Single-leg fault hexapod robot path planning method based on risk degree model

Abstract

The invention belongs to the field of robot path planning. In order to solve the problems that an existing path planning method is insufficient in adaptability when a single leg of a hexapod robot fails, and terrain constraints and motion ability are not fully coupled, the invention provides a hexapod robot path planning method for a single leg failure fault based on a risk degree model. The method specifically comprises the steps that firstly, the extreme movement capacity of the robot in slopes, obstacles and gully terrains in a left front leg / left middle leg failure mode is analyzed, secondly, according to the extreme movement capacity and terrain geometric constraints, a risk degree model of coupling of the terrain geometric constraints and fault states is designed, and a risk degree map is formed; robot motion threat degrees are divided; and finally, designing an improved A * algorithm based on the obtained risk degree model, taking the risk degree as a path cost core index, realizing global safe path planning of the fault hexapod robot in a multi-terrain superposition scene, and remarkably improving environmental adaptability and task continuity.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and particularly to a path planning method for a hexapod robot in a single-leg failure state under ramp, obstacle, and gully terrains. This method calculates the limit motion parameters in the failure state, designs a risk model under the fusion of terrain geometric constraints and limit motion capabilities, constructs a risk map based on the obtained risk model, quantifies the threat level, and designs an improved A* algorithm for path planning of the failed hexapod robot. Through this method, the present invention can effectively improve the path planning ability, stability, and autonomous task execution efficiency of the hexapod robot in the failure state, ensuring that the robot can still maintain high-efficiency movement and task execution capabilities in a complex environment. Background Art

[0002] Hexapod robots are widely used in tasks such as exploration, rescue, and patrol in dangerous environments due to their high stability and adaptability. However, hexapod robots may encounter failures such as single-leg failure during task execution. For example, when walking on various complex terrains, the load and walking pressure on each leg of the hexapod robot will increase, which may trigger sudden failures. This will seriously affect the stability, movement ability, and load distribution of the robot, resulting in the robot losing its walking ability. Existing path planning methods for hexapod robots usually assume that the robot is in a normal working state for path optimization, without fully considering the impact of leg failures on the robot control system and path planning. Therefore, when a leg failure occurs, existing path planning algorithms often cannot quickly adapt, leading to the robot being unable to continue the task.

[0003] Although some path planning algorithms, such as D* algorithm, RRT (Rapidly-Exploring Random Tree) algorithm, PRM (Probabilistic Roadmap) algorithm, etc., have been widely applied to path optimization and local obstacle avoidance of hexapod robots, most of these algorithms assume that the robot is in a normal working state during task execution, without fully considering the failure conditions of the robot, especially the impact of leg failures (such as single-leg or double-leg failure) on the movement ability, stability, and path planning of the robot. Existing path planning methods usually rely on all the motion degrees of freedom of the robot and ideal control inputs, ignoring the influencing factors when the robot has leg failures. Therefore, these algorithms often cannot effectively adjust the path planning when the robot loses some of its motion abilities, which may lead to the robot being unable to continue the task or even being completely disabled. Different from these traditional methods, the A* algorithm, as a classic global path planning algorithm, has strong flexibility and adjustability. By appropriately modifying the A* algorithm, the path can be adjusted in real time according to the failure state of the robot. The A* algorithm can comprehensively consider the current state of the robot and the impact of failures on the hexapod robot, so as to plan an optimal path that can avoid obstacles and adapt to the failure state. Summary of the Invention

[0004] To address the deficiency in path planning under fault conditions in the prior art, the present invention proposes a path planning method for a hexapod robot with a single-leg failure based on a risk model. First, analyze the extreme motion capabilities of the faulty hexapod robot on slopes, obstacles, and gully terrains when the left front leg or the left middle leg fails. Then, design a risk model that integrates terrain geometric constraints and extreme motion capabilities. By combining the extreme motion capabilities and terrain geometric features under two types of faults, calculate the ratio of the average values of the slope gradient, obstacle height, and gully width between different points to the extreme motion parameters, obtain the risk between different central nodes of adjacent grids, and numerically label according to the calculated risk. Construct a risk map and divide the threat level to the faulty hexapod robot. Subsequently, design an improved A* algorithm. Combine the obtained risk map, select the grid central node path with the minimum local comprehensive risk, and converge to form a global path with the lowest risk, enabling the hexapod robot to quickly perform path planning on three typical terrains when a fault occurs and ensuring that the robot can continue to execute tasks in a complex environment.

