Older multi-terrain obstacle control and stress early warning collaborative control method and system

Abstract

The application provides an old-age-adaptive multi-terrain obstacle-crossing operation and pressure early-warning cooperative control method and system, which is applied to an old-age-adaptive mobile auxiliary tool and comprises the following steps: scanning a front threshold surface profile, extracting a threshold height after filtering and removing noise interference; identifying an initial crawler front inclination angle according to the threshold height and the crawler geometric size, screening the initial front inclination angle within an executable range through safety boundary evaluation, and determining a feasible front inclination angle initial setting through angle convergence processing; performing risk assessment analysis on a current skidding risk level according to an average contact pressure and a friction warning threshold, and extracting a skidding displacement and a skidding direction from a pressure sensing pad as correction bases for dynamic adjustment of the front inclination angle; and according to the skidding risk level, the skidding displacement, the skidding direction and the ground friction level, adjusting an upper limit of the front inclination angle through dynamic limiting processing combined with the feasible front inclination angle initial setting to obtain an optimized front inclination angle upper limit constraint.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method and system for age-friendly multi-terrain obstacle crossing control and pressure warning collaborative control. Background Technology

[0002] Assistive devices for the elderly need to cope with various complex terrains in homes and public places, especially obstacles such as thresholds and steps. The ability to overcome obstacles directly affects the independent travel safety and quality of life of elderly users, making research in this area crucial. Current obstacle-crossing control methods often increase climbing height by adjusting a fixed or preset forward tilt angle. However, these methods ignore the impact of changes in ground material on friction, leading to insufficient adaptability in different environments and a risk of posture instability or obstacle-crossing failure. Currently, when assistive devices cross higher thresholds, they typically need to increase the forward tilt angle of the tracks to provide sufficient climbing force. However, with an increased tilt angle, the contact area between the front of the tracks and the ground decreases significantly, making friction extremely sensitive to changes in the tilt angle. On smooth surfaces such as tiles or marble, the reduced contact area directly leads to a rapid decrease in available friction, making the front end prone to slippage. Even with sufficient climbing force, slippage can prevent successful advancement, causing interruptions in the obstacle-crossing process or violent swaying of the assistive device. This contradiction is further manifested in practical use cases. For example, when an elderly user pushes an assistive device towards a high threshold, if the controller raises the forward tilt angle to a large angle to ensure the device's ability to overcome obstacles, it may be successful on a dry wooden floor. However, on a tiled floor within the same residence, the change in the front contact pressure distribution causes insufficient friction, and the front of the track begins to slide in place. The assistive device's overall forward movement is hindered, and the user's body sways, increasing the risk of tipping over. Therefore, a close constraint is formed between the forward tilt angle and the success rate of obstacle crossing. While an excessively large forward tilt angle is beneficial for climbing higher obstacles, it amplifies the risk of slippage due to differences in ground friction. Therefore, achieving a dynamic balance between the need for higher thresholds and preventing slippage on smooth surfaces has become a key issue in improving the safety and reliability of age-friendly multi-terrain obstacle crossing control. Summary of the Invention

[0003] This invention provides an age-friendly multi-terrain obstacle crossing control and pressure warning collaborative control method, applied to age-friendly mobility aids, mainly including:

[0004] The system scans the surface contour of the front sill, removes noise, and extracts the sill height. Based on the sill height and track geometry, it identifies the initial track tilt angle. A safety boundary assessment filters for initial tilt angles within the executable range, and angle convergence processing determines a feasible initial tilt angle setting. The system uses track pressure sensors to collect real-time pressure distribution in the contact area between the front track and the ground, extracts average contact pressure using pressure aggregation processing, and obtains the ground friction level by identifying the current ground material type. A risk assessment is performed based on the average contact pressure and friction warning threshold to analyze the current slippage risk level. Slippage displacement and direction are extracted from the pressure sensors as the basis for dynamic tilt angle adjustment. Based on the slippage risk level, slippage displacement and direction, and ground friction level, combined with the feasible initial tilt angle setting, dynamic limiting processing is used to adjust the upper limit of the tilt angle, resulting in an optimized upper limit constraint. This upper limit constraint is applied to the assistive device chassis control system to correct track posture in real-time, and coordinated with pressure warning signals triggered when the average contact pressure exceeds the elderly riding comfort threshold for coordinated control.

[0005] Furthermore, the step of scanning the surface contour of the front sill and extracting the sill height after noise reduction includes:

[0006] The threshold area is scanned to obtain the distance and reflection intensity values ​​of each sampling point on the threshold surface. The boundary line between the threshold and the ground is identified based on the location of abrupt changes in reflection intensity values. The spatial coordinates of the threshold's starting boundary are extracted using the Canny edge detection operator. The threshold scanning area is determined based on the spatial coordinates. Median filtering is applied to the distance values ​​within the scanning area to remove impulse noise, resulting in preliminary smooth contour data. Gaussian filtering is then applied to obtain the denoised threshold contour point cloud data. The maximum vertical coordinate value is extracted, and the difference between this maximum value and the ground reference height is calculated as the threshold height value.

[0007] Furthermore, the step of identifying the initial track tilt angle based on the threshold height and track geometry, filtering initial tilt angles within the executable range through safety boundary assessment, and determining feasible initial tilt angle settings using angle convergence processing includes:

[0008] Based on the threshold height and total track length, the required forward tilt angle to raise the front end of the track to the top of the threshold is calculated using the arctangent function. The vertical projection point of the center of gravity on the track support plane is determined based on the track span width and the overall center of gravity height of the auxiliary equipment. The overturning moment is calculated using the horizontal distance between the vertical projection point and the rear support point of the track. The current forward tilt angle is determined to be within the executable range based on the overturning moment and a preset safety threshold. The support length ratio is obtained by dividing the actual ground contact section length of the track by the total track length. The climbing ability coefficient is obtained by querying a preset climbing ability correspondence table based on the support length ratio. The initial step size for angle adjustment is set based on the climbing ability coefficient. The forward tilt angle is iteratively optimized using a bisection method.

[0009] Furthermore, the initial track tilt angle is identified based on the sill height and track geometry. Initial tilt angles within the feasible range are selected through safety boundary assessment. Angle convergence processing is then used to determine a feasible initial tilt angle setting, including: obtaining the initial number of contact points between the track and sill based on the sill height and track length to obtain the initial tilt angle; determining whether the track support length covers the center of gravity projection based on the initial tilt angle and center of gravity position to determine the safe support state; obtaining the ground friction coefficient based on the safe support state and track width to obtain the maximum tolerable tilt angle; determining a feasible initial tilt angle setting using angle convergence processing based on the maximum tolerable tilt angle and track spacing; and determining whether the rollover risk is below the safety boundary based on the feasible initial tilt angle setting and climb height to obtain a feasible initial tilt angle setting.

[0010] Furthermore, the step of collecting the pressure distribution in the contact area between the front track and the ground in real time through the track pressure sensing pad, extracting the average contact pressure using pressure aggregation processing, and obtaining the ground friction level by identifying the current ground material type includes:

[0011] Pressure signals are collected at various points, and a pressure matrix is ​​constructed based on the row and column coordinates of the sensing unit and the pressure values. The pressure matrix is ​​aggregated using a weighted average method to obtain the average contact pressure value of the front track area. The track bearing state is determined by the average contact pressure value. Ground feature signals are acquired by the composite sensor group in the sensing layer. The material type of the current contacting ground is determined by querying a preset material identification database based on the spectral characteristics and amplitude distribution of the signals. The corresponding surface roughness parameters are read based on the ground material type. The ground friction level is determined based on the roughness parameters and the texture depth of the track surface.

[0012] Furthermore, the step of acquiring the pressure distribution of the contact area between the front track and the ground in real time through the track pressure sensing pad, extracting the average contact pressure using pressure aggregation processing, and obtaining the ground friction level by identifying the current ground material type includes: acquiring pressure values ​​at each point through the sensor array of the track pressure sensing pad at a preset sampling frequency; interpolating the pressure values ​​between adjacent sensors using bilinear interpolation to obtain spatially continuous high-resolution sensing pad data; extracting pressure fluctuation characteristics based on the time series changes of the sensing pad data; removing high-frequency noise using a Butterworth low-pass filter; averaging the filtered pressure sequence using a sliding window to obtain the average contact pressure after spatiotemporal filtering; acquiring the physical characteristic signal of the contact surface through the ground material sensing layer; constructing a material feature vector based on the signal amplitude and frequency distribution; using the feature vector to perform cosine similarity matching with a preset material database to determine the current ground material type; and determining the ground friction level based on the average contact pressure after spatiotemporal filtering and the current ground material type.

