection stiffness is dynamically adjusted. Specifically,

[0060] The adjustment target value of the current connection stiffness is calculated based on the elastic potential energy function of each edge. If the elastic potential energy exceeds 60% of the stable operation threshold of the transport unit, the control system enters the early warning state. At this time, the sampling frequency of the magnetorheological fluid drive circuit is increased from the original 200Hz to 400Hz, and the predictive control module is started to calculate the potential energy change trend.

[0061] If the elastic potential energy continues to increase and exceeds 80% of the stable operating threshold, a rapid stiffness adjustment response is triggered. At this time, the microcontroller calculates the optimal stiffness adjustment amount based on the gradient information of the elastic potential energy function, and smoothly converts the adjustment amount into the incremental value of the magnetic field strength at a rate of 5% per millisecond.

[0062] If it is necessary to increase the connection stiffness, the controller adjusts the duty cycle of the H-bridge circuit through a PWM signal with a 20kHz carrier frequency, so that the magnetic field coil generates a magnetic field strength that is proportional to the elastic potential energy gradient. The proportionality coefficient is determined according to the hysteresis characteristic curve of the magnetorheological fluid, so that the yield stress of the magnetorheological fluid can be continuously and controllably changed with the magnetic field strength.

[0063] If it is necessary to reduce the connection stiffness, a segmented strategy of reducing the magnetic field strength is adopted. Each reduction cycle lasts for 5ms, during which the magnetorheological fluid is kept in a quasi-steady state flow to ensure the smoothness of the stress release process. Throughout the stiffness adjustment process, the Hall sensor continuously monitors the actual change of the magnetic field strength at a frequency of 1kHz and sends the feedback signal to the adaptive PID controller for closed-loop control. The proportional coefficient is adjusted in the range of 0.5-2 according to the rate of change of elastic potential energy, the integral time varies with the absolute value of the rate of change of potential energy in the range of 50ms-200ms, and the derivative time is fixed at 10ms.

[0064] If the elastic potential energy of adjacent transport units decreases to below 40% of the stable operating threshold and lasts for more than 100ms, the connection stiffness will remain at the current value, and the system will resume the normal sampling frequency of 200Hz until the next stiffness adjustment response is triggered.

[0065] Throughout the adjustment process, the controllers of adjacent transport units exchange stiffness adjustment status information via the CAN bus at a frequency of 50Hz to ensure the coordination of the adjustment process.

[0066] S3: When the piezoelectric sensing array detects a moving obstacle in the environment, it generates a virtual repulsive field based on the elastic potential energy function, driving the transport unit array to produce local deformation.

[0067] Obstacle feature extraction is performed based on the strain-voltage data acquired from the piezoelectric sensor array. Let the...

[0068] A piezoelectric sensing element at time The strain voltage is Then the obstacle feature vector It can be represented as:

[0069]

[0070] in, This represents the spatial weighting coefficient of the sensing element. This is the position vector of the sensing element relative to the center of the transport unit.

[0071] When || When the threshold is exceeded, the real-time feature analysis module is activated. Based on the spatial distribution pattern of the strain voltage, the feature analysis module identifies the approach direction and relative velocity of the obstacle, and simultaneously calculates the obstacle's approach velocity vector, expressed by the formula:

[0072]

[0073] in, For speed mapping coefficients, It is a non-linear adjustment index.

[0074] Furthermore, when generating the virtual repulsive field based on the acquired elastic potential energy function, an improved potential field method is used to construct the obstacle avoidance potential energy function, establishing the potential energy function with the obstacle's material center as the pole. , is represented as:

[0075]

[0076] Among them, Let be the distance from a point in space to the center of the obstacle matter. It is the azimuth angle. The fundamental potential field strength coefficient, The parameter represents the range of influence of the potential field. For directional modulation coefficients, The direction angle of the obstacle's velocity.

[0077] The potential field and the elastic potential energy of the transport unit The combined potential energy function is obtained by superposition. The formula is expressed as:

[0078]

[0079]

[0080] in, For adaptive weighting function, This is the critical distance parameter. For smooth transition parameters.

