An adaptive path planning method considering the coupling of tracked vehicle dynamics and terrain

Abstract

The application discloses a self-adaptive path planning method considering the coupling of dynamics and terrain of a tracked vehicle, and comprises the following steps: acquiring terrain characteristic parameters and vehicle dynamics parameters; constructing a terrain-dynamics coupling model according to the terrain characteristic parameters and the vehicle dynamics parameters, wherein the terrain-dynamics coupling model is used for outputting a slip risk prediction value and dynamic resistance of the vehicle under current terrain conditions in real time; based on the slip risk prediction value and the dynamic resistance, acquiring a planning path by using an improved Hybrid A * algorithm; monitoring a vehicle operating state in real time, dynamically correcting the planning path according to the vehicle operating state, and realizing self-adaptive path planning based on the corrected planning path. The method of the application can accurately identify the risks existing in the path and plan a safer and more reliable route.

Description

Technical Field

[0001] This invention belongs to the field of intelligent vehicle control and path planning technology, and particularly relates to an adaptive path planning method that considers the dynamics of tracked vehicles and terrain coupling. Background Technology

[0002] Tracked vehicles have strong terrain adaptability and obstacle crossing ability, but track slippage and rotation during actual driving will affect the vehicle's driving stability, especially in areas with soft soil, slippery slopes and complex obstacle distribution, where the vehicle body is prone to slipping or getting stuck.

[0003] Traditional tracked vehicle path planning algorithms primarily rely on static terrain parameters such as road slope and surface roughness, failing to establish a dynamic correlation model between terrain features and vehicle dynamics parameters. This makes it difficult for the planned paths to adapt to sudden changes in road conditions and the resulting risk of track slippage during actual operation. Furthermore, the path planning objective is singular, considering only the shortest path and ignoring the impact of vehicle dynamics parameters on energy consumption. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an adaptive path planning method that considers the coupling of tracked vehicle dynamics and terrain. By combining a dynamic coupling model of tracked vehicle dynamics and terrain features, the method improves the driving safety and efficiency of autonomous driving of tracked vehicles in complex terrain.

[0005] To achieve the above objectives, the present invention provides an adaptive path planning method that considers tracked vehicle dynamics and terrain coupling, comprising:

[0006] Obtain terrain feature parameters and vehicle dynamics parameters;

[0007] A terrain-dynamic coupling model is constructed based on the terrain feature parameters and vehicle dynamic parameters. The terrain-dynamic coupling model is used to output the predicted value of vehicle slip risk and dynamic drag under the current terrain conditions in real time.

[0008] Based on the predicted slip risk and dynamic resistance, using the improved Hybrid A * Algorithm to obtain the planned path;

[0009] The system monitors the vehicle's operating status in real time, dynamically corrects the planned path based on the vehicle's operating status, and achieves adaptive path planning based on the corrected planned path.

[0010] Optionally, the terrain feature parameters include: slope, road surface roughness, steering resistance coefficient, and adhesion coefficient.

[0011] Optionally, the vehicle dynamics parameters include: slip ratio and steering resistance.

[0012] Optionally, based on the predicted slip risk and dynamic drag, an improved Hybrid A... * The algorithm for obtaining the planned path includes:

[0013] Obtain the parameters of the target function;

[0014] Based on the objective function parameters, combined with the predicted slip risk and dynamic resistance, an improved Hybrid A... * The algorithm performs path search to obtain the planned path.

[0015] Optionally, the objective function parameters include: path length, energy consumption data, and slip risk data;

[0016] The path length is used to calculate the distance between different path points using the Euclidean distance calculation formula between adjacent path nodes, and then accumulates the total path length.

[0017] The energy consumption data is used to monitor the vehicle driving probability and power system efficiency in real time, and to calculate the vehicle's energy consumption on different road sections based on the energy consumption function.

[0018] The slip risk data is used to calculate slip risk based on the real-time slip rate and the pre-set maximum allowable slip rate.