[0013] Furthermore, a risk assessment analysis is conducted based on the average contact pressure and friction warning threshold to determine the current slippage risk level. The slippage displacement and direction are extracted from the pressure sensing pad as the basis for dynamically adjusting the forward tilt angle, including:

[0014] The difference between the average contact pressure and the preset friction warning threshold is calculated. The difference is divided by the threshold value to obtain the friction margin coefficient. The friction margin coefficient is used to determine the high-risk, medium-risk, and low-risk levels. The continuous sampling mode of the pressure sensing pad is activated by the risk level indicator. The position coordinates of the pressure peak of each sensor in two adjacent sampling periods are compared. The displacement vector of the pressure peak point in the track coordinate system is calculated. The average value of the displacement vectors of multiple sensors is taken to obtain the overall displacement vector. The magnitude of the horizontal component is extracted from the displacement vector as the slip displacement. The slip direction is determined according to the direction angle of the displacement vector.

[0015] Furthermore, based on the slippage risk level, slippage displacement and direction, and ground friction level, and combined with feasible initial settings for the rake angle, dynamic limiting is used to adjust the upper limit of the rake angle, resulting in an optimized upper limit constraint for the rake angle, including:

[0016] The weighting factor is determined according to the slippage risk level. The basic adjustment amount is calculated by multiplying the weighting factor by the slippage displacement. The adjustment amount is reduced according to the degree of deviation between the slippage direction angle and the track forward direction. The allowable pitch angle adjustment range is determined according to the ground friction level. The actual adjustment amount is obtained by limiting the adjustment amount to the allowable adjustment range. The candidate pitch angle upper limit value is obtained by subtracting the actual adjustment amount from the feasible initial pitch angle setting. The optimized pitch angle upper limit constraint is obtained by using the moving average of multiple consecutive candidate values.

[0017] Furthermore, by applying a forward tilt angle upper limit constraint to the assistive device chassis control system to correct track posture in real time, and combining this with a pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold for coordinated regulation, including:

[0018] The upper limit constraint of the forward tilt angle is input to the auxiliary chassis controller to calculate the angular deviation between the current track posture and the target posture. The proportional-integral control algorithm is used to generate a posture correction amount based on the deviation value. The correction amount is converted into a motor torque command to drive the track to adjust the forward tilt angle. The pressure exceedance degree is judged based on the difference between the average contact pressure and the elderly riding comfort threshold. The cooperative control coefficient is determined through the warning signal, and the motor torque command is attenuated using the cooperative control coefficient.

[0019] This invention provides an age-friendly multi-terrain obstacle crossing control and pressure warning collaborative control system, applied to age-friendly mobile assistive devices. It mainly includes: a scanning and filtering module for scanning the surface contour of a forward threshold, removing noise interference through filtering, and extracting the threshold height; a forward tilt angle recognition module for identifying the initial track tilt angle based on the threshold height and track geometry, filtering initial tilt angles within the executable range through safety boundary assessment, and determining feasible initial tilt angle settings through angle convergence processing; a pressure acquisition module for real-time acquisition of the pressure distribution in the contact area between the front track and the ground through track pressure sensing pads, extracting the average contact pressure through pressure aggregation processing, and simultaneously identifying the current ground material type and obtaining the ground friction level through a ground material sensing layer; and a risk assessment module for assessing the risk based on the average contact pressure and friction warning threshold. The risk assessment analyzes the current slippage risk level, extracts the slippage displacement and slippage direction from the pressure sensing pad as the basis for dynamic adjustment of the forward tilt angle; the dynamic limiting module is used to adjust the upper limit of the forward tilt angle based on the slippage risk level, slippage displacement and slippage direction, and ground friction level, combined with a feasible initial setting of the forward tilt angle, to obtain an optimized upper limit constraint of the forward tilt angle; the collaborative control module is used to apply the upper limit constraint of the forward tilt angle to the assistive device chassis control system to correct the track posture in real time, and to perform collaborative control in conjunction with the pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold, to obtain feedback from the posture sensor to evaluate whether the posture stability margin meets the elderly safety standard, to determine the collaborative control output to drive the track motor to perform angle fine-tuning operation, and to monitor the cumulative statistics of obstacle crossing completion during the obstacle crossing process to obtain the improvement in obstacle crossing success rate. The technical solution provided by the embodiments of the present invention can include the following beneficial effects:

[0020] (i) It achieves a dynamic balance between obstacle-crossing ability and anti-slip safety, significantly reducing the risk of slipping and overturning.

[0021] Existing technologies typically employ a fixed or preset lean angle for obstacle crossing, failing to detect the impact of changes in ground material on friction. This leads to a decrease in contact area and a sharp drop in friction when crossing smooth surfaces (such as tiles or marble), making slippage and even rollover highly likely. This application obtains a feasible initial lean angle setting through threshold scanning and geometric modeling. Simultaneously, it utilizes track pressure sensors to collect contact pressure distribution in real time, identify ground material, and obtain friction levels. Based on the slippage risk level, slippage displacement, and direction, the upper limit of the lean angle is dynamically optimized. This mechanism allows the assistive device to adaptively adjust the lean angle according to the current ground friction characteristics—appropriately reducing the lean angle on low-friction surfaces to maintain sufficient contact area and friction, and fully utilizing obstacle-crossing capabilities on high-friction surfaces, thereby effectively suppressing slippage and rollover risks while ensuring climbing height.

[0022] (ii) Innovatively, slippage displacement and direction are directly extracted from changes in pressure distribution, providing a precise basis for dynamic control correction.

[0023] Traditional slip detection methods often rely on indirect calculations based on the difference between drive wheel speed and travel speed, resulting in delayed response and an inability to determine the slip direction. This application abandons indirect calculation methods and directly tracks the position coordinate changes of multiple pressure peak points on the pressure sensing pad within adjacent sampling periods. By calculating the overall displacement vector, the slip displacement and slip direction are obtained. This method directly perceives the amplitude and trend of slip from the spatiotemporal changes in pressure distribution, providing clear physical meaning and rapid response. It offers a quantitative and precise correction basis for the dynamic limiting of the forward tilt angle, significantly improving the real-time performance and effectiveness of slip control.

[0024] (III) Integrating comfort pressure warning to achieve synergistic optimization of obstacle crossing safety and elderly riding comfort.

[0025] The physiological characteristics of the elderly determine their lower tolerance to vibration, impact, and unstable movements. This application introduces elderly riding comfort threshold monitoring into the posture control loop: when the average contact pressure exceeds this threshold, a pressure warning signal is triggered, and a collaborative control coefficient is determined accordingly to attenuate the motor torque command. This design dynamically integrates riding comfort as a soft constraint into the underlying drive control. While prioritizing core functions such as obstacle crossing and anti-slip, it actively "softens" posture adjustment movements, reducing impact and vibration on passengers, and significantly improving the ergonomics and safety of age-friendly assistive devices.

[0026] (iv) A fully closed-loop adaptive control architecture was constructed, encompassing environmental perception, risk assessment, and attitude execution.

[0027] This application systematically integrates multiple modules, including threshold contour scanning filtering, forward tilt angle safety boundary assessment and convergence optimization, pressure aggregation and ground material identification, slippage risk assessment and displacement extraction, dynamic amplitude limiting, and comfort-based coordinated control. These modules form a tight perception-decision-execution-feedback closed loop. Compared to existing open-loop or single-parameter adjustment control methods, this application can adaptively adjust the upper limit of the track's forward tilt angle based on real-time changes in threshold height, ground material, pressure distribution, and slippage status, and continuously iterate and optimize during obstacle crossing. Experimental statistics show that using this method significantly improves the obstacle crossing success rate of assistive devices under various terrain conditions.

[0028] (v) Possesses broad applicability and engineering feasibility of assistive devices for the elderly.

[0029] The core components used in this application—the laser rangefinder, track pressure sensing pad (flexible piezoresistive material), tilt sensor, attitude sensor, and motor driver—are all mature industrial components, characterized by controllable cost and ease of integration. Furthermore, the specification provides multiple parallel implementation methods (such as two algorithms for initial tilt angle setting and two methods for acquiring pressure and friction), offering flexible options for assistive devices with different hardware configurations and accuracy requirements. Therefore, this application can be widely used in age-friendly mobility aids such as electric wheelchairs, intelligent walkers, and stair climbers, and has promising industrial application prospects. Attached Figure Description

[0030] Figure 1 This is a flowchart of the age-adaptive multi-terrain obstacle crossing control and pressure warning collaborative control method of the present invention.