[0005] The technical innovation of the present invention lies in proposing an innovative method for establishing a risk model based on terrain geometric constraints and extreme motion performance under fault conditions, and designing a path planning method based on this model to achieve a balanced decision-making of global safety and efficiency under fault conditions. This method effectively solves the problem of insufficient adaptability of traditional planning methods under the coupling effect of faults and terrains by deeply integrating the redundant characteristics of the robot body and the environmental interaction characteristics, providing new technical support for the reliable operation of hexapod robots in high-risk and complex environments.

[0006] The present invention is realized through the following technical solutions:

[0007] Step 1: Regarding the different extreme safety pitch angles, extreme step heights, and extreme step lengths of the hexapod robot when different types of faults occur, according to the symmetric structure of the hexapod robot, analyze the extreme safety pitch angle, extreme step height value, and extreme step length value of the hexapod robot on slopes, obstacles, and gully terrains when the left front leg or the left middle leg fails; The extreme safety pitch angle, extreme step height value, and extreme step length value of the robot;

[0008] Step 2: Propose a risk model under the fusion of terrain geometric constraints and extreme motion ability. By combining terrain geometric features with extreme motion ability under two types of faults, calculate the ratio of the average values of slope gradient, obstacle height, and gully width between different points to extreme motion parameters, obtain the risk between different central nodes of adjacent grids, and perform numerical annotation according to the calculated risk, construct a risk map, and divide the threat level to the faulty hexapod robot;

[0009] Step 3: Through the obtained terrain risk model, design an improved A* algorithm, where "risk" is the core factor of path cost. Combining the obtained risk map, select the path of the central node of the grid with the minimum local comprehensive risk, and converge to form a global path with the lowest risk, realizing intelligent path planning considering terrain geometric features and the extreme motion ability of the faulty hexapod robot.

[0010] The beneficial effects of the present invention are as follows:

[0011] The described path planning method for a single-leg faulty hexapod robot based on a risk model can effectively improve the path planning ability of the faulty hexapod robot. When a single-leg failure occurs, through the analysis of the extreme motion ability of the hexapod robot in three typical terrains under different fault types, design a risk model under the fusion of terrain geometric constraints and extreme motion ability, and use an improved A* algorithm to perform path planning for the faulty hexapod robot to ensure that the robot can maintain a relatively stable gait and motion performance in typical terrains, thereby improving the task execution ability of the robot. Description of the Drawings

[0012] Figure 1 It is a flowchart of risk division for different typical terrains coupled with two types of faults

[0013] Figure 2 It is a flowchart of the path planning algorithm for a faulty hexapod robot based on a risk model

[0014] Figure 3 It is a schematic diagram of the improved A* algorithm Detailed Embodiment

[0015] The following further describes the embodiments of the present invention in detail with reference to the drawings and embodiments.

[0016] An embodiment of the present invention: A path planning method for a single-leg faulty hexapod robot based on a risk model mainly includes the following contents:

[0017] (1) The model adopted is a rectangular hexapod robot configuration. The legs are numbered counterclockwise as legs 1 - 6. Compared with other configurations, the probability of failure of this rectangular hexapod robot is the same, so the control method used is universal. Initialize the joint angles and foot end position information, and start the virtual simulation environment. This method is verified by Gazebo simulation. By constructing a rectangular hexapod robot model and loading the URDF (Unified Robot Description Format) file in Gazebo, the geometric structure, joints, and their physical properties of the robot are successfully defined. The model consists of six legs, each leg is composed of a hip joint, a knee joint, and an ankle joint, and the joint angles are determined by inverse kinematics calculations. By defining the body coordinate system and the leg movement trajectory, using the physical engine of Gazebo and the ROS framework, the low - level motion control of the robot is achieved. To derive the robot's motion trajectory, first initialize the joint angles of the robot, and combine inverse kinematics to derive the foot end position and trajectory in the normal state. This process ensures that the robot can walk stably according to the set gait, and all leg movements comply with physical constraints. During the path planning process, the improved A* algorithm is combined for global path optimization to ensure that the faulty hexapod robot can avoid obstacles and move forward stably on three typical terrains. This simulation test provides strong support for the comprehensive risk assessment and path planning of faulty hexapod robots in practical applications.