[0019] Optionally, based on the objective function parameters, combined with the predicted slip risk and dynamic resistance, an improved Hybrid A... * The algorithm's path search, which obtains the planned path, includes:

[0020] Based on the objective function parameters, calculate the distance from the current node n to node n. i The actual value and inspirational value of this study;

[0021] Based on the actual cost value and heuristic value, and combined with node n i The estimated remaining path length to the target point, estimated energy consumption and risk, and calculation of node n i The heuristic cost function value;

[0022] The node with the smallest heuristic cost function value is selected as the next expansion node. It is then determined whether the expansion node has reached the target point. Based on the determination result, the planned path is obtained.

[0023] Optionally, real-time monitoring of vehicle operating status and dynamic correction of the planned route based on the vehicle operating status include:

[0024] Based on the vehicle's operating status and combined with the terrain-dynamics coupling model, the vehicle's real-time slip rate is calculated.

[0025] The real-time slip ratio is compared with the preset maximum allowable slip ratio to obtain the comparison result;

[0026] Based on the comparison results, the planned path is dynamically revised.

[0027] Optionally, dynamically correcting the planned path based on the comparison results includes:

[0028] If the real-time slip rate is greater than the preset maximum allowable slip rate, the planned path is dynamically corrected.

[0029] Otherwise, the planned path will not be dynamically modified.

[0030] Optionally, dynamically correcting the planned path includes:

[0031] Adjust the weights of the objective function parameters, and based on the partial results of the original path planning, combined with the additional risk correction value caused by terrain changes, recalculate the actual cost value and heuristic value of each node, and re-search for a path starting from the previous path point of the risk point according to the improved HybridA* algorithm.

[0032] Compared with the prior art, the present invention has the following advantages and technical effects:

[0033] 1. From the perspective of route planning accuracy and safety, by constructing a terrain-dynamics coupled model, the relationship between vehicle dynamics and terrain conditions is accurately described. This model can predict vehicle slippage risk in real time and quantify the dynamic resistance of the terrain to the vehicle. This allows the planned route to fully consider the interaction between the vehicle and the terrain during actual driving, greatly improving the accuracy of route planning. Compared with traditional planning methods based solely on terrain geometry information, this invention effectively avoids dangerous situations such as vehicle slippage and getting stuck due to neglecting dynamic factors, significantly improving vehicle driving safety. For example, in complex mountainous terrain, traditional methods may plan a seemingly feasible path that is actually unsafe to traverse due to vehicle dynamics limitations, while the method of this invention can accurately identify the risks of such a path and plan a safer and more reliable route.

[0034] 2. Regarding improved driving efficiency, the multi-objective optimization path planning algorithm comprehensively considers multiple key factors such as path length, safety, and energy consumption. By adaptively adjusting weight coefficients, it achieves overall performance optimization according to different task requirements. While ensuring safety, it balances path length and energy consumption, avoiding the problems of excessive energy consumption or reduced safety caused by traditional single-objective optimization (such as only pursuing the shortest path).

[0035] 3. The real-time dynamic correction mechanism provides vehicles with enhanced safety and robustness in practical applications. During vehicle operation, sensors monitor vehicle status and terrain changes in real time. Once the slip rate exceeds a threshold, the path correction mechanism is immediately triggered. This rapid response capability allows the vehicle to avoid risky areas and replan a safe path. This not only prevents vehicles from getting stuck due to sudden terrain changes but also reduces operational delays and economic losses caused by vehicle malfunctions or dangerous situations. For example, in field operations, when a vehicle suddenly encounters newly appearing soft areas or obstacles, the real-time dynamic correction mechanism can quickly adjust the path, ensuring continued safe driving and greatly enhancing the vehicle's adaptability in complex and changing environments. Attached Figure Description

[0036] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0037] Figure 1 This is a flowchart of an adaptive path planning method that considers tracked vehicle dynamics and terrain coupling according to an embodiment of the present invention;

[0038] Figure 2 This is a flowchart of the improved HybridA* path search method according to an embodiment of the present invention;

