High-agility wheeled vehicle local motion planning method for dealing with sudden air risk

Abstract

The invention belongs to the technical field of motion planning, and particularly relates to a high-agility wheeled vehicle local motion planning method for dealing with sudden air risks, which comprises the following steps: mapping risk positions and speed attributes to a space potential field model and a speed potential field model based on composite risk assessment of an artificial potential energy field to complete risk preliminary screening; collision point analysis is carried out according to the preliminary screening result, risk fine screening is completed, and risk assessment values are generated in combination with risk categories; local motion sampling is carried out according to the vehicle state and performance constraint information, and an expected candidate track set is generated; calculating a performance function of each candidate trajectory based on the risk assessment value, and assessing an optimal trajectory satisfying risk avoidance and vehicle driving stability; and selecting a planning control instruction of an optimal track in real time according to a rolling optimization theory, sending the planning control instruction to a chassis controller for execution, and dynamically realizing air risk avoidance.

Description

Technical Field

[0001] The present invention belongs to the field of motion planning technology, and in particular relates to a local motion planning method for a high-agility wheeled vehicle to cope with sudden aerial risks. Background Art

[0002] During maneuvering in urban construction sites, the uncertainty of falling objects can make it difficult for vehicles to maintain their safety characteristics. Under these conditions, the extremely short window for risk response makes it difficult to rely solely on human responses. Therefore, vehicles must possess strong intelligent maneuverability, using autonomous motion planning to take over the vehicle control system and proactively maneuver to avoid sudden aerial risks, thereby ensuring their own safety. Most existing path planning methods based on search, sampling, and optimization focus on ground-based risks, ignoring the impact of vehicle voxel height on aerial risk assessment in a three-dimensional environment. These methods are unsuitable for high-speed aerial risk assessment and evasive maneuver planning. Summary of the Invention

[0003] (1) Technical issues to be solved

[0004] The technical problem to be solved by the present invention is: the present invention provides a local motion planning method for a high-agility wheeled vehicle to cope with sudden air risks, which is used to realize the vehicle's active maneuvering and evasive movement, develop the vehicle's intelligent stress response characteristics, and improve survivability.

[0005] (2) Technical solution

[0006] To solve the above technical problems, the present invention provides a local motion planning method for a highly agile wheeled vehicle to cope with sudden air risks. The local motion planning method comprises the following steps:

[0007] Step 1: Initial risk screening;

[0008] After detecting an incoming air risk, the system quickly predicts risk information and vehicle trajectory, and uses a composite risk assessment based on an artificial potential field to map the risk location and velocity attributes to the spatial potential field model and velocity potential field model to complete the initial risk screening.

[0009] Step 2: Risk screening;

[0010] Based on the initial screening results, collision point analysis is performed to identify the air risk trajectory predicted to hit the vehicle, complete risk screening, and generate a risk threat coefficient based on the risk category coefficient;

[0011] Step 3: candidate trajectory sampling;

[0012] Based on the vehicle status and performance constraint information, local expected trajectory sampling is performed to generate a set of feasible candidate trajectories for the vehicle in the prediction domain;

[0013] Step 4: Optimal trajectory evaluation;

[0014] Calculate the performance evaluation function value of each candidate trajectory based on the risk threat coefficient, and evaluate the optimal trajectory that meets risk avoidance and vehicle driving stability;

[0015] Step 5: Generate optimal planning trajectory;

[0016] According to the rolling optimization theory, the optimal trajectory planning instructions are selected in real time and sent to the chassis controller for execution, dynamically achieving air risk avoidance.

[0017] Through the above steps, the local motion planning method for a high-agility wheeled vehicle to cope with sudden air risks can automatically assess air risks and calculate risk avoidance trajectories in real time.

[0018] In the risk initial screening part of step 1, the artificial potential energy field adopts a coupled potential energy field model based on a space potential field model and a velocity potential field model;

[0019] The spatial potential field model uses the coordinates of air risk and vehicle as research objects. It analyzes whether the danger threshold is reached in the future. If so, it is determined that the air risk will pose a threat to the vehicle in the future, and the air risk is considered a threat target.

[0020] The velocity potential field model takes the current velocity vector of the air risk and the vehicle as the research objects; it judges whether the angle between the current composite velocity vector and the risk pointing vector is within the expansion angle. If so, it is determined to be a risk target.

[0021] In the risk fine screening part of step 2, for the predicted air risk trajectory, the risk category coefficient is obtained by mapping and evaluating according to the identified target category.

[0022] The air threat assessment part of steps 1 and 2 inputs the air risk prediction trajectory point sequence and the vehicle prediction trajectory point sequence, undergoes Euclidean distance coarse screening, vehicle coordinate system conversion, and coordinate threshold fine screening operations, and completes the air threat risk assessment at the current moment based on the spatial field of view model and the velocity field of view model;

[0023] If it is determined that the aerial threat will hit the vehicle, the remaining hit time, the coordinates of the hit point, and the hit point velocity vector parameters are output to the subsequent steps;

[0024] If it is determined that the vehicle will not be hit, the time of hitting the ground and the predicted landing point coordinates are output to the subsequent steps;

[0025] If the incoming air object has not yet landed, the system will proceed to the next sampling moment and re-evaluate the air threat under the new state.

[0026] In the air threat assessment part of step 2, if it is determined that the air threat will hit the vehicle, the risk threat coefficient is calculated based on the risk category coefficient corresponding to the identified target category and the remaining hit time, and output to the subsequent step for optimal trajectory evaluation.

[0027] In the local expected trajectory sampling process of step 3, trajectory point sampling is performed based on the vehicle's own performance, and the sampling constraints include the vehicle's performance constraints, the vehicle's geometric mechanical constraints, the vehicle's sideslip dynamics constraints, and the vehicle's obstacle avoidance safety constraints.

[0028] In the optimal trajectory evaluation process of step 4, the performance evaluation function includes a target orientation evaluation item, a speed evaluation item, and an obstacle distance evaluation item considering the target type.

[0029] The obstacle distance evaluation item is related to the risk threat coefficient.

[0030] Among them, during the selection process of the optimal trajectory planning instructions in step 5, within each sampling interval, the first three control instructions are selected based on the optimal trajectory with the highest performance evaluation function value and sent to the chassis controller for platform maneuver control.

[0031] Among them, the method can realize stress trajectory planning for sudden air risks, improve the agility, intelligence and safety of vehicle maneuvers, and enhance the survivability of the vehicle.

[0032] (3) Beneficial effects

[0033] Compared with the existing technology, the present invention provides a