[0018] When the hexapod robot walks on different typical terrains, due to the many uncertain factors in typical terrains, it will be in danger when walking. Therefore, various typical terrains are analyzed first, and the risk levels under different typical terrains are calculated respectively. For typical terrains, this study is divided into three types: slope terrain, bumpy terrain, and gully terrain. For the analysis of terrains, we usually use DEM data to make judgments. DEM is a discrete digital method to represent terrain, defining a set of three - dimensional vectors to describe the position and elevation information of a series of discrete points in the terrain: {H i,j =(x i,j ,y i,j ,z i,j ),i,j=1,2,3,...,N} (1)

[0019] In the formula: (x i,j ,y i,j ) represents the position coordinates of the point, and z i,j is the elevation information of the corresponding point.

[0020] The most widely used DEM is the rectangular grid. Since it is the same as the data storage format of the computer, only the elevation information value of the data points needs to be stored, which greatly reduces the data storage volume. However, for path planning, DEM data cannot be directly used in the path planning problem of the hexapod robot. It is meaningless to evaluate the characteristics of each point for a very high-resolution DEM. Therefore, it is necessary to rasterize the DEM according to the actual size of the hexapod robot in the planning.

[0021] The common DEM range for path planning is 4 km × 4 km, the unit grid is 10 m × 10 m, and the size of the hexapod robot is 1 m. Therefore, it is not necessary to consider the small-range movement ability of the hexapod robot. Only the constraint relationship between the hexapod robot and the terrain needs to be considered, and at the same time, it is considered for the subsequent behavior mode planning of the hexapod robot.

[0022] For various typical terrains, hexapod robots with different fault types will have different limit values for corresponding parameters. For example, for the slope terrain, the limit parameter is the pitch angle of the hexapod robot; for the gully terrain, the limit parameter is the step length of the hexapod robot; for the obstacle terrain, the limit parameter is the step height of the hexapod robot. (1) Calculation of the limit parameter for the slope terrain: Let δ max be the limit pitch angle of the faulty hexapod robot, and the calculation formula is: where: h c is the height of the robot's center of mass, and r s is the radius of the circumcircle of the support polygon. (2) Calculation of the limit parameter for the obstacle terrain: The calculation of the limit step height for crossing obstacles needs to comprehensively consider the kinematic constraints of the hexapod robot in the current fault state, the stability in the fault state, and the joint driving ability, that is: where: H max is the limit crossing height, τ max is the torque constraint of the corresponding faulty leg, η is the transmission efficiency in the current fault state, and mg is the gravity of the robot. (3) Calculation of the limit parameter for the gully terrain: Select the analysis when the stability margin is the lowest in each state to ensure the safety and stability of the platform during the movement. When the hexapod robot moves forward at a constant speed, calculate the limit step length: where: I max is the limit step length of the faulty hexapod robot, W is the body width, L is the distance between adjacent foot ends when standing still, and the choice of x is related to the fault-tolerant gait type corresponding to the fault. First, calculate the expected stability margin of the fuselage: Where: A is the end of the left front leg of the faulty hexapod robot, and B is the end of the right middle leg of the faulty hexapod robot. For the stability margin SSM of the actual situation, it is the distance d between the center of gravity M of the faulty hexapod robot after moving x at the yaw angle θ and the polygon formed by the supporting feet. The key constraint is that the actual stability margin needs to satisfy SSM ≥ SSM * , that is: Since the extreme motion ability is being solved, the inequality sign is taken as an equality to solve for x, and we get: Substitute x into the original extreme step length formula, and we can obtain the final expression of the extreme ditch-crossing step length based on the faulty state: Here, the constraint of the unknown x is eliminated, reducing the uncertainty in the original formula. Substitute the yaw angle, body width of the actual hexapod robot in the faulty state, and the distance between adjacent foot ends when standing still, and the extreme ditch-crossing step length of the robot can be calculated.

