An Adaptive Obstacle-Crossing Control Method for an Electric-Driven Cylindrical Wheeled Robot Based on Path Recognition

Abstract

This invention relates to the field of robot control technology, specifically to an adaptive obstacle-crossing control method for an electrically driven column-type wheeled robot based on path recognition, comprising the following steps: S1: Environmental perception and terrain map generation: generating a terrain map; S2: Mode switching and determination: determining the current obstacle type as a platform, slope, or ditch based on the terrain map and a preset wheel-type mode switching threshold; S3: Setting parameters for each mode; S4: Dynamic stability verification and correction preprocessing: assessing obstacle-crossing stability based on the robot's current center of gravity position, and automatically adjusting the settings if there is a risk of tipping over. This invention can accurately identify typical obstacle types such as platforms, ditches, and slopes, and compared to traditional methods that rely on a single sensor or manually preset thresholds, it has higher environmental adaptability and obstacle-crossing scenario judgment accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot control, and in particular to a self-adaptive obstacle crossing control method for an electric-driven columnar wheel-legged robot based on travel path recognition. BACKGROUND

[0002] Wheel-legged hybrid robots are widely used in complex terrain environments for reconnaissance, search and rescue, and surveying tasks due to their high efficiency in moving and strong obstacle crossing ability. However, in actual operation, existing wheel-legged robots generally have the following technical limitations:

[0003] On the one hand, traditional obstacle crossing control methods rely on preset fixed strategies and cannot dynamically identify and switch modes according to the actual distribution characteristics of different types of obstacles (such as steps, trenches, or ramps) in the path. Especially in unstructured terrain, the obstacle type recognition accuracy is insufficient, which can easily lead to failure of obstacle crossing actions or mis-triggering of control instructions.

[0004] On the other hand, obstacle crossing parameters are generally fixed or manually adjusted, such as setting a uniform foot end lifting height and front wheel angle, which lacks real-time analysis of key physical properties such as terrain slope and surface roughness, resulting in a mismatch between control parameters and the environment, affecting the success rate of obstacle crossing and operational stability.

[0005] In addition, the posture stability control has the disadvantage of passive response. When the robot has a centroid shift or an imbalance in the support structure during obstacle crossing, the traditional system usually relies on global re-planning or delayed response mechanisms, which makes it difficult to make local posture corrections in a timely manner, increasing the risk of operation. SUMMARY

[0006] The present application provides a self-adaptive obstacle crossing control method for an electric-driven columnar wheel-legged robot based on travel path recognition, which combines multi-source environment perception, path obstacle recognition, and dynamic control parameter adjustment to improve the intelligent response capability and obstacle crossing safety of wheel-legged robots in complex paths.

[0007] A self-adaptive obstacle crossing control method for an electric-driven columnar wheel-legged robot based on travel path recognition, comprising the following steps:

[0008] S1: Environment perception and terrain map generation: Real-time acquisition of three-dimensional terrain feature parameters of the travel path through front-looking laser radar and foot end pressure sensors, including obstacle height distribution, surface roughness, and slope change, and combining visual recognition to perceive the height of the front step or the width of the trench, dynamically generating a terrain map for determining whether to enter the step climbing, trench crossing, or ramp control mode;

[0009] S2: Mode switching and determination: According to the terrain map, combined with the preset wheel-foot mode switching threshold, it is determined that the current obstacle type is climbing, slope or crossing ditch;

[0010] S3: Parameter setting of each mode:

[0011] In the slope mode, the dynamic friction coefficient of each foot end and the slope ground is calculated based on the slope and surface roughness, and the foot end pressure safety threshold is set combined with the target action mode:

[0012] In the climbing mode, wheel-foot lifting and propulsion instructions are generated based on the terrain map to control the hydraulic cylinder, and the front wheel support height is lifted first to make the wheel-foot lift and stretch in turn to climb the platform;

[0013] In the crossing ditch mode, wheel-foot rotation propulsion instructions are generated based on the terrain map, the front wheel opening angle is expanded to more than 50 degrees, and the wheel-foot rotates in turn to cross the obstacle;

[0014] S4: Dynamic stability check and correction preprocessing: According to the current center of gravity position of the robot, the obstacle crossing stability is evaluated, and if there is a risk of overturning, the setting is automatically adjusted.