[0031] Figure 2 This is a schematic diagram of the age-adaptive multi-terrain obstacle crossing control and pressure warning collaborative control method of the present invention.

[0032] Figure 3 This is a schematic diagram of the structure of the age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control system of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0034] like Figures 1-3The aging-friendly multi-terrain obstacle crossing control and pressure warning collaborative control method and system of this embodiment may specifically include:

[0035] Step S101: Scan the surface contour of the front threshold, remove noise interference by filtering, and then extract the threshold height.

[0036] The laser ranging probe scans the threshold area ahead at a preset scanning frequency, acquiring the distance and reflection intensity values ​​of each sampling point on the threshold surface. The boundary between the threshold and the ground is identified based on abrupt changes in reflection intensity values. The spatial coordinates of the threshold's initial boundary are extracted using a Canny edge detection operator. The threshold scanning area is determined based on these spatial coordinates. Median filtering is applied to the distance values ​​within the scanning area to remove impulse noise. The filter window size is adaptively adjusted based on the spacing between adjacent sampling points, resulting in preliminary smooth contour data. Gaussian filtering is then applied to the contour data to remove high-frequency noise components generated by surface textures, yielding denoised threshold contour point cloud data. The maximum vertical coordinate value is extracted from the point cloud data, and the difference between this maximum value and the ground reference height is calculated as the threshold height measurement value. The validity of the measurement value is determined based on a laser ranging accuracy threshold. If the measurement value fluctuates beyond the threshold, the sampling density is increased, and the area is rescanned. The determined threshold height value is used as the input parameter for adjusting the track tilt angle to calculate the tilt angle.

[0037] In one embodiment, the laser ranging probe uses a 905-nanometer wavelength pulsed laser to horizontally scan the threshold area in front at a scanning frequency of 100 times per second. The laser beam oscillates left and right via a rotating reflector, and the scanning angle covers an area extending 10 centimeters on each side of the threshold width. During each scanning cycle, the probe uniformly collects 128 ranging points in the horizontal direction. At each sampling point, the time difference between laser emission and reception is recorded, and the distance value is calculated based on the speed of light. Simultaneously, the intensity value of the returned laser pulse is recorded.

[0038] Specifically, when the laser beam illuminates the boundary between the threshold and the ground, the reflection intensity changes significantly due to the abrupt change in material and height. The reflection intensity of a ceramic tile floor is typically within the normalized range of 60-80, while the reflection intensity of a wooden threshold drops to the range of 30-50. By setting a threshold of 20 for the change in reflection intensity, when the intensity difference between two adjacent sampling points exceeds this threshold, it is determined to be a boundary location. Based on this, the Canny edge detection operator calculates the direction and magnitude of the intensity gradient, suppresses non-maximum points, and accurately locates the spatial coordinates of the threshold's initial boundary. Median filtering mainly targets impulse noise in the laser ranging process, which manifests as abnormal deviations in the distance values ​​of individual sampling points from surrounding points. The filtering window is dynamically adjusted according to the physical distance between adjacent sampling points; a 5-point window is used when the sampling density is high, and it expands to a 7-point window when the density is low. Gaussian filtering handles high-frequency noise generated by surface textures. Wood grain or anti-slip stripes on the threshold surface can cause small fluctuations in the distance values. A Gaussian kernel function is used to perform a weighted average of the distance values ​​in the neighborhood, smoothing out these subtle fluctuations.

[0039] In one possible implementation, when extracting the maximum vertical coordinate from the filtered point cloud data, the algorithm iterates through the Z-axis coordinates of all sampling points to find the highest point as the top position of the threshold. The ground reference height is determined by the average height of the unobstructed area in front of the threshold. When the standard deviation of the threshold height measurement exceeds the accuracy threshold of 5 mm, the system determines that the reliability of the current scan data is insufficient and automatically increases the number of sampling points to 256, performing a more intensive scan of the area. This increased data redundancy improves measurement accuracy.

[0040] Step S102: Identify the initial track tilt angle based on the threshold height and track geometry. Filter the initial tilt angles within the executable range through safety boundary assessment. Use angle convergence processing to determine the feasible initial tilt angle setting.

[0041] Based on the sill height and total track length, the forward tilt angle required for the track front end to rise to the top of the sill is calculated using an arctangent function. The input to the arctangent function is the ratio of the sill height to the track ground contact length. Simultaneously, the vertical projection point of the center of gravity on the track support plane is determined based on the track span width and the overall center of gravity height of the auxiliary equipment. The overturning moment is calculated using the horizontal distance between this projection point and the track rear support point. If the overturning moment is less than a preset safety threshold, the current forward tilt angle is considered within the executable range. The support length ratio is obtained by dividing the actual track ground contact length by the total track length. The climbing ability coefficient is then obtained by consulting a preset climbing ability correspondence table based on this support length ratio. This climbing ability correspondence table is established through experimental testing; the input is the support length ratio, and the output is the climbing ability coefficient, which represents the track's stability and dynamic performance under different support ratios. The initial step size for angle adjustment is set according to the climbing ability coefficient. The forward tilt angle is iteratively optimized using the bisection method. In each iteration, the search direction is adjusted according to the difference between the overturning moment at the current angle and the safety threshold. The iteration is terminated when the angle difference between two adjacent iterations is less than the preset convergence accuracy threshold. The converged angle value is output as a feasible initial setting for the forward tilt angle.

[0042] In one implementation, the arctangent function is calculated based on the geometric relationship of the right triangle formed by the track and the ground. When the sill height is 15 cm and the total track length is 80 cm, the vertical height that the front end of the track needs to be raised is the sill height. The track ground contact length refers to the horizontal distance from the rear track support point to the bottom of the sill, which is approximately 60 cm. This is calculated... The initial forward tilt angle is approximately 14 degrees. The overall center of gravity of the auxiliary equipment is determined by the weighted average of the masses of each component, and is typically located 40 centimeters above the center of the track.

[0043] Specifically, the calculation of the overturning moment involves the relative positional relationship between the center of gravity projection point and the rear track pivot point. When the auxiliary device tilts forward, the center of gravity projection point moves forward. If the projection point exceeds the contact line between the rear track pivot point and the ground, a backward overturning tendency occurs. The overturning moment is equal to the total weight of the auxiliary device multiplied by the horizontal distance from the projection point to the pivot point. The safety threshold is set at 30% of the total weight of the auxiliary device multiplied by the track length. When the actual overturning moment is less than this threshold, the forward tilt angle is considered to be within the acceptable range.

[0044] It should be noted that the support length ratio reflects the actual contact degree between the track and the ground. An increased rake angle causes the front end of the track to rise, correspondingly reducing the length of the contact patch. When the support length ratio decreases from 100% to 60%, the corresponding climbing ability coefficient decreases from 1.0 to 0.7. A preset climbing ability correspondence table was established through experimental testing, recording the probability of the auxiliary equipment successfully crossing a standard threshold under different support length ratios.

[0045] Preferably, the bisection iterative optimization process starts from the initial forward tilt angle and sets upper and lower bounds for the search interval. In each iteration, the overturning moment at the current angle is calculated. If the moment exceeds 90% of the safety threshold, the current angle is used as the upper bound for the search, and the search continues in the lower half of the interval; if the moment is less than 50% of the safety threshold, the current angle is used as the lower bound for the search, and the search continues in the upper half of the interval. The initial step size is dynamically adjusted based on the climbing ability coefficient; the larger the coefficient, the smaller the step size, ensuring convergence accuracy.

[0046] In one embodiment, the convergence accuracy threshold is set to 0.5 degrees. When the angle difference between two consecutive iterations is less than this threshold, the algorithm is considered to have converged to a stable solution. The output forward tilt angle satisfies both the obstacle clearance height requirement and ensures that the assistive device does not tip backward, achieving a balance between safety and functionality.

[0047] The initial forward tilt angle is obtained by determining the number of initial contact points between the track and the threshold based on the threshold height and track length. The safe support state is determined by judging whether the track support length covers the center of gravity projection based on the initial forward tilt angle and the center of gravity position. The maximum tolerable forward tilt angle is obtained by obtaining the ground friction coefficient through the safe support state and track width. The feasible initial forward tilt angle is determined by angle convergence processing based on the maximum tolerable forward tilt angle and track spacing. The feasible forward tilt angle is obtained by judging whether the rollover risk is below the safety boundary through the feasible initial forward tilt angle setting and the climb height.