[0023] Since the terrain is diverse when the hexapod robot walks, the influence of slope, terrain roughness, and obstacle height needs to be considered during terrain analysis. For the analysis of the danger level of different typical terrains: 1) For the slope terrain, we can describe it with the slope. The magnitude of the slope represents the angle between the tangent plane at a certain point and the horizontal plane. Let the surface expression of the slope terrain be: z = f(x, y). Then, for any point on the slope terrain surface, the angle between its normal vector and the vertical direction is the slope, and the calculation formula for the slope is: where f x (x, y) and f y (x, y) are the projection values of this point on the x-axis and y-axis respectively. 2) For the obstacle terrain, for the hexapod robot, when walking, there will be obstacles similar to steps distributed on the road surface, which will also affect the walking of the hexapod robot. Use the depth value D(x, y) of the corresponding pixel point in the obstacle depth map (or point cloud data) of the obstacle area and the sensor installation height h sensor to define the undulation degree β obstacle (x, y) of the obstacle terrain: β obstacle (x, y) = h sensor - D(x, y) (10) 3) For gully terrain, in addition to the slope terrain affecting the walking of the hexapod robot, the gully spacing also has a great impact on the walking of the hexapod robot. The gully spacing refers to the horizontal distance between the two endpoints on both sides of the gully. The larger the value, the longer the gully spacing; the smaller the value, the shorter the gully spacing. Mathematically, the definition of the gully spacing can be calculated using the position coordinates of the two endpoints of the gully: Where: (x b , y b ) is the horizontal maximum distance coordinate of the robot from the gully edge, and (x a , y a ) is the horizontal minimum distance coordinate of the robot from the gully edge.

[0024] (2) The influencing factors of terrain danger degree mainly include slope terrain, obstacle terrain and gully terrain. When driving beyond the maximum climbing angle, it will cause the hexapod robot to capsize. When the hexapod robot walks on the obstacle terrain, it will hinder the progress of the hexapod robot. When crossing the gully, the hexapod robot will fall into the gully and cannot pass. Therefore, when planning the driving path of the hexapod robot, the influence of these terrain geometric factors should be fully considered, and according to the walking ability of the faulty hexapod robot, the danger degree of the three terrains should be evaluated to obtain the danger degree model under the integration of terrain geometric constraints and limit movement ability.

[0025] When driving on the slope terrain, when the slope exceeds the maximum climbing ability α max of the faulty hexapod robot, it will cause the faulty hexapod robot to be unable to pass. At this time, the cost is the greatest. When the slope is less than the maximum climbing ability, the faulty hexapod robot can pass through this area, but it needs to pay the corresponding cost. The defined slope danger degree is: Where: δ max is the limit safety pitch angle of the faulty hexapod robot, R is the number of typical terrains in the corresponding known terrain in the current distance, and (x1, y1) and (x2, y2) are the coordinates of the central nodes of adjacent grids after rasterization.

[0026] Similarly, the danger degree function of the influence of the obstacle terrain on the performance of the faulty hexapod robot can be obtained as: Where: H max is the limit step height value that the faulty hexapod robot can cross the obstacle.

[0027] Similarly, the danger degree function of the influence of the gully terrain on the safety performance of the faulty hexapod robot can be obtained as: Where: I max is the limit step length of the faulty hexapod robot.

[0028] Mainly analyze the faults of the left front leg and the left middle leg, consider the situations of different fault types under different typical terrains, and construct a risk model under the fusion of terrain geometric constraints and limit movement ability: P = P α (α slope ) + P β (β obstacle ) + P γ (γ gully ) (15)

[0029] Based on the risk model under the fusion of terrain geometric constraints and limit movement ability obtained above, obtain the risk between different central nodes of adjacent grids, numerically label according to the calculated risk, construct a risk map and divide the threat level to the faulty hexapod robot, that is: through the limit movement parameters of the hexapod robot under the current fault type and terrain geometric constraints, when P ∈ (0, 1), it is stipulated that the terrain in the selected path poses a small threat to the movement of the faulty hexapod robot, so walk this path as much as possible; when P ∈ [1, 2), it is stipulated that the terrain in the selected path poses a general threat to the movement of the faulty hexapod robot, and the risk of the corresponding terrain in this path needs to be calculated separately to confirm whether it is close to or reaches the limit value, and this path needs to be carefully selected; when P ∈ [2, ∞), it is stipulated that the terrain in the selected path poses a great threat to the movement of the faulty hexapod robot, and avoid walking this path, as shown in Table 1. Table 1 Relationship between risk and movement threat level of faulty robots