[0015] Optionally, the S1 specifically includes:

[0016] S11, three-dimensional point cloud modeling of the advancing path is performed through the front-looking laser radar, the obstacle height distribution and slope change are extracted, and the terrain profile is updated in real time through the point cloud registration algorithm;

[0017] S12: The contact force signals of each wheel-foot and the ground are collected through the foot end pressure sensor, the surface roughness is calculated based on the contact force variance, and when the contact force variance exceeds the preset roughness threshold, it is determined that the current area is a high roughness terrain;

[0018] S13: The geometric features of the front step or trench are identified through the binocular vision sensor, the step height or trench width is measured at the sub-pixel level, and the step / trench boundary is segmented based on the edge detection algorithm.

[0019] Optionally, the S1 further includes multi-source data fusion of the obstacle height distribution and slope change parameters extracted by the laser radar, the surface roughness calculated by the foot end pressure sensor, and the step height or trench width measured by the vision sensor, to generate a three-dimensional raster map including elevation, roughness and obstacle type as the terrain map.

[0020] Optionally, in the S2: according to the comparison results of the ratio of the obstacle height in the terrain map to the maximum lifting height of the robot wheel-foot, the ratio of the trench width to the maximum span of the wheel-foot, and the slope to the overturning threshold of the wheel-foot, the climbing, crossing ditch or slope mode is triggered dynamically.

[0021] Optionally, in the S2:

[0022] If the obstacle is identified as a "ramp-type" obstacle, a normal passing process is started;

[0023] If the obstacle is identified as a "ramp-type" obstacle, a normal passing process is started;

[0024] If the obstacle is identified as a "ramp-type" obstacle, a normal passing process is started.

[0025] Optionally, the parameter setting in the ramp mode comprises:

[0026] According to the slope angle and surface roughness level in the terrain map, the real-time friction coefficient of each foot end with the ramp ground is calculated based on a preset friction coefficient mapping table ;

[0027] Based on the friction coefficient and the current load weight of the robot, the minimum anti-skid pressure threshold of each wheel foot foot end is calculated through a statics balance equation , represents the minimum anti-skid pressure threshold of the i-th wheel foot foot end, and a wheel foot foot end pressure safety threshold is set.

[0028] Optionally, the parameter setting in the ramp mode comprises:

[0029] According to the step height H in the terrain map, a wheel foot lifting propulsion instruction is generated, and a hydraulic cylinder is controlled to press , is a lifting margin, the front wheel support height is lifted, and simultaneously based on the centroid projection position of the robot, the lifting timing of the rear wheel foot is planned, so that the wheel foot climbs over the step in the action sequence of "front wheel lifting → front wheel stretching → rear wheel following".

[0030] Optionally, the parameter setting in the ramp mode comprises:

[0031] According to the trench width W in the terrain map, a wheel foot rotation propulsion instruction is generated, and the front wheel opening angle is controlled to expand, and based on a kinematics inverse solution module, the rotation angle and propulsion speed of each wheel foot are calculated, so that the wheel foot performs the crossing action of "front wheel rotating to cut into the trench edge → middle and rear wheel synchronous propulsion → front wheel resetting" in turn.

[0032] Optionally, the S4 comprises constructing a support polygon projection boundary according to the position coordinates and contact state of each wheel foot at present, and acquiring the gravity center position coordinates in real time through a robot centroid sensor; if the gravity center position coordinates exceed the support polygon projection boundary, it is judged that there is a risk of overturning.

[0033] Optionally, in the S4, after determining the overturning risk, a buffer support section is inserted in the preset action sequence, at least one pair of diagonal wheels is controlled to synchronously touch the ground and increase the foot end pressure until the center of gravity returns to the support polygon projection boundary.

[0034] Advantages of the present application:

[0035] The present application fuses laser radar three-dimensional point cloud, foot end pressure signal and binocular vision image, constructs a multi-source three-dimensional terrain atlas with elevation, slope, roughness and geometric structure information, can accurately identify typical obstacle types such as climbing, crossing ditch and slope, and has higher environmental adaptability and obstacle scene judgment accuracy compared with traditional methods relying on only a single sensor or manually preset threshold.

[0036] The present application compares the obstacle geometric features with the robot threshold value, automatically triggers the corresponding action mode, and sets exclusive parameters for different modes: such as dynamically estimating the friction coefficient based on the slope-roughness mapping table, deriving the anti-skid pressure threshold according to the static force model, calculating the opening angle and forward distance, etc. This mechanism ensures the accurate correspondence between the control strategy and the terrain features, improves the stability and energy consumption control efficiency of obstacle crossing.