[0039] Figure 3 This is a flowchart of the objective function calculation according to an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of path correction according to an embodiment of the present invention. Detailed Implementation

[0041] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0042] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0043] This embodiment proposes an adaptive path planning method that considers the coupling of tracked vehicle dynamics and terrain, such as... Figure 1 As shown, the specific steps include:

[0044] Obtain terrain feature parameters and vehicle dynamics parameters;

[0045] A terrain-dynamic coupling model is constructed based on terrain feature parameters and vehicle dynamic parameters. The terrain-dynamic coupling model is used to output the predicted value of vehicle slip risk and dynamic drag under the current terrain conditions in real time.

[0046] Based on the predicted slip risk and dynamic resistance, using the improved Hybrid A * The algorithm obtains the planned path, where an adaptive step size Hybrid A algorithm is used. * - An improved Hybrid A is obtained by introducing steering angle variation, curvature variation, and slope cost function into the dissipation function using an adaptive radius RS curve. * Algorithm: Real-time monitoring of vehicle operating status; dynamic correction of the planned path based on the vehicle operating status; adaptive path planning based on the corrected planned path.

[0047] Specifically, in this embodiment, terrain feature parameters are selected and a real-time mapping relationship between them and vehicle dynamic characteristics is established through precise dynamic formulas.

[0048] A comprehensive cost function is constructed by taking into account path length, energy consumption, and slip risk. Based on the improved Hybrid A* algorithm, the node cost is updated in real time according to the dynamically changing terrain resistance during the node expansion stage, so as to achieve multi-objective balance optimization.

[0049] By using multiple sensors to monitor the vehicle's operating status in real time, when the slip ratio exceeds the threshold, the objective function weights are quickly adjusted and the path is replanned to ensure the vehicle can drive safely in complex and dynamically changing terrain.

[0050] Furthermore, terrain feature parameters include: slope, road surface roughness, steering drag coefficient, and adhesion coefficient.

[0051] Specifically, slope S: longitudinal slope of the road surface, expressed as a slope angle or percentage;

[0052] Road surface roughness R q Ground undulation statistics reflect the degree of road surface unevenness;

[0053] Steering drag coefficient f t : Describes the additional resistance experienced by a tracked vehicle when it turns;

[0054] Adhesion coefficient μ: The coefficient of friction between the track and the ground.

[0055] The above terrain feature parameters were obtained through lidar and cameras, and combined with historical terrain data to obtain relevant parameters;

[0056] Furthermore, vehicle dynamics parameters include slip ratio and steering resistance.

[0057] Specifically, the slip ratio model:

[0058] In the formula, v t The theoretical speed of the vehicle is given by denoted as 'v', and the actual speed of the vehicle is given by 'v'. The speed is obtained in real time through feedback from the vehicle speed sensor, and the vehicle's slip ratio is calculated in real time.

[0059] Steering resistance model:

[0060] In the formula, W represents the vehicle's vertical load, obtained through a pressure sensor mounted on the vehicle chassis, and f t ω is the steering resistance coefficient, B is the track center distance, L is the track contact length, and ω is the steering angular velocity, which is measured in real time by the vehicle angular velocity sensor.

[0061] The acquired terrain feature parameters and the calculated vehicle dynamics model parameters are substituted into the coupling function M. d =f(S,R) q ,μ,s,R t This function, built upon extensive experimental data and theoretical analysis, can output real-time predictions of vehicle slip risk and dynamic resistance under current terrain conditions, providing accurate data support for subsequent path planning.

[0062] Furthermore, based on the predicted slip risk and dynamic resistance, an improved Hybrid A... * The algorithm for obtaining the planned path includes:

[0063] Obtain the parameters of the target function;

[0064] Based on the objective function parameters, combined with the predicted slip risk and dynamic resistance, an improved Hybrid A * The algorithm performs path search to obtain the planned path.

[0065] Furthermore, the objective function parameters include: path length, energy consumption data, and slip risk data;

[0066] Path length L: The distance between different path points is calculated using the Euclidean distance formula between adjacent path nodes, and then the total path length is obtained by accumulating the distances.