[0048] The geometric angle formed between the front end of the track and the edge of the sill is calculated based on the sill height and the total track length. The contact point distribution is estimated using tilt sensors and track rear-end force sensors. The number of effective contact points is counted, and the actual contact length is determined by multiplying the number of contact points by the track link spacing. An initial forward tilt angle is calculated using an arcsine function. The horizontal offset of the center of gravity is calculated using the initial forward tilt angle and the auxiliary support's center of gravity height coordinates. The distance between the center of gravity offset and the track rear support point is used to determine if the center of gravity projection is within the support area. If the distance from the projection point to the rear support point is less than the track ground contact section length, it is considered a safe support state, and the track ground contact area value under this safe support state is obtained. The friction coefficient under the current ground conditions is obtained by consulting a friction coefficient lookup table using the ground contact area value and track width. The maximum static friction force is obtained by multiplying the friction coefficient by the track normal force. The maximum tolerable forward tilt angle without slippage is determined by the ratio of the maximum static friction force to the driving force required for climbing, where the driving force required for climbing is F. d F is calculated using the assistive device mass m, gravitational acceleration g, and climbing slope θ. d Using the maximum tolerable forward tilt angle as the upper limit constraint, a binary search convergence is performed based on the left-right track spacing and the lateral position of the center of gravity. Each search adjusts the angle search interval according to the lateral stability margin, where the lateral stability margin Ms is defined as the ratio of the lateral offset of the center of gravity to half the track width. d is the offset distance, w is the track width. When the difference between two consecutive angles is less than the preset accuracy, a feasible initial setting of the forward tilt angle is obtained. The height of the auxiliary overturning critical point is calculated by the initial setting angle and the actual climbing height. If the critical point height is greater than the safety boundary threshold, the angle is output as a feasible forward tilt angle, which can be used as a feasible initial setting of the forward tilt angle.

[0049] In one embodiment, the track surface pressure sensor array consists of multiple piezoelectric thin-film sensors, each covering an area of ​​2 square centimeters, distributed in a 5×20 matrix on the track contact section surface. When the front end of the track raises to contact the sill, the pressure values ​​detected by the sensors exhibit a non-uniform distribution, with the pressure values ​​near the edge of the sill significantly higher than in other areas. By setting a pressure threshold of 10 Newtons per unit area, sensor locations with pressure values ​​exceeding the threshold are selected; these locations are the effective contact points. The number of effective contact points is counted and multiplied by a fixed spacing of 8 centimeters between adjacent links to obtain the actual contact length between the track and the sill. Based on the ratio of the sill height to the contact length, the initial forward tilt angle required by the track is calculated using the arcsine function. The center of gravity height coordinates of the auxiliary equipment are obtained by measuring the mass distribution of each component during the design phase. The battery pack is located at the rear of the chassis, the control box is located in the middle, and the seat and armrests are located at the top. Combining the mass and position coordinates of each component, the overall center of gravity is calculated to be 45 centimeters above the track centerline and 35 centimeters from the front end of the track. When the track is raised at an initial forward tilt angle, the center of gravity shifts forward in the horizontal direction. This shift is equal to the height of the center of gravity multiplied by the sine of the forward tilt angle. The distance from the center of gravity projection point to the rear track pivot point is equal to the original distance minus the shift. When this distance is less than the length of the current track contact section, it indicates that the center of gravity projection is still within the support area, and the system is considered to be in a safe support state.

[0050] It should be noted that the calculation of the track ground contact area takes into account the reduction in contact area caused by the forward tilt. Initially, the track is fully in contact with the ground, and the ground contact area is the track length multiplied by its width. After tilting forward, the suspended portion of the track no longer contacts the ground, and the actual ground contact area decreases accordingly. Through geometric calculations, the length of the ground contact segment is equal to the total track length multiplied by the cosine of the forward tilt angle, and then multiplied by the track width of 15 centimeters to obtain the actual ground contact area.

[0051] For example, the friction coefficient reference table is a database established through extensive experimental testing, recording empirical values ​​of the friction coefficient under different combinations of ground contact area and track width. The friction coefficient is approximately 0.3 for a ceramic tile surface with a ground contact area of ​​600 square centimeters, 0.5 for a wooden floor, and 0.7 for a cement surface. After obtaining the friction coefficient under the current conditions from the table, multiplying it by the normal force borne by the track (i.e., the vertical component of the total weight of the auxiliary equipment) yields the maximum static friction force. The driving force required for climbing is determined by multiplying the auxiliary equipment weight by the sine of the climbing angle. When the ratio of the maximum static friction force to the required driving force is greater than a safety factor of 1.2, the corresponding forward tilt angle is the maximum tolerable forward tilt angle without slippage. This angle ensures that even on smooth surfaces, the track has sufficient friction to support the climbing action, preventing obstacle crossing failure due to slippage.

[0052] Preferably, the binary search convergence process employs an iterative approach to gradually narrow the angle search range. The initial search interval is set from 0 degrees to the maximum tolerable forward tilt angle, with the first search point selected as the midpoint of this interval. At each search point, a lateral stability margin is calculated, determined by the relationship between the lateral position of the center of gravity and the left-right track spacing. The left-right track spacing is 50 centimeters; when the lateral shift of the center of gravity exceeds 40% of the spacing, the auxiliary equipment faces a risk of tipping over. If the lateral stability margin at the current search point is insufficient, the upper bound of the search interval is adjusted to the current point; if the margin is sufficient but the target climb height has not been reached, the lower bound is adjusted to the current point. After each iteration, the new search point is selected as the midpoint of the updated interval. When the angle difference between two adjacent searches is less than a preset accuracy of 0.5 degrees, convergence to a feasible solution satisfying multiple constraints is considered successful.

[0053] In one possible implementation, the calculation of the tipping critical height is based on the principle of torque balance. When the assistive device climbs at a specific forward tilt angle, there exists a theoretical tipping axis that passes through the rear fulcrum of the track and is perpendicular to the ground. The vertical distance from the assistive device's center of gravity to the tipping axis forms a stabilizing lever arm. Increased climbing height causes the center of gravity to shift forward, reducing the stabilizing lever arm. The tipping critical height is defined as the climbing height at which the stabilizing lever arm decreases to zero, at which point the assistive device's center of gravity is directly above the tipping axis, and the torque generated by gravity is zero. The critical height is derived through geometric relationships. , where hc is the critical height of the flipping point, h0 is the initial height of the center of gravity, d is the horizontal distance from the center of gravity to the rear support point, and θ is the forward tilt angle.

[0054] The setting of the safety boundary threshold takes into account both the elderly user's tolerance and the structural strength of the assistive device. The threshold is usually set at 70% of the theoretical tilting height, with a 30% safety margin. When the calculated tilting critical point height is greater than this safety boundary threshold, it indicates that the assistive device will not tip backward at the current forward tilting angle, and this angle is determined as the feasible forward tilting angle.

[0055] For example, when the threshold height is 20 centimeters, the initial forward tilt angle calculated using the complete process described above might be 18 degrees. After considering friction constraints, this is reduced to 15 degrees. Following lateral stability testing and rollover risk assessment, the final feasible forward tilt angle is determined to be 13.5 degrees. This angle ensures that the front end of the track reaches sufficient height to clear the threshold while maintaining the multi-dimensional stability of the auxiliary equipment during the climbing process, thus achieving a safe and reliable obstacle-crossing function.

[0056] Step S103: The pressure distribution of the front track in contact with the ground is collected in real time by the track pressure sensing pad, and the average contact pressure is extracted by pressure aggregation processing. At the same time, the ground material sensing layer is used to identify the current ground material type and obtain the ground friction level.

[0057] Pressure signals at various points are collected in real time by an array of sensing units within the track pressure sensing pad. Each sensing unit outputs a pressure value at its corresponding location. A pressure matrix is ​​constructed based on the row and column coordinates of the sensing units and the pressure values. A weighted average method is used to aggregate the pressure matrix, with the weight values ​​inversely proportional to the distance from the sensing unit to the track centerline, yielding the average contact pressure value for the front track area. The average contact pressure value is used to determine the track load-bearing state. When the pressure value exceeds a preset threshold, the ground material sensing layer is activated. A composite sensor array within the sensing layer acquires ground characteristic signals. Based on the signal's spectral characteristics and amplitude distribution, a preset material identification database is queried to determine the material type of the currently contacting ground. The corresponding surface roughness parameters are read based on the ground material type. The actual contact area ratio is calculated based on the roughness parameters and the track surface texture depth. The contact area ratio is multiplied by the material's basic friction coefficient to obtain the ground friction level under the current conditions.