[0037] The present application introduces a support polygon projection construction and torque balance evaluation mechanism, combined with real-time center of mass coordinate monitoring, automatically inserts a buffer support section or adjusts the hydraulic pressure distribution when detecting an overturning risk, ensuring stable control of the overall posture of the robot during obstacle crossing. This strategy enhances the redundancy fault tolerance capability in multi-legged coordinated action, reduces the probability of obstacle crossing failure caused by sudden terrain changes or action interference. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0039] Fig. 1 The control method flowchart of the embodiment of the present application is shown in the figure.

[0040] Fig. 2 The mode switching and determination schematic diagram of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0041] The application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to describe the embodiments in more detail, and are not intended to specifically limit the application.

[0042] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiment can include a specific feature, structure or property, but not necessarily every embodiment includes the specific feature, structure or property. In addition, when a specific feature, structure or property is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to implement such a feature, structure or property in combination with other embodiments (whether or not explicitly described).

[0043] Generally, the terms can be understood at least in part from the context of their use. For example, depending on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular or can be used to describe combinations of features, structures, or characteristics, whether large or small, whether related or unrelated to each other. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but can instead, at least in part, depend on the context, allowing the presence of other factors not necessarily explicitly described.

[0044] As shown in Figs. 1-2 A method for adaptive obstacle crossing control of an electric-driven columnar-legged robot based on travel path recognition, comprising the following steps:

[0045] S1: Environment perception and terrain map generation: Real-time acquisition of three-dimensional terrain feature parameters of the travel path by front-looking laser radar and foot pressure sensor, including obstacle height distribution, surface roughness and slope change, and combining with visual recognition to perceive the front step height or groove width, dynamically generating a terrain map for determining whether to enter the step climbing, groove crossing or slope control mode;

[0046] S2: Mode switching and determination: According to the terrain map, in combination with the preset wheel-foot mode switching threshold, determine the current obstacle type as step climbing, slope or groove crossing; The wheel-foot mode switching threshold includes the ratio threshold of the obstacle height to the maximum lifting height of the robot wheel-foot, the ratio threshold of the groove width to the maximum span of the wheel-foot, and the wheel-foot overturning threshold;

[0047] S3: Parameter setting for each mode:

[0048] In the slope mode, the dynamic friction coefficient of each foot end and the slope ground is calculated based on the slope and surface roughness, and the foot pressure safety threshold is set in combination with the target action mode:

[0049] In the climbing mode, a wheel lifting and propulsion command is generated based on the terrain map, which controls the hydraulic cylinder to prioritize raising the height of the front wheel support, so that the wheel feet are raised and extended in sequence to climb the platform.

[0050] In the obstacle crossing mode, the wheel and foot rotation propulsion command is generated based on the terrain map. The front wheel angle is extended to more than 50 degrees, and the wheel and foot rotate in sequence to propel across the obstacle.

[0051] S4: Dynamic stability check and correction preprocessing: Based on the robot's current center of gravity position, assess obstacle crossing stability. If there is a risk of tipping over, automatically adjust the settings.

[0052] S1 specifically includes:

[0053] S11, using forward-looking lidar with The scanning frequency is used to model the 3D point cloud of the travel path and extract the height distribution of obstacles. and slope variation parameters The iterative nearest point registration (ICP) method is used to register point clouds in adjacent frames and update the terrain contour in real time.

[0054] ,in, This represents the point cloud acquired by the lidar at the current moment. This represents the point cloud at the previous time step. This represents the iterative nearest point algorithm. This represents the registered point cloud, used to construct a continuous terrain outline.

[0055] S12 collects the contact force signals between each wheel and the ground through foot pressure sensors. Calculate its variance over a time window. As a criterion for judging surface roughness:

[0056] ;

[0057] When the following conditions are met: If so, the current location is determined to be high-roughness terrain, where, This represents the contact force of the i-th wheel foot at time t. This represents the average contact force, and N represents the number of sampling points. This represents the roughness judgment threshold, with a value range of 15~25 N².

[0058] S13 identifies the outline of obstacles ahead using a binocular vision sensor and extracts the boundaries of steps or trenches using edge detection and sub-pixel precise localization algorithms, calculating their geometric features:

[0059] Step height Calculated from the depth difference of stereo matching of images;

[0060] Trench width Derived from the difference between left and right edge positions.

[0061] , ;

[0062] wherein, are the depth values of the upper and lower edges of the step in the image depth map, are the lateral positions of the trench edges in the image coordinate system.