[0081] The virtual repulsive field is generated by the negative gradient of the comprehensive potential energy function, and its direction of action always points in the direction of decreasing potential energy. Therefore, the virtual repulsive field generated based on the comprehensive potential energy function can be expressed by the following formula:

[0082]

[0083] When a virtual repulsive field is applied to a transport unit, the desired displacement of the unit... satisfy:

[0084]

[0085] Among them, The displacement response coefficient, To control the cycle, This is the force field saturation parameter.

[0086] Simultaneously, considering the connection constraints between adjacent elements, the actual displacement Must meet:

[0087]

[0088] in, This represents the maximum permissible displacement.

[0089] Furthermore, the connection stiffness between adjacent units Dynamic adjustment follows:

[0090]

[0091] in, This is the stiffness attenuation coefficient. This is the stiffness gain coefficient. This is the critical value for displacement.

[0092] Based on the above constraints, to ensure that the transport unit array generates sufficient local deformation to avoid obstacles, specifically:

[0093] When the actual displacement caused by the virtual repulsive field is observed If the minimum distance between the obstacle and the transport unit array exceeds 80% of a preset safety threshold, and this state persists for 100 consecutive sampling periods, then the displacement response coefficient will be... The value gradually decreases from the initial value according to the exponential decay law (decay coefficient 0.15 / s). The attenuation rate is linearly related to the moving speed of the obstacle. For every 1 m / s increase in the moving speed of the obstacle, the attenuation coefficient increases by 0.05 / s.

[0094] When the virtual repulsive field strength is detected to have decreased to 20% of its initial value via the connection stiffness equation, the system enters a position lock state. At this time, the controller records the relative position vector between each pair of adjacent transport units, with a position data accuracy of 0.1 mm. Simultaneously, the attenuation coefficient in the connection stiffness equation is... When the standard value is reduced to 20% of the standard value, the gain coefficient Increasing the connection stiffness from the initial value to twice the initial value... The system stabilizes at three and a half times the initial stiffness. If a sudden stress change (stress change rate exceeding a standard threshold) is detected by a local sensor during the locking period, the system will... The value is temporarily increased to 2.5 times the initial value, and then restored after 50ms to quickly suppress local vibration.

[0095] When the calculated desired displacement equation shows that the minimum distance between the obstacle and the transport unit array exceeds 1.5 times the safety threshold, the system initiates a 3-second observation period. During this period, if 1500 consecutive displacement calculations do not show any approaching trend of the obstacle, a deformation recovery procedure is triggered. This recovery procedure first releases the position lock by adjusting the parameters of the connection stiffness equation, and then designs a progressive recovery path using the stored initial configuration data and the desired displacement equation. Specifically, this is achieved by reversing the virtual repulsive field and dynamically adjusting the displacement response coefficient. This allows the transport unit array to smoothly return to its original position before obstacle avoidance.

[0096] Simultaneously, the potential energy density distribution is calculated in real time throughout the obstacle avoidance process. :

[0097]

[0098] when When the spatial root mean square value is less than the preset threshold and the duration exceeds the safe waiting period, the system will release the obstacle avoidance state and the transport unit array will resume normal operation mode.

[0099] S4: Based on the local deformation, calculate the deformation recovery force of the transport unit array, control the omnidirectional drive device of each transport unit to generate a cooperative driving force that matches the deformation recovery force, so that the transport unit array can restore its original structure after obstacle avoidance.

[0100] By integrating the elastic potential energy, dissipated power, and comprehensive potential energy function over time, the total structural deformation energy is obtained, and then the deformation restoring force of the transport unit array is calculated. Specifically:

[0101] Setting the first The pose deviation matrix of each transport unit is , is represented as:

[0102]

[0103] in, For the first Each transport unit Positional deviation of direction For the first Each transport unit Positional deviation of direction For the first Angular deviation of each transport unit.