[0058] In one embodiment, the track pressure sensing pad is made of flexible piezoresistive material, and the sensing units are evenly distributed in an 8x12 matrix at the front grounding section of the track. Each sensing unit consists of a conductive rubber layer and an insulating base layer. When pressure is applied, the resistance value changes, and this change is converted into a voltage signal via a Wheatstone bridge circuit. The voltage value output by the sensing unit is proportional to the applied pressure, and after analog-to-digital conversion, a digital pressure value is generated. Based on the row and column index of each unit in the matrix, the pressure value is filled into the corresponding matrix element to construct a real-time updated pressure matrix.

[0059] Specifically, the weighting of the weighted average method follows the distance decay principle. The track centerline is defined as the longitudinal axis of the 6th column of the matrix. For every increase in the lateral distance from the sensing unit to the centerline by one unit spacing, the weight value decreases by a decay coefficient of 0.9. The weight of the unit located on the centerline is set to 1.0, the weight of the adjacent column is 0.9, and so on. The average contact pressure value, reflecting the overall stress state, is obtained by summing the products of the pressure value of each unit and its corresponding weight, and then dividing by the total weight. When the average contact pressure value exceeds the preset threshold of 50 Newtons, it indicates that the track has made stable contact with the ground, at which point the material sensing layer is activated for ground identification. The composite sensor group within the sensing layer includes a capacitive proximity sensor, a vibration sensor, and a temperature sensor, which detect different physical characteristics. The capacitive sensor identifies the electrical properties of the material by measuring changes in dielectric constant, the vibration sensor detects the vibration spectrum generated during track movement, and the temperature sensor monitors the heat conduction rate of the contact surface.

[0060] Preferably, the material identification database pre-stores feature vectors of common flooring materials. The feature vector for ceramic tiles includes a dielectric constant of 8-10, a dominant vibration frequency of 200-300 Hz, and a thermal conductivity of 1.0 W / m Kelvin. The feature vector for wood flooring includes a dielectric constant of 3-5, a dominant vibration frequency of 100-150 Hz, and a thermal conductivity of 0.15 W / m Kelvin. By calculating the Euclidean distance between the measured signal features and the feature vectors of each material in the database, the material type with the smallest distance is selected as the identification result.

[0061] In one embodiment, the surface roughness parameter is read from the physical property table corresponding to the material type; the roughness of ceramic tile is 0.5 micrometers, and that of wood flooring is 20 micrometers. The texture depth of the track surface is designed to be 500 micrometers. The ratio of the roughness to the texture depth is used to calculate the proportion of the microscopic contact area to the macroscopic contact area. This proportion is multiplied by the material's base coefficient of friction to obtain the ground friction level under actual conditions.

[0062] High-resolution sensor data is obtained by real-time acquisition of pressure distribution in the contact area between the front track and the ground using a track pressure sensing pad. The average contact pressure is obtained by pressure aggregation processing based on the sensor data. The current ground material type is determined by synchronously acquiring the sensing layer signal in the contact area of ​​the front track through the ground material sensing layer. The average contact pressure is extracted after filtering in the spatial and temporal domains. The ground friction level is determined based on the ground material type and the average contact pressure.

[0063] Pressure values ​​at various points are collected by the sensor array of the track pressure sensing pad at a preset sampling frequency. A pressure distribution matrix is ​​constructed based on the sensor row and column positions and pressure values. Bilinear interpolation is used to interpolate the pressure values ​​between adjacent sensors to obtain spatially continuous high-resolution sensing pad data. Pressure fluctuation characteristics are extracted based on the time series changes of the sensing pad data. High-frequency noise is removed by setting the cutoff frequency of a Butterworth low-pass filter to a preset Hertz value. A sliding window is used to segment and average the filtered pressure sequence, calculating the average pressure within each window. When the average difference between consecutive windows is less than a threshold, it is determined as a stable segment, obtaining the average contact pressure after spatiotemporal filtering. Physical feature signals of the contact surface are obtained through the ground material sensing layer. A material feature vector is constructed based on the signal amplitude and frequency distribution. The feature vector is then matched with a preset material database using cosine similarity. The material with the highest similarity is the current ground material type. The basic friction coefficient is queried according to the material type, the normal load value is calculated using the average contact pressure, the theoretical friction force is obtained by multiplying the basic friction coefficient and the normal load, and the theoretical friction force is normalized according to the track contact area to obtain the ground friction force level.

[0064] In one embodiment, the track pressure sensing pad employs a matrix-arranged piezoresistive sensor array, with sensors evenly distributed in a 10x16 grid across the front contact area of ​​the track. Each sensor unit has an effective sensing area of ​​4 square centimeters, and the center-to-center distance between adjacent sensors is 2 centimeters. The sensor array synchronously acquires pressure values ​​from all sensing points at a sampling frequency of 1000 Hz, obtaining 160 discrete pressure data points per sampling period. The sensor's spatial position in the track coordinate system is determined based on its row and column indices, and the pressure value is used as a scalar attribute of that position, constructing a sparse pressure distribution matrix of 160 nodes.

[0065] Specifically, the bilinear interpolation method involves a weighted combination of pressure values ​​from the four nearest-neighbor sensor nodes. For any point to be interpolated, the four sensor nodes surrounding that point are first identified, and the normalized distance from the point to be interpolated to each node is calculated. The interpolation weight is inversely proportional to the distance; nodes closer to each other contribute a larger weight. Linear interpolation is performed separately in the lateral and longitudinal directions, and then the interpolation results in the two directions are linearly combined again to obtain the pressure estimate for the point to be interpolated. After interpolation, the original 160 discrete points are expanded into a dense grid of 80×128, forming high-resolution sensor pad data with a resolution of 0.25 square centimeters. This high-density pressure distribution data can accurately reflect the detailed characteristics of the track-ground contact, including local stress concentration areas and pressure gradient changes.

[0066] It should be noted that the extraction of pressure fluctuation characteristics is based on a data sequence of multiple consecutive sampling periods. The pressure values ​​at each sensor location are arranged in chronological order to form a time series of length 1000, corresponding to a 1-second observation window. By calculating the first difference of the sequence, the pressure change rate sequence is obtained, reflecting the instantaneous pressure fluctuation. A fast Fourier transform is performed on the change rate sequence to obtain a frequency domain representation, where low-frequency components correspond to slow pressure change trends, and high-frequency components correspond to rapid vibrations and noise.

[0067] Preferably, the Butterworth low-pass filter is designed with a fourth-order configuration and a cutoff frequency set at 50 Hz. This filter has a flat amplitude-frequency response in the passband, avoiding signal distortion, provides -3 dB attenuation at the cutoff frequency, and attenuates at a rate of 80 dB per decade in the stopband. The filtering process is implemented in the frequency domain, multiplying the spectrum of the pressure signal by the filter transfer function, and then returning to the time domain via an inverse Fourier transform to obtain a smooth pressure sequence with high-frequency noise removed. The filtered signal retains the main pressure change characteristics generated by track movement while eliminating interference from mechanical vibration and sensor noise.

[0068] For example, the width of the sliding window is set to 100 sampling points, corresponding to a time span of 0.1 seconds, and the window step is 50 sampling points, achieving 50% overlap. Within each window, the arithmetic mean of all sensor pressure values ​​is calculated to obtain the overall pressure level for that time period. Simultaneously, the standard deviation of the pressure values ​​within the window is calculated to assess the dispersion of the pressure distribution. When the average pressure difference over five consecutive windows is less than a preset threshold of 2 Newtons, and the standard deviation is less than 5 Newtons, the system is considered to have entered a stable state. The average pressure within the stable segment is used as the final output after spatiotemporal filtering, representing the typical contact pressure under the current operating conditions.

[0069] In one possible implementation, the ground material sensing layer integrates multiple physical quantity sensors. The sensing layer acquires the electrical, dielectric, and acoustic properties of the material by measuring the resistance, capacitance, and sound wave propagation speed of the contact surface. These physical quantities are normalized to the 0-1 range to form a three-dimensional feature vector. A preset material database stores standard feature vectors for 20 common ground materials, including ceramic tiles, wood flooring, carpet, cement, and asphalt.

[0070] Understandably, cosine similarity matching assesses similarity by calculating the cosine of the angle between two vectors. The measured feature vector is multiplied by each standard vector in the database, and then divided by the product of the magnitudes of the two vectors to obtain a similarity value between -1 and 1. The closer the similarity value is to 1, the more similar the two vectors are. The material with the highest similarity is selected as the identification result; when the highest similarity is below 0.7, it is determined to be an unknown material type.

[0071] For example, the coefficient of friction for ceramic tiles is 0.4, for wood flooring it is 0.6, and for carpet it is 0.8. The normal load value is calculated by multiplying the average contact pressure by the contact area. The theoretical friction force equals the product of the coefficient of friction and the normal load, reflecting the maximum static friction force under ideal conditions. Area normalization divides the theoretical friction force by the actual contact area to obtain the friction force level per unit area, eliminating the influence of contact area variations on friction force assessment and making friction force levels comparable under different working conditions.