[0063] S14, multi-source fusion of foot end pressure sensor vision recognition to construct a three-dimensional grid terrain map using a weighted fusion mechanism :

[0064] wherein each grid contains the elevation, slope, roughness and obstacle geometry attributes of the local terrain.

[0065] In S2, according to the ratio of each parameter in the terrain map to the robot capability boundary, the obstacle type is judged and the control mode is dynamically triggered:

[0066] If: the climb mode is triggered;

[0067] If: the cross trench mode is triggered;

[0068] If: the slope mode is triggered;

[0069] wherein, is the maximum lifting height of the wheel-foot, is the maximum span of the wheel-foot, is the maximum anti-overturning safety slope threshold of the robot, set to 25°~28°, is the control mode trigger threshold, set to 0.8, i.e. the climb mode is triggered when the step height reaches 80% of the maximum lifting capacity, set to 0.7, i.e. the cross trench mode is triggered when the trench width reaches 70% of the maximum span.

[0070] S3 specifically includes:

[0071] Slope mode parameter setting:

[0072] S31, according to the slope angle and the surface roughness level querying and dynamically obtaining the real-time friction coefficient between the foot end and the ground from a preset friction coefficient mapping table:

[0073] wherein, is the friction coefficient between the foot end and the ground, represents a mapping table of slope and roughness to friction coefficient, is the contact surface roughness level.

[0074] S32, based on the obtained friction coefficient and the current load mass M of the robot, the minimum anti-skid pressure threshold of each foot end is calculated according to a statics balance model:

[0075] ;

[0076] The actual foot end pressure safety threshold is set as: ;

[0077] wherein, represents the minimum anti-skid pressure of the i th wheel foot, is the corresponding safety foot end support pressure threshold, M is the total mass of the robot, g is the acceleration of gravity, and n is the number of force-receiving wheel feet, is the current slope, is the safety margin coefficient.

[0078] The preset friction coefficient mapping table is used to estimate the friction coefficient according to the slope angle and the surface roughness level , and the content structure is as follows:

[0079] Discretization of input parameters:

[0080] Slope angle : divided into six grades of 5°, 10°, 15°, 20°, 25° and 30°;

[0081] Surface roughness : divided into three grades according to the contact force variance:

[0082] Low roughness N ; medium roughness N ; high roughness N ;

[0083] Table 1 preset friction coefficient mapping table

[0084]

[0085] The static equilibrium model calculates the minimum anti-skid pressure threshold of each foot end as follows:

[0086] a Basic settings:

[0087] The terrain is a slope with a slope of ; the total mass of the robot is M, the acceleration of gravity is g; the number of foot ends is n, which are uniformly stressed; the friction coefficient is (by the aforementioned mapping table);

[0088] b Force analysis: To prevent sliding, the pressure provided by each wheel foot should satisfy the friction force balance of the sliding force:

[0089] , represents the friction force required by the i-th wheel foot in the step mode to prevent sliding;

[0090] Therefore, the minimum anti-skid pressure is: ;

[0091] And set the upper limit of the safety pressure: ; if the slope increases or the roughness decreases (resulting in decreases), then automatically increases to avoid wheel foot sliding or overloading, ensuring stability and execution efficiency in the step mode.

[0092] Climbing table mode parameter setting:

[0093] S33, according to the step height H extracted from the terrain map, generate wheel foot lifting and propulsion instructions, set the lifting height as: ;

[0094] At the same time, based on the projection position of the robot centroid , the timing of lifting and propulsion of the rear wheel is planned, forming the following action sequence:

[0095] 1. The front wheel is lifted to ;

[0096] 2. The front wheel is extended to the step surface;

[0097] 3. The rear wheel follows and climbs.

[0098] Where H is the actual height of the step, is the lifting margin, is the target lifting height, is the lifting margin ratio, is the position of the robot centroid, obtained by the robot centroid sensor.

[0099] Cross ditch mode parameter setting:

[0100] S34, calculate the required front wheel splay angle according to the groove width W in the topographic map Satisfy:

[0101] , and ; and call the inverse kinematics module to calculate the rotation angle of each wheel foot and the propulsion speed , generate the following phased actions:

[0102] 1. The front wheel rotates to cut into the edge of the groove;

[0103] 2. The middle and rear wheels are propelled synchronously;

[0104] 3. The front wheel is reset to restore the default posture.

[0105] Where W is the groove width, L is the wheel foot length, is the front wheel splay angle, , is the rotation angle of the i-th wheel foot, is the propulsion speed of the i-th wheel foot, is the minimum splay angle of the crossing action (about 50°).