[0067] Energy consumption function E: Real-time monitoring of vehicle driving probability P(t) and power system efficiency η(t), based on the energy consumption function. To calculate the energy consumption of a vehicle on different road segments, the trapezoidal integral method is used for discretization in the actual calculation, where T is the total travel time of the route.

[0068] Slip risk function R: Based on the slip ratio s(l) at the real-time path position l and the pre-set maximum allowable slip ratio smax Through formula Calculate the slip risk. Here, s(l) represents the slip ratio at path position l. In the actual calculation, the trapezoidal integral method is also used for discretization.

[0069] Weight coefficient adjustment: Adjust the values ​​of path length coefficient α, safety weight coefficient γ, and energy consumption weight coefficient β according to different scenario requirements, but ensure that α+β+γ=1.

[0070] Furthermore, such as Figure 2 As shown, based on the objective function parameters, combined with the predicted slip risk and dynamic resistance, an improved Hybrid A... * The algorithm's path search, which obtains the planned path, includes:

[0071] like Figure 3 As shown, based on the objective function parameters, the distance from the current node n to node n is calculated. i The actual value and inspirational value of this study;

[0072] Based on the actual cost value and heuristic value, and combined with node n i The estimated remaining path length to the target point, estimated energy consumption and risk, and calculation of node n i The heuristic cost function value;

[0073] Select the node with the smallest heuristic cost function value as the next expansion node, determine whether the expansion node has reached the target point, and obtain the planned path based on the determination result.

[0074] Specifically, based on the improved HybridA* path search:

[0075] Starting from the current node n, its set of neighboring nodes is N. For each node n in the node set... i Calculate the actual cost function From node n to node n i The path length increment, energy consumption increment, and slip risk increment.

[0076] Simultaneously, the heuristic function h(n) is calculated using the Euclidean distance from the current node to the target node. i Consider node n i The estimated remaining path length to the target point, estimated energy consumption and risk, and based on f(n) i )=g(n i )+h(n i ) Compute node n i The heuristic cost function value.

[0077] After calculating the heuristic cost function values ​​for all neighboring nodes, select f(n) iThe smallest node is selected as the next node to be expanded.

[0078] Furthermore, real-time monitoring of vehicle operating status and dynamic correction of the planned route based on vehicle operating status include:

[0079] Based on the vehicle's operating status and combined with the terrain-dynamics coupling model, the real-time slip rate of the vehicle is calculated.

[0080] The real-time slip ratio is compared with the preset maximum allowable slip ratio to obtain the comparison result;

[0081] Based on the comparison results, the planned path is dynamically revised.

[0082] Furthermore, based on the comparison results, the planned path is dynamically revised, including:

[0083] If the real-time slip rate exceeds the preset maximum allowable slip rate, the planned path will be dynamically corrected.

[0084] Otherwise, the planned path will not be dynamically corrected.

[0085] Specifically, a) Real-time status monitoring:

[0086] By utilizing various sensors installed on the vehicle, real-time operational status information is acquired, including parameters such as vehicle speed, acceleration changes, track slippage, power system output torque, and power. The sensors transmit this real-time data to the vehicle's central control system, providing data support for dynamic risk assessment.

[0087] b) Dynamic risk assessment and correction:

[0088] The central control system calculates the vehicle's slip ratio s in real time based on real-time sensor data and a terrain-dynamic coupling model. a . s a With the pre-set maximum allowable slip s max Compare, if s a >s max If the current driving path of the vehicle is deemed to have a high risk, the path correction mechanism will be triggered.

[0089] like Figure 4 As shown, the path correction restarts the path planning algorithm, using the vehicle's current position as the new starting point and the original target point as the ending point. First, the objective function weights are adjusted, with a slight increase in the safety weight coefficient γ to prioritize path safety. Then, based on partial results from the original path planning, and combined with the additional risk correction value ΔR caused by terrain changes, the algorithm adjusts the path. newBy rapidly adjusting the node cost, i.e. recalculating the actual cost function g(n) and heuristic function h(n) of each node according to the new risk situation, the path is re-searched starting from the previous path point of the risk point according to the improved HybridA* algorithm.