[0072] Step S104: Based on the average contact pressure and friction warning threshold, conduct a risk assessment and analysis of the current slippage risk level, and extract the slippage displacement and slippage direction from the pressure sensing pad as the basis for dynamic adjustment of the forward tilt angle.

[0073] The difference between the average contact pressure and a preset friction warning threshold is calculated. Dividing the difference by the threshold value yields the friction margin coefficient. A friction margin coefficient less than a first threshold indicates a high-risk level, between the first and second thresholds indicates a medium-risk level, and greater than the second threshold indicates a low-risk level, thus obtaining the current slippage risk level identifier. The continuous sampling mode of the pressure sensing pad is activated using this risk level identifier. The position coordinates of the pressure peak values ​​of each sensor within two adjacent sampling periods are compared, and the displacement vector of the pressure peak point in the track coordinate system is calculated. The average displacement vector of multiple sensors is taken to obtain the overall displacement vector. The magnitude of the horizontal component is extracted from this displacement vector as the slippage displacement. The slippage direction is determined based on the direction angle of the displacement vector. The slippage displacement is multiplied by a preset correction coefficient to obtain the forward tilt angle correction amplitude. The correction amplitude and slippage direction are combined to form a correction basis containing both amplitude and direction parameters.

[0074] In one implementation, the friction warning threshold is preset according to different floor materials: 30 Newtons for tile floors, 45 Newtons for wood floors, and 60 Newtons for carpets. The difference between the average contact pressure and the corresponding threshold reflects the safety margin of the current friction force. When the average contact pressure is 25 Newtons and the threshold for tile floors is 30 Newtons, the difference is -5 Newtons, indicating insufficient friction. Dividing the difference by the threshold value yields the friction margin coefficient, which is -0.17 in this example. A negative value indicates that the friction force is below the safe level.

[0075] Specifically, the risk level is classified using a three-tier threshold system. The first threshold is set at 0.2, and the second threshold is set at 0.5. When the friction margin coefficient is less than 0.2, the system classifies it as a high-risk level, where the friction between the track and the ground is close to a critical state, and slippage may occur at any time. A coefficient between 0.2 and 0.5 is a medium-risk level, requiring close monitoring but not yet reaching a dangerous level. A coefficient greater than 0.5 is a low-risk level, where friction is sufficient and the track operates stably.

[0076] It should be noted that the activation mechanism of continuous sampling mode is dynamically adjusted according to the risk level. At high risk levels, the sampling frequency is increased to 2000 Hz, with each sensor updating data twice every millisecond. At medium risk levels, the standard frequency of 1000 Hz is maintained. At low risk levels, the frequency is reduced to 500 Hz to conserve computational resources. In continuous sampling mode, the system caches complete data from the most recent 10 sampling cycles for displacement detection.

[0077] Preferably, the identification of pressure peak points is achieved through local maximum search. In the pressure matrix of each sampling period, positions with values ​​greater than the eight surrounding points are identified as pressure peak points. The row and column coordinates of the peak points in the track coordinate system are recorded, and the coordinate changes of the same peak point between adjacent periods constitute the displacement vector. Since there may be multiple pressure concentration points on the track surface, the system simultaneously tracks 5-8 main peak points, and calculates the arithmetic mean of the displacement vectors of each point as the overall displacement vector.

[0078] In one embodiment, the horizontal component of the displacement vector is calculated using coordinate differences. If the coordinates of a peak point are (x1, y1) in the first period and (x2, y2) in the second period, then the horizontal displacement is... The slippage direction passes through. The calculated result is the deflection angle relative to the track's forward direction.

[0079] For example, when a slippage displacement of 5 mm is detected, with a leftward deviation of 15 degrees, the correction factor is set to 0.1 degrees / mm, and the calculated forward tilt angle correction is 0.5 degrees. Combining the 0.5-degree correction value and the -15-degree directional value forms a correction basis containing two parameters, guiding the dynamic adjustment of the track attitude.

[0080] Step S105: Based on the slip risk level, slip displacement and slip direction, and ground friction level, and combined with feasible initial settings for the forward tilt angle, the upper limit of the forward tilt angle is adjusted using dynamic limiting processing to obtain the optimized upper limit constraint for the forward tilt angle.

[0081] The weighting factors are determined based on the slippage risk level: 1.0 for high risk, 0.6 for medium risk, and 0.3 for low risk. The basic adjustment is calculated by multiplying the weighting factor by the slippage displacement. Based on the deviation between the slippage direction angle and the track's forward direction, the adjustment remains unchanged if the deviation angle is less than a preset threshold; otherwise, the adjustment is reduced by the sine of the deviation angle, resulting in the adjusted value after directional compensation. The allowable rake angle adjustment range is determined based on the ground friction level; the lower the friction level, the smaller the allowable adjustment range. The actual adjustment is obtained by limiting the adjustment value within the allowable range. The candidate rake angle upper limit is obtained by subtracting the actual adjustment from the feasible initial rake angle setting. A moving average of multiple candidate values ​​is used to reduce abrupt changes. If the moving average result is less than a preset lower safety angle limit, the lower safety angle limit is used as the output; otherwise, the moving average result is used as the output, resulting in the optimized rake angle upper limit constraint.

[0082] Specifically, the weighting factor's tiered assignment mechanism is designed based on the urgency of the risk level. A high-risk level means the track is likely to slip severely at any time, and a maximum weight of 1.0 is assigned to ensure the system remains highly sensitive to adjustments. A medium-risk level indicates a tendency to slip but not yet out of control, and a weight of 0.6 ensures a moderate response. A low-risk level requires only preventative adjustments, and a weight of 0.3 avoids overreaction. The weighting factor is multiplied by the slip displacement, coupling the risk level with the adjustment magnitude; in high-risk situations, even a small displacement results in a large adjustment. In one implementation, the directional compensation mechanism is achieved by judging the deviation between the slip direction and the track's forward direction. When the track is moving forward, the ideal slip direction should be consistent with the forward direction, with a deviation angle of zero. In actual operation, due to uneven ground or uneven friction on one side, oblique slippage may occur. The deviation angle threshold is set to 15 degrees; values ​​less than this are considered to indicate a basically normal slip direction, and the adjustment amount remains unchanged. When the threshold is exceeded, the sine of the deviation angle is used as the reduction coefficient. For a deviation of 30 degrees, the sine value is 0.5, and the adjustment is halved. For a deviation of 90 degrees, complete sideslip occurs, and the adjustment is reduced to zero. The ground friction level directly determines the safe adjustment range for the roll angle. The friction level is represented by a normalized value in the 0-1 range. Above 0.8 is considered high friction, with an allowable adjustment range of ±10 degrees. 0.4-0.8 is medium friction, with the adjustment range narrowed to ±5 degrees. Below 0.4 is low friction, allowing only a fine adjustment of ±2 degrees to prevent track instability due to excessive adjustment.

[0083] Preferably, the moving average processing employs a five-point averaging method, caching candidate upper limit values ​​for the most recent five control cycles. Upon obtaining a new candidate value, the oldest historical value is discarded, and the arithmetic mean of the current five values ​​is calculated. This processing method filters out sudden changes caused by transient disturbances, resulting in smoother tilt angle adjustments. The choice of average window width balances response speed and stability; too narrow a window will not provide effective smoothing, while too wide a window will result in a sluggish response.

[0084] For example, the lower limit of the safe angle is preset to 5 degrees based on the assistive device's structural parameters. This is the minimum forward tilt angle required to maintain basic obstacle-crossing capability. When the moving average result is 3 degrees, the system determines that the angle is too small and may lead to obstacle-crossing failure, and forcibly outputs 5 degrees as the upper limit constraint for the forward tilt angle. If the average result is 8 degrees, which is within the safe range, then 8 degrees is directly adopted as the final constraint value, achieving a balance between dynamic optimization and safety assurance.

[0085] Step S106: The upper limit constraint of the forward tilt angle is applied to the assistive device chassis control system to correct the track posture in real time. Combined with the pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold, the system performs coordinated regulation. The posture sensor feedback is obtained to evaluate whether the posture stability margin meets the elderly safety standard. The coordinated control output drives the track motor to perform angle fine-tuning operation. The obstacle crossing completion rate is monitored and accumulated to obtain the obstacle crossing success rate improvement.