[0106] The inverse kinematics module is specifically used to convert control intentions (such as wheel foot position, splay angle, propulsion speed) into actual executable driving instructions, and the core includes forward kinematics modeling, Jacobian matrix construction and inverse solution calculation.

[0107] 1. Establish a wheel foot structure model

[0108] Each wheel foot model includes:

[0109] Swivel joint (for splay angle expansion);

[0110] Telescopic or sliding rail joint (for forward / retracting);

[0111] The support arm end effector position is the end pose .

[0112] 2. Forward kinematics expression: find the end position through the joint angles

[0113] ;

[0114] 3. Solve the inverse solution: let the target position be , the actual value is: ;

[0115] If it is a redundant system, use the pseudo-inverse solution: ;

[0116] where​ is Jacobian matrix, is Moore-Penrose pseudo-inverse, is end-effector desired velocity, is joint command velocity.

[0117] 4. Implementation steps:

[0118] Generate desired end-effector trajectory according to path planning;

[0119] Calculate Jacobian and its pseudo-inverse in real time;

[0120] Output corresponding joint angles and velocities to control the driver to achieve precise motion.

[0121] S4 is as follows:

[0122] S41, support polygon projection boundary construction:

[0123] According to the position coordinates of the i th wheel-foot at the current time and the contact state flag , 1 for contact, 0 for suspension, construct the effective support foot end set: ;

[0124] Construct the support polygon boundary by the convex hull algorithm , and get the current center of gravity coordinates of the robot .

[0125] S42, attitude stability determination: if , it is determined that there is a risk of overturning.

[0126] S43, if the center of gravity is detected to be out of bounds, execute the buffer support correction:

[0127] Insert a buffer support segment in the current motion sequence, control a pair of diagonal wheel feet to land synchronously and exert enhanced pressure: , until , where is the buffer support intensity coefficient, is the support force of the i th wheel-foot, is the safety foot end support pressure threshold.

[0128] In the process of robot obstacle crossing, in order to evaluate its attitude stability, it is necessary to determine its support area boundary according to the position of the wheel foot in contact with the ground, and the specific method is as follows:

[0129] Contact foot end screening: first, according to the state of the foot end contact sensor, screen out all the wheel feet in contact with the ground at the current time, and extract the coordinate point set of its projection plane (x-y plane) on the ground;

[0130] Convex hull boundary construction: take the above foot end coordinate points as input, adopt two-dimensional convex hull algorithm to fit the boundary, the convex hull algorithm finds a smallest closed polygon, so that all contact points are located on the boundary of the polygon or inside, and the boundary line does not intersect.

[0131] Boundary polygon formation: the obtained convex hull point set is sorted in clockwise or counterclockwise order to form the vertex sequence of the support polygon, thereby defining the geometric boundary of the leg support surface on the ground.

[0132] Centroid position judgment: by comparing with the projection position of the current centroid of the robot on the ground, it is judged whether the centroid falls within the support polygon range, which is used as the basis for judging the overturning risk.

[0133] The present application encompasses any alternative, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0134] The above is only the preferred embodiment of the present application, it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A self-adaptive obstacle crossing control method for an electric-driven columnar wheel-foot robot based on travel path recognition, characterized in that, The method comprises the following steps: S1: environment perception and terrain map generation: real-time collection of three-dimensional terrain feature parameters of the travel path by the front-looking laser radar and the foot end pressure sensor, including obstacle height distribution, surface roughness and slope change, and combination of visual recognition to perceive the front step height or groove width, dynamic generation of a terrain map for determining whether to enter the climb, cross or slope control mode; S2: mode switching and determination: according to the terrain map, in combination with the preset wheel-foot mode switching threshold, determining that the current obstacle type is a climb, slope or cross ditch; S3: parameter setting of each mode: In the slope mode, the dynamic friction coefficient of each foot end and the slope ground is calculated based on the slope and surface roughness, and the foot end pressure safety threshold is set in combination with the target action mode: In the climb mode, the wheel-foot lifting and advancing instructions are generated based on the terrain map to control the hydraulic cylinder, and the front wheel support height is preferentially lifted to make the wheel-foot sequentially lifted and stretched to climb the steps; In the cross ditch mode, the wheel-foot rotation advancing instructions are generated based on the terrain map to expand the front wheel opening angle, and the wheel-foot sequentially rotates and advances to cross the obstacle; S4: dynamic stability check and correction preprocessing: according to the current center of gravity position of the robot, the obstacle crossing stability is evaluated, and if there is a risk of overturning, the setting is automatically adjusted; The S1 further comprises multi-source data fusion of the obstacle height distribution extracted by the laser radar, the slope change parameter, the surface roughness calculated by the foot end pressure sensor, and the step height or groove width measured by the visual sensor to generate a three-dimensional grid map including elevation, roughness and obstacle type as the terrain map; The parameter setting in the climb mode comprises: According to the step height H in the terrain map, a wheel foot lifting propulsion instruction is generated to control the hydraulic cylinder to lift the robot according to the step height H in the terrain map , The lifting margin is used to lift the front wheel support height, and the lifting timing of the rear wheel foot is planned based on the projection position of the robot centroid, so that the wheel foot climbs over the step in the action sequence of "front wheel lifting → front wheel stretching → rear wheel following".