[0090] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An adaptive path planning method considering tracked vehicle dynamics and terrain coupling, characterized in that, include: Obtain terrain feature parameters and vehicle dynamics parameters; A terrain-dynamic coupling model is constructed based on the terrain feature parameters and vehicle dynamic parameters. The terrain-dynamic coupling model is used to output the predicted value of vehicle slip risk and dynamic drag under the current terrain conditions in real time. Based on the predicted slip risk and dynamic resistance, using the improved Hybrid A Algorithm to obtain the planned path; Based on the predicted slip risk and dynamic resistance, using the improved Hybrid A The algorithm for obtaining the planned path includes: Obtain the parameters of the target function; Based on the objective function parameters, combined with the predicted slip risk and dynamic resistance, an improved Hybrid A... The algorithm performs path search to obtain the planned path; The objective function parameters include: path length, energy consumption data, and slip risk data; The path length is used to calculate the distance between different path points using the Euclidean distance calculation formula between adjacent path nodes, and then accumulates the total path length. The energy consumption data is used to monitor the vehicle driving probability and power system efficiency in real time, and to calculate the vehicle's energy consumption on different road sections based on the energy consumption function. The slip risk data is used to calculate slip risk based on the real-time slip rate and the preset maximum allowable slip rate; The system monitors the vehicle's operating status in real time, dynamically corrects the planned path based on the vehicle's operating status, and achieves adaptive path planning based on the corrected planned path.

2. The adaptive path planning method considering tracked vehicle dynamics and terrain coupling according to claim 1, characterized in that, The terrain feature parameters include: slope, road surface roughness, steering resistance coefficient, and adhesion coefficient.

3. The adaptive path planning method considering tracked vehicle dynamics and terrain coupling according to claim 1, characterized in that, The vehicle dynamics parameters include: slip ratio and steering resistance.

4. The adaptive path planning method considering tracked vehicle dynamics and terrain coupling according to claim 1, characterized in that, Based on the objective function parameters, combined with the predicted slip risk and dynamic resistance, an improved Hybrid A... The algorithm's path search, which obtains the planned path, includes: Based on the objective function parameters, calculate the distance from the current node n to the node. The actual value and inspirational value of this study; Based on the actual cost value and heuristic value, and combined with the node... The estimated remaining path length to the target point, estimated energy consumption and risk, and calculation of nodes. The heuristic cost function value; The node with the smallest heuristic cost function value is selected as the next expansion node. It is then determined whether the expansion node has reached the target point. Based on the determination result, the planned path is obtained.

5. The adaptive path planning method considering tracked vehicle dynamics and terrain coupling according to claim 4, characterized in that, Real-time monitoring of vehicle operating status and dynamic correction of the planned route based on the vehicle operating status include: Based on the vehicle's operating status and combined with the terrain-dynamics coupling model, the vehicle's real-time slip rate is calculated. The real-time slip ratio is compared with the preset maximum allowable slip ratio to obtain the comparison result; Based on the comparison results, the planned path is dynamically revised.

6. The adaptive path planning method considering tracked vehicle dynamics and terrain coupling according to claim 5, characterized in that, Based on the comparison results, dynamically correcting the planned path includes: If the real-time slip rate is greater than the preset maximum allowable slip rate, the planned path is dynamically corrected. Otherwise, the planned path will not be dynamically modified.

7. The adaptive path planning method considering tracked vehicle dynamics and terrain coupling according to claim 6, characterized in that, Dynamically correcting the planned path includes: Adjusting the weights of the objective function parameters, based on partial results from the original path planning and incorporating additional risk corrections due to terrain changes, recalculates the actual cost and heuristic value of each node, following the improved Hybrid A... The algorithm starts searching for a new path from the previous path point of the risk point.

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