[0104] Based on the acquired pose deviation data, the deformation degree of the entire system is quantified, and then a structural deformation energy function is constructed. This function comprises two parts: the deformation energy of the element itself and the coupling energy between adjacent elements, expressed by the formula:

[0105]

[0106] in, The total number of transport units. This is the weighting coefficient for the positional deviation. This is a weighting coefficient for attitude deviation, used to balance the contribution of angle deviation. Adjacent units and The coupling coefficient between them.

[0107] Since a restoring force is required to return the structure to its target configuration when it deforms, according to the principle of energy minimization, the restoring force should point in the direction of the fastest energy decrease. Therefore, the restoring force is determined by the negative gradient of the structural deformation energy, expressed by the following formula:

[0108]

[0109] in, This represents the gradient of structural deformation energy.

[0110] Meanwhile, to avoid violent oscillations during the recovery process, a velocity-dependent damping force is introduced. The original restoring force is combined with the dynamic damping term to obtain the modified deformation restoring force, expressed by the formula:

[0111]

[0112] in, The damping coefficient is time-varying. The initial damping coefficient, For energy scaling parameters, This is the minimum damping coefficient.

[0113] The corrected restoring force needs to be achieved through an omnidirectional drive device. Considering the dynamic characteristics of the transport unit and combining inertial force, Coriolis force, and gravity compensation, its driving force vector must satisfy:

[0114]

[0115] in, The inertia matrix, The Coriolis force matrix, For gravity compensation, This is the velocity vector of the transport unit.

[0116] The total driving force is distributed to each driving wheel, and the optimal distribution of driving force is achieved through the pseudo-inverse operation of the Jacobian matrix. The formula is expressed as:

[0117]

[0118] in, For the first The output force of each drive wheel For the first Jacobian matrix of each driving wheel It is the pseudo-inverse of the Jacobian matrix.

[0119] Meanwhile, to ensure coordinated movement of adjacent transport units and avoid local stress concentration, the driving forces between adjacent units must satisfy synchronization constraints, expressed as:

[0120]

[0121] in, For dynamic synchronization tolerance, To the maximum permissible synchronization tolerance, The synchronization time constant controls the tolerance adjustment rate.

[0122] The convergence criterion for the recovery process is determined based on a combination of structural deformation energy and motion state, and is expressed as:

[0123]

[0124] in, For convergence index, The maximum allowable structural deformation energy, The weighting coefficient for the velocity term. To the maximum permissible recovery speed, The convergence threshold is the criterion for determining whether recovery is complete.

[0125] In summary, the intelligent logistics transportation platform based on hexagonal modular omnidirectional splicing and its control method, as described in this invention, are elucidated. By collecting piezoelectric sensor array data from the edges of the hexagonal transportation units, flexible connection features are generated, and the dynamic adjustment of the connection stiffness between transportation units is achieved based on a distributed elastic control network, significantly improving the system's adaptability in complex environments. By generating a virtual repulsive field based on the elastic potential energy function, the flexible obstacle avoidance function of the transportation unit array is realized, which is more efficient and smoother than traditional path planning methods. Furthermore, through the calculation of deformation recovery force and the control of cooperative driving force, the transportation unit array can quickly restore its original structure after obstacle avoidance, significantly improving the overall stability and task continuity of the system, and effectively solving the technical bottlenecks of existing modular transportation systems in terms of dynamic adjustment, flexible obstacle avoidance, and deformation recovery.

Claims

1. An intelligent logistics transportation platform based on modular omnidirectional splicing of regular hexagons and its control method, characterized in that, include: Strain voltage data of the piezoelectric sensing array at the edge of the regular hexagonal transport unit is collected, and the stress distribution vector between adjacent transport units is calculated based on the strain voltage data to generate flexible connection features between transport units. A distributed elastic control network is established based on the flexible connection characteristics, the elastic potential energy function of each regular hexagonal transport unit is calculated, and the connection stiffness of adjacent transport units is dynamically adjusted based on the elastic potential energy function. When the piezoelectric sensing array detects a moving obstacle in the environment, it generates a virtual repulsive field based on the elastic potential energy function, which drives the transport unit array to produce local deformation. Based on the local deformation calculation of the deformation recovery force of the transport unit array, the omnidirectional drive device of each transport unit is controlled to generate a cooperative driving force that matches the deformation recovery force, so that the transport unit array can restore its original structure after obstacle avoidance.

2. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 1, characterized in that, The piezoelectric sensing array comprises eight spaced-apart lead zirconate titanate piezoelectric ceramic sensing elements; electrodes are formed on both sides of the elements by conductive silver paste, and the sensing elements are fixed to the edge of the transport unit by a polyimide flexible substrate.

3. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 2, characterized in that, The establishment of the flexible connection feature includes: The acquired strain-voltage data is subjected to Gaussian filtering for noise reduction. Based on the piezoelectric constant matrix, the noise-reduced strain-voltage data is converted into stress values ​​to generate discrete stress sampling points. A cubic spline interpolation algorithm is used to generate continuous edge stress distribution curves for the discrete stress sampling points. The stress distribution vector is calculated based on the stress distribution curves, where the stress distribution vector contains both the stress magnitude and direction. The stress distribution vector of each edge is used to generate a flexible connection feature vector, which includes strength parameters, direction parameters, and dynamic feature parameters, forming an 18-dimensional vector structure.

4. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 3, characterized in that, Based on the flexible connection feature vector, a local elastic control unit is established for each of the six edges of the regular hexagonal transport unit; The local elastic control unit includes an elastic deformation monitoring module and a stiffness adjustment execution module; The elastic deformation monitoring module receives the flexible connection feature vector of the corresponding edge and monitors the elastic deformation state of the edge in real time; the stiffness adjustment execution module includes a magnetorheological fluid driving device, which adjusts the viscosity of the magnetorheological fluid by changing the magnetic field strength to achieve dynamic adjustment of the connection stiffness.

5. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 4, characterized in that, Network nodes are formed based on the local elastic control units, and a distributed elastic control network is constructed according to the physical connection relationship of the transportation units.

6. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 3, characterized in that, Based on the distributed elastic control network, a local elastic potential energy matrix is ​​constructed. The feature vectors of flexible connection are extracted and normalized to generate weight coefficients. The matrix is ​​then symmetricized and summed to generate an elastic potential energy function that characterizes the overall elastic potential energy of the transportation unit.

7. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 6, characterized in that, A virtual repulsive field is generated based on the aforementioned elastic potential energy function, and a potential energy function is established with the center of the obstacle as the pole. , is represented as: Among them, Let be the distance from a point in space to the center of the obstacle matter. It is the azimuth angle. The fundamental potential field strength coefficient, The parameter represents the range of influence of the potential field. For directional modulation coefficients, The direction angle of the obstacle's velocity.

8. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 7, characterized in that, Based on the aforementioned potential energy function and the elastic potential energy of the transport unit The combined potential energy function is obtained by superposition. The formula is expressed as: in, For adaptive weighting function, This is the critical distance parameter. For smooth transition parameters.

9. The intelligent logistics transportation platform and its control method based on modular omnidirectional splicing of regular hexagons according to claim 8, characterized in that, The structural deformation energy function is constructed based on the pose deviation of the transport unit, and the formula is expressed as: in, The total number of transport units. This is the weighting coefficient for the positional deviation. For the first Each transport unit Positional deviation of direction For the first Each transport unit Positional deviation of direction For the first angular deviation of each transport unit This is a weighting coefficient for attitude deviation, used to balance the contribution of angle deviation. Adjacent units and The coupling coefficient between them For the first The pose deviation matrix of each transport unit For the first The pose deviation matrix of each transport unit.

10. The intelligent logistics transportation platform and its control method based on hexagonal modular omnidirectional splicing according to claim 9, characterized in that, The deformation restoring force is determined based on the negative gradient of the structural deformation energy function, and the total driving force is determined by considering the dynamic characteristics of the transport unit and combining inertial force, Coriolis force and gravity compensation. Based on the total driving force, the pseudo-inverse operation of the Jacobian matrix is ​​used to distribute the total driving force to each driving wheel, as expressed by the formula: in, For the first The output force of each drive wheel For the first Jacobian matrix of each driving wheel It is the pseudo-inverse of the Jacobian matrix.

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