[0086] The upper limit constraint of the forward tilt angle is input to the assistive chassis controller to calculate the angular deviation between the current track posture and the target posture. A proportional-integral control algorithm is used to generate a posture correction value based on the deviation. This correction value is converted into a motor torque command to drive the track to adjust the forward tilt angle, and the cumulative deviation value for each control cycle is recorded. The pressure exceedance level is determined based on the difference between the average contact pressure and the elderly riding comfort threshold. If the difference exceeds a preset limit, a pressure warning signal is triggered. The warning signal determines the collaborative control coefficient, which is used to attenuate the motor torque command, resulting in a control command with comfort constraints. Track pitch and roll angle data are acquired through attitude sensors. The weighted sum of the rate of change of angular velocity and the cumulative deviation value is calculated as the posture stability margin. If the stability margin is lower than the elderly safety standard threshold, the proportional gain parameter is reduced. The posture correction value and motor torque command are recalculated using the adjusted proportional gain parameter, and the adjusted collaborative control command is output. The track motor is driven by the aforementioned collaborative control command to perform angle fine-tuning. The ratio of the actual track rotation angle to the target rotation angle is detected by the encoder to obtain the single obstacle crossing completion rate. The proportion of times the completion rate exceeds the preset threshold in multiple obstacle crossing tasks is counted to obtain the obstacle crossing success rate improvement.

[0087] In one implementation, the auxiliary chassis controller uses an embedded processor to implement real-time control functions. The processor executes a control loop at a frequency of 100 Hz. The upper limit constraint of the tilt angle is used as an input parameter to the controller and is compared with the current track posture. The current posture is measured by a tilt sensor mounted on the track support, with a measurement accuracy of 0.1 degrees. The target posture is determined based on obstacle crossing requirements and the upper limit constraint of the tilt angle; the difference between the two is the angle deviation that needs to be adjusted. This deviation value is input into the proportional-integral control algorithm, with the proportional coefficient set to 0.8 and the integral coefficient set to 0.2. The proportional term quickly responds to changes in deviation, while the integral term eliminates steady-state errors.

[0088] Specifically, the implementation of the proportional-integral (PI) control algorithm involves real-time calculation and historical accumulation of deviation. The proportional term directly multiplies the current deviation by a proportional coefficient, generating a control quantity proportional to the deviation; the larger the deviation, the stronger the adjustment. The integral term accumulates historical deviations; the deviation value for each control cycle is recorded and accumulated, forming a cumulative deviation value. This cumulative value is multiplied by the integral coefficient and added to the proportional term to obtain the attitude correction amount. The correction amount is calculated using the torque-angle conversion relationship of the motor controller to determine the required motor torque command. The conversion coefficient is determined based on the motor characteristic curve, typically corresponding to a 0.5-degree angle change per Newton-meter of torque. After receiving the torque command, the motor drives the track support to rotate through the reduction mechanism, thereby adjusting the tilt angle.

[0089] The comfort threshold for elderly passengers is set considering their physiological characteristics and psychological tolerance. Medical research shows that the elderly have a 30-40% lower tolerance for acceleration and vibration compared to younger people. The comfort threshold is set at a contact pressure of 50 Newtons. When the measured pressure exceeds this value, elderly passengers will experience significant discomfort. The degree of pressure exceeding the limit is divided into three levels: less than 20% exceeding the threshold is considered mild, 20-50% is moderate, and more than 50% is severe. Different levels trigger pressure warning signals of varying intensities: a coordination control coefficient of 0.9 for mild warnings, 0.7 for moderate warnings, and 0.5 for severe warnings. The coordination control coefficient is multiplied by the motor torque command to reduce the control intensity, prioritizing comfort.

[0090] Preferably, the attitude sensor integrates a three-axis gyroscope and a three-axis accelerometer, capable of simultaneously measuring the pitch angle, roll angle, and rate of change of the tracks. The pitch angle reflects the forward and backward tilt of the tracks, while the roll angle reflects the left and right tilt. The rate of change of angular velocity is obtained by differentiating the angular velocity signal output by the gyroscope, reflecting the acceleration of attitude changes. The attitude stability margin is calculated using a weighted sum method, with the rate of change of angular velocity weighted at 0.6 and the cumulative deviation weighted at 0.4. A large rate of change of angular velocity indicates rapid attitude changes and reduced stability; a large cumulative deviation indicates that the system has failed to eliminate deviations for an extended period, resulting in poor control performance. The weighted sum of these two factors comprehensively reflects the stability of the system.

[0091] For example, the age-friendly safety standard threshold is determined based on balance ability test data of older adults. Tests show that older adults experience significant insecurity when the track tilt angular velocity exceeds 10 degrees per second, and cumulative deviations exceeding 5 degrees affect posture stability. Therefore, the safety threshold for posture stability margin is set to a comprehensive index value of 0.3. When the calculated stability margin is lower than this value, the system determines that the current control parameters are too aggressive and needs to reduce the response speed to improve stability. At this time, the proportional gain parameter is reduced from 0.8 to 0.5 to slow down the system response and avoid overshoot and oscillation.

[0092] In one possible implementation, the tracked motor is equipped with a high-precision incremental encoder with a resolution of 4096 pulses per revolution. The encoder detects the angular position of the motor shaft in real time and obtains the actual rotation angle through pulse counting. The target rotation angle is calculated based on the required tilt angle adjustment and the reduction ratio. The ratio of the actual rotation angle to the target rotation angle represents the completion rate of a single obstacle crossing; a completion rate of 95% or higher is considered a successful obstacle crossing.

[0093] Understandably, the obstacle-crossing success rate is calculated using a sliding window method, with the window size being the last 20 obstacle-crossing tasks. Each time a new obstacle-crossing task is completed, the oldest historical record is removed, and the new record is added to the statistical window. The current obstacle-crossing success rate is obtained by dividing the number of successful attempts within the statistical window by the total number of attempts (20). By comparing the difference in success rates before and after optimization, the improvement effect of the control algorithm is quantitatively evaluated, providing data support for further optimization.

[0094] This invention provides an age-adaptive multi-terrain obstacle crossing control and pressure warning collaborative control system, mainly comprising: a scanning and filtering module for scanning the surface contour of a forward threshold, removing noise interference through filtering, and extracting the threshold height; a forward tilt angle recognition module for identifying the initial track tilt angle based on the threshold height and track geometry, filtering initial tilt angles within the executable range through safety boundary assessment, and determining feasible initial tilt angle settings through angle convergence processing; a pressure acquisition module for real-time acquisition of the pressure distribution in the contact area between the front track and the ground through track pressure sensing pads, extracting the average contact pressure through pressure aggregation processing, and simultaneously identifying the current ground material type and obtaining the ground friction level through a ground material sensing layer; and a risk assessment module for performing risk assessment analysis based on the average contact pressure and friction warning threshold. The current slip risk level is used to extract the slip displacement and slip direction from the pressure sensing pad as the basis for dynamic adjustment of the forward tilt angle. A dynamic limiting module is used to adjust the upper limit of the forward tilt angle based on the slip risk level, slip displacement and slip direction, and ground friction level, combined with a feasible initial setting of the forward tilt angle, resulting in an optimized upper limit constraint. A collaborative control module is used to apply the upper limit constraint of the forward tilt angle to the assistive device chassis control system to correct the track posture in real time. It also performs collaborative control by combining the pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold, obtaining feedback from the posture sensor to assess whether the posture stability margin meets the elderly-friendly safety standard, determining the collaborative control output to drive the track motor to perform angle fine-tuning operations, and monitoring the cumulative statistics of obstacle crossing completion during the obstacle crossing process to obtain the improvement in obstacle crossing success rate. The above description is only a preferred embodiment of this application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of this application. For example, technical solutions formed by replacing the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method, characterized in that, The method, applied to age-appropriate mobility aids, includes: Scan the surface contour of the front threshold, remove noise, and then extract the threshold height. The initial track tilt angle is identified based on the threshold height and track geometry. Initial tilt angles within the executable range are selected through safety boundary assessment. Angle convergence processing is used to determine the feasible initial tilt angle setting. The pressure distribution in the contact area between the front track and the ground is collected in real time by the track pressure sensing pad. The average contact pressure is extracted by pressure aggregation processing. The ground friction level is obtained by identifying the current ground material type. A risk assessment analysis of the current slippage risk level is conducted based on the average contact pressure and friction warning threshold. The slippage displacement and slippage direction are extracted from the pressure sensing pad as the basis for dynamic adjustment of the forward tilt angle; including: The difference between the average contact pressure and the preset friction warning threshold is calculated. The difference is divided by the threshold value to obtain the friction margin coefficient. The friction margin coefficient is used to determine the high-risk level, medium-risk level, and low-risk level. The continuous sampling mode of the pressure sensing pad is activated by the risk level identification. The position coordinates of the pressure peak of each sensor in two adjacent sampling periods are compared. The displacement vector of the pressure peak point in the track coordinate system is calculated. The average value of the displacement vectors of multiple sensors is taken to obtain the overall displacement vector. The magnitude of the horizontal component is determined as the slip displacement. The slip direction is determined according to the direction angle of the displacement vector. Based on the slip risk level, slip displacement and slip direction, and ground friction level, the upper limit of the forward tilt angle is adjusted by dynamic limiting processing from the initial setting of the forward tilt angle to obtain the optimized upper limit constraint of the forward tilt angle. The upper limit constraint of the forward tilt angle is applied to the assistive device chassis control system to correct the track posture in real time, and coordinated regulation is carried out in conjunction with the pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold.

2. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The process of scanning the surface contour of the front threshold, denoising, and extracting the threshold height includes: The threshold area is scanned to obtain the distance and reflection intensity values ​​of each sampling point on the threshold surface. The boundary line between the threshold and the ground is identified based on the location of abrupt changes in reflection intensity values. The spatial coordinates of the threshold's starting boundary are extracted using the Canny edge detection operator. The threshold scanning area is determined based on the spatial coordinates. Median filtering is applied to the distance values ​​within the scanning area to remove impulse noise, resulting in preliminary smooth contour data. Gaussian filtering is then applied to obtain the denoised threshold contour point cloud data. The maximum vertical coordinate value is extracted, and the difference between this maximum value and the ground reference height is calculated as the threshold height value.

3. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The process of identifying the initial track tilt angle based on the threshold height and track geometry, filtering out initial tilt angles within the executable range through safety boundary assessment, and determining feasible initial tilt angle settings using angle convergence processing includes: Based on the threshold height and total track length, the required forward tilt angle to raise the front end of the track to the top of the threshold is calculated using the arctangent function. The vertical projection point of the center of gravity on the track support plane is determined based on the track span width and the overall center of gravity height of the auxiliary equipment. The overturning moment is calculated using the horizontal distance between the vertical projection point and the rear support point of the track. The current forward tilt angle is determined to be within the executable range based on the overturning moment and a preset safety threshold. The support length ratio is obtained by dividing the actual ground contact section length of the track by the total track length. The climbing ability coefficient is obtained by querying a preset climbing ability correspondence table based on the support length ratio. The initial step size for angle adjustment is set based on the climbing ability coefficient. The forward tilt angle is iteratively optimized using a bisection method.

4. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The initial track tilt angle is identified based on the sill height and track geometry. An initial tilt angle within the feasible range is selected through safety boundary assessment. Angle convergence processing is then used to determine a feasible initial tilt angle setting, including: obtaining the initial number of contact points between the track and sill based on the sill height and track length to obtain the initial tilt angle; determining whether the track support length covers the center of gravity projection based on the initial tilt angle and center of gravity position to determine the safe support state; obtaining the ground friction coefficient based on the safe support state and track width to obtain the maximum tolerable tilt angle; determining a feasible initial tilt angle setting using angle convergence processing based on the maximum tolerable tilt angle and track spacing; and determining whether the rollover risk is below the safety boundary based on the feasible initial tilt angle setting and climb height to obtain a feasible initial tilt angle setting.

5. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The process involves real-time acquisition of pressure distribution in the contact area between the front track and the ground using a track pressure sensing pad, extraction of average contact pressure through pressure aggregation processing, and identification of the current ground material type to determine the ground friction level, including: Pressure signals are collected at various points, and a pressure matrix is ​​constructed based on the row and column coordinates of the pressure sensing unit and the pressure value. The pressure matrix is ​​aggregated to obtain the average contact pressure value of the front track area. The track bearing state is determined by the average contact pressure value. Ground feature signals are obtained through the composite sensor group in the sensing layer. The material type of the current contacting ground is determined based on the spectral characteristics and amplitude distribution of the signal. The corresponding surface roughness parameter is read through the ground material type. The ground friction level is determined based on the roughness parameter and the texture depth of the track surface.

6. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The process involves real-time acquisition of pressure distribution in the contact area between the front track and the ground using a track pressure sensing pad, extraction of average contact pressure through pressure aggregation processing, and determination of ground friction level by identifying the current ground material type. This includes: acquiring pressure values ​​at various points using the sensor array of the track pressure sensing pad at a preset sampling frequency; interpolating the pressure values ​​between adjacent sensors using bilinear interpolation to obtain spatially continuous high-resolution sensing pad data; extracting pressure fluctuation characteristics based on the time-series changes of the sensing pad data; removing high-frequency noise to obtain the average contact pressure after spatiotemporal filtering; acquiring physical characteristic signals of the contact surface through a ground material sensing layer; constructing a material feature vector based on the signal amplitude and frequency distribution; performing cosine similarity matching between the feature vector and a preset material database to determine the current ground material type; and determining the ground friction level based on the average contact pressure after spatiotemporal filtering and the current ground material type.

7. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The optimized upper limit constraint for the forward tilt angle is obtained by dynamically limiting the upper limit of the forward tilt angle based on the risk level of slippage, the amount and direction of slippage, and the level of ground friction, combined with a feasible initial setting of the forward tilt angle, including: The corresponding weighting factor is determined based on the slippage risk level. The basic adjustment amount is calculated by multiplying the weighting factor by the slippage displacement. The adjustment amount is reduced based on the degree of deviation between the slippage direction angle and the track forward direction. The allowable pitch angle adjustment range is determined based on the ground friction level. The actual adjustment amount is obtained by limiting the adjustment amount to the allowable adjustment range. The candidate pitch angle upper limit value is obtained by subtracting the actual adjustment amount from the feasible initial pitch angle setting. The optimized pitch angle upper limit constraint is then determined.

8. The age-adaptive multi-terrain obstacle crossing control and pressure early warning collaborative control method according to claim 1, characterized in that, The method of applying the upper limit constraint of the forward tilt angle to the assistive device chassis control system to correct the track posture in real time, and combining it with the pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold for coordinated regulation, including: The upper limit constraint of the forward tilt angle is input to the auxiliary chassis controller to calculate the angular deviation between the current track posture and the target posture. The proportional-integral control algorithm is used to generate a posture correction amount based on the deviation value. The correction amount is converted into a motor torque command to drive the track to adjust the forward tilt angle. The pressure exceedance degree is judged based on the difference between the average contact pressure and the elderly riding comfort threshold. The cooperative control coefficient is determined through the warning signal, and the motor torque command is attenuated using the cooperative control coefficient.

9. An age-adaptive, multi-terrain obstacle-crossing control and pressure warning collaborative control system, characterized in that: The system, applicable to age-appropriate mobility aids, includes: The scanning filtering module is used to scan the surface contour of the front sill, remove noise, and then extract the sill height. The forward tilt angle recognition module is used to identify the initial forward tilt angle of the track based on the threshold height and track geometry. It filters the initial forward tilt angles within the executable range through safety boundary assessment and uses angle convergence processing to determine the feasible initial forward tilt angle setting. The pressure acquisition module is used to collect the pressure distribution in the contact area between the front track and the ground in real time through the track pressure sensing pad. The average contact pressure is extracted by pressure aggregation processing, and the ground friction level is obtained by identifying the current ground material type. The risk assessment module is used to analyze the current slippage risk level based on the average contact pressure and friction warning threshold, and extracts the slippage displacement and slippage direction from the pressure sensing pad as the basis for dynamic adjustment of the tilt angle; including: The difference between the average contact pressure and the preset friction warning threshold is calculated. The difference is divided by the threshold value to obtain the friction margin coefficient. The friction margin coefficient is used to determine the high-risk level, medium-risk level, and low-risk level. The continuous sampling mode of the pressure sensing pad is activated by the risk level identification. The position coordinates of the pressure peak of each sensor in two adjacent sampling periods are compared. The displacement vector of the pressure peak point in the track coordinate system is calculated. The average value of the displacement vectors of multiple sensors is taken to obtain the overall displacement vector. The magnitude of the horizontal component is determined as the slip displacement. The slip direction is determined according to the direction angle of the displacement vector. The dynamic limiting module is used to adjust the upper limit of the forward tilt angle based on the risk level of slippage, the amount and direction of slippage, and the level of ground friction, combined with a feasible initial setting of the forward tilt angle, and to obtain an optimized upper limit constraint of the forward tilt angle. The collaborative control module is used to apply the upper limit constraint of the forward tilt angle to the assistive chassis control system to correct the track posture in real time, and to carry out collaborative control in combination with the pressure warning signal triggered when the average contact pressure exceeds the elderly riding comfort threshold.

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