2. The adaptive obstacle crossing control method for the electrically driven columnar-legged robot based on the identified travel path according to claim 1, characterized in that, The S1 specifically comprises: S11: three-dimensional point cloud modeling of the travel path by the front-looking laser radar to extract the obstacle height distribution and slope change, and real-time updating of the terrain profile by the point cloud registration algorithm; S12: acquisition of the contact force signal of each wheel-foot and the ground by the foot end pressure sensor, calculation of the surface roughness based on the contact force variance, and determination that the current area is a high roughness terrain when the contact force variance exceeds the preset roughness threshold; S13: identification of the geometric features of the front step or groove by the binocular visual sensor, sub-pixel level measurement of the step height or groove width, and segmentation of the step / groove boundary based on the edge detection algorithm. 3.The adaptive obstacle-surmounting control method for the electrically-driven columnar wheel-legged robot based on the identified travel path according to claim 1, wherein, In the S2: according to the comparison results of the ratio of the obstacle height in the terrain map to the maximum lifting height of the robot wheel-foot, the ratio of the groove width to the maximum span of the wheel-foot, and the slope to the overturning threshold of the wheel-foot, the climb, cross ditch or slope mode is dynamically triggered.

4. The adaptive obstacle crossing control method for the electrically driven columnar-legged robot based on the identified travel path according to claim 3, characterized in that, In the S2: If it is identified as a "slope type" obstacle, the normal passing process is started; If it is identified as a "step type" obstacle, the climb action process is started; If it is identified as a "groove type" obstacle, the cross ditch action process is started.

5. The adaptive obstacle crossing control method for the electrically driven columnar wheel-legged robot based on the identified traveling path according to claim 1, characterized in that, The parameter setting in the slope mode comprises: According to the slope angle and the surface roughness grade in the topographic map, based on a preset friction coefficient mapping table, the real-time friction coefficient of each foot end with the slope surface is calculated ; Based on the friction coefficient The minimum slip prevention pressure threshold of each wheel-foot end is calculated by the static equilibrium equation with the current load weight of the robot , represents the minimum slip prevention pressure threshold of the i-th wheel-foot end, and sets the wheel-foot end pressure safety threshold.

6. The adaptive obstacle crossing control method for the electrically driven columnar-legged robot based on the identified travel path according to claim 1, characterized in that, The parameter setting in the cross ditch mode comprises: According to the groove width W in the terrain map, the wheel-foot rotation advancing instructions are generated to control the front wheel opening angle to expand, and the rotation angle and advancing speed of each wheel-foot are calculated based on the inverse kinematics module to make the wheel-foot sequentially perform the "front wheel rotation cutting into the groove edge → middle and rear wheel synchronous advancing → front wheel resetting" crossing action.

7. The adaptive obstacle crossing control method for the electrically driven columnar-tripod robot based on the identified travel path according to claim 1, characterized in that, The S4 includes constructing a support polygon projection boundary according to the position coordinates and contact state of the current each wheel foot, and acquiring the barycenter position coordinates in real time through the robot barycenter sensor; if the barycenter position coordinates exceed the support polygon projection boundary, it is judged that there is a risk of overturning. 8.The adaptive obstacle-surmounting control method for the electrically-driven columnar-fect robot based on the identified travel path according to claim 7, wherein, In the S4, after it is judged that there is a risk of overturning, a buffer support section is inserted in the preset action sequence, at least one pair of diagonal wheel feet is controlled to land synchronously and increase the foot end pressure, until the barycenter returns to the support polygon projection boundary.

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