[0030] (3): Start path planning based on the established risk model above. When the hexapod robot fails, comprehensively consider the current fault state and the characteristics of typical terrains, and conduct path planning. Based on the A* algorithm path planning framework, use the heuristic search method to find the optimal path from the starting point to the target point. The general A* algorithm is:

[0031] f(n) = g(n) + h(n) (16)

[0032] Based on the risk model under the fusion of terrain geometric constraints and limit movement ability obtained, correct the dissipation function and estimated cost function of the A* algorithm to obtain the following formula:

[0033] Therefore, the cost function of the finally obtained improved A* algorithm is:

[0034]

[0035] According to the obtained risk model under the integration of terrain geometric constraints and extreme - motion capabilities, the dissipation function and the estimated cost function of the A* algorithm are corrected to obtain the following formula: where: (x n , y n , z n ) is the current position coordinate; (x goal , y goal , z goal ) is the target position coordinate, P n-min is the minimum risk, and P n is the risk value of the current node. And finally, path planning is carried out according to this cost function.

[0036] Through the above method, the path - planning problem of the hexapod robot when a single - leg fault occurs can be solved.

Claims

1. A path planning method for a single - leg - faulted hexapod robot based on a risk - degree model, aiming to improve the robot's environmental adaptability and task continuity in a structurally damaged state. When the hexapod robot moves in a complex unstructured environment, dynamic impact loads are likely to cause leg joint injuries, and single - leg sudden failures are more likely to occur in multi - terrain alternating areas. To address this problem, this study proposes an innovative method of establishing a risk - degree model based on terrain geometric constraints and extreme motion performance under fault conditions, and designs a path planning method based on this model to achieve a balanced decision - making of global safety and efficiency under fault conditions. By deeply integrating the redundant characteristics of the robot body and the environmental interaction characteristics, this method effectively solves the problem of insufficient adaptability of traditional planning methods under the coupled action of faults and terrain, providing new technical support for the reliable operation of hexapod robots in high - risk complex environments. Its specific implementation can be divided into the following steps: Step 1: Considering the different extreme safe pitch angles, extreme step heights, and extreme step lengths of the hexapod robot when different fault types occur, according to the symmetric structure of the hexapod robot, analyze the extreme safe pitch angle, extreme step height value, and extreme step length value of the hexapod robot on slopes, obstacles, and gully terrains when the left front leg or the left middle leg fails. Step 2: Propose a risk - degree model under the fusion of terrain geometric constraints and extreme motion ability. By combining the terrain geometric characteristics with the extreme motion ability under two fault types, calculate the ratio of the average value of the slope gradient, obstacle height, and gully width between different points to the extreme motion parameters, obtain the risk degree between different central nodes of adjacent grids, and numerically label according to the calculated risk degree, construct a risk - degree map and divide the threat level to the faulted hexapod robot. Step 3: Through the obtained terrain risk - degree model, design an improved A* algorithm, where the risk degree is the core factor of the path cost. Combining the obtained risk - degree map, select the path of the grid central node with the minimum local comprehensive risk degree, and converge to form a global path with the lowest risk degree, realizing intelligent path planning considering the terrain geometric characteristics and the extreme motion ability of the faulted hexapod robot.

2. The extreme sports ability analysis described in step 1 of claim 1 is characterized in that: During the movement of the faulted hexapod robot, through the sensing devices carried by itself, it can identify and measure road condition information. For the three typical terrains, hexapod robots with different fault types will have different limit values for corresponding parameters. For the slope terrain, the limit parameter is the pitch angle of the hexapod robot; for the gully terrain, the limit parameter is the step length of the hexapod robot; for the obstacle terrain, the limit parameter is the step height of the hexapod robot. Based on the symmetry characteristics of the hexapod robot, this study selects two typical single-leg fault types, the left front leg and the left middle leg, as the analysis objects to simplify the computational complexity and cover the core working conditions of the front and middle parts of the robot. The limit motion capabilities are different under different fault types. For the slope terrain, the arctangent function of the ratio of the support point height h c corresponding to the faulty leg and the projection distance r s is used to calculate the limit safe pitch angle δ max . In the obstacle terrain, by using the ratio of the leg moment constraint τ max corresponding to the fault type, the transmission efficiency η in the current fault state, and its own gravity, the limit step height value H max can be determined. For the gully terrain, for the calculation of the limit step length I max , the yaw angle θ, the body width W, and the adjacent foot end spacing L need to be combined to ensure that the remaining legs can still cover the gully span and maintain a stable margin when the left front leg or the left middle leg fails. Through symmetry simplification and multi-terrain parameter coupling analysis, this method provides a quantitative basis for motion planning in the fault state that takes into account both safety and efficiency.

3. According to the risk model under the integration of terrain geometric constraints and extreme sports ability described in step 2 of claim 1, it is characterized in that: By dividing the planning space from the starting point to the target point into multiple grids, and discretizing the central node of each grid into several node segments, gradually analyzing the risk level of each small segment, a non-linear terrain parameter analysis method based on integral operation is designed, transitioning from local planning to global planning. This design uses an integral operator to smooth the continuous terrain parameters. By mapping the non-linearly distributed terrain features into linearly superimposable threat factors, a risk level model for three typical terrains is designed in the form of a piecewise function to achieve dimensionality reduction representation of complex scenarios. The danger level under the slope terrain is as follows: Since the magnitude of the slope can be obtained from the angle α between the tangent plane passing through a point in the slope and the horizontal plane slope (x, y). Therefore, the calculation of the slope danger level in the local path between two nodes is as follows: Similarly, the danger level in obstacle terrain is as follows: Due to the presence of obstacle terrain, the depth value D(x, y) of the corresponding pixel point in the obstacle depth map (or point cloud data) of the obstacle area and the sensor installation height h sensor are used to define the undulation degree β of the obstacle terrain obstacle (x, y): β obstacle (x, y) = h sensor - D(x, y). The obstacle danger level is calculated as follows: Similarly, the risk level in a gully terrain is as follows: Since there is a horizontal distance in the gully, the difference between the maximum distance (x2, y2) and the minimum distance (x1, y1) to the gully edge is used to define the width of the gully: The gully risk level is calculated as follows: Where: δ max is the limit pitch angle of the robot in the current state, H max is the limit step height value of the robot in the current state, I max is the limit step length value of the robot in the current state, R is the number of typical terrains in the corresponding known terrain in the current distance, and (x1, y1) and (x2, y2) are the coordinates of the central nodes of adjacent grids after rasterization. Finally, by integrating the slope risk level, obstacle risk level, and gully risk level, the risk level model under the fusion of terrain geometric constraints and extreme motion ability can be defined as: P = P α (α slope ) + P β (β obstacle ) + P γ (γ gully ) (4) Based on the above-obtained risk level model under the fusion of terrain geometric constraints and extreme motion ability, the risk level between different central nodes of adjacent grids is obtained, numerically marked, and then a global risk level map is constructed. Finally, a threat degree analysis is carried out, that is: through the extreme motion parameters of the hexapod robot and the terrain geometric constraints under the current fault type, when P ∈ (0, 1), it is stipulated that the terrain in the selected path poses a small threat to the motion of the faulty hexapod robot, so walk along this path as much as possible; when P ∈ [1, 2), it is stipulated that the terrain in the selected path poses a medium threat to the motion of the faulty hexapod robot, and the risk level of the corresponding terrain in this path needs to be calculated separately to confirm whether it is close to or reaches the limit value, and this path needs to be carefully selected; when P ∈ [2, ∞), it is stipulated that the terrain in the selected path poses a great threat to the motion of the faulty hexapod robot, and avoid walking along this path.

4. An improved A* algorithm cost function according to step three in claim 1, characterized in that: Finally, based on the obtained risk level model under the fusion of faults and typical terrains, the A* algorithm is improved to obtain the improved cost function: Where: (x n , y n , z n ) is the current position coordinate; (x goal , y goal , z goal ) is the target position coordinate, P n is the risk degree of the nth selected local path, and P n-min is the minimum risk degree value in the nth selected local path. Therefore, the cost function of the finally obtained improved A* algorithm and finally the global path planning is carried out based on this cost function: Through the above methods, the path planning problem of the hexapod robot when a single leg fails can be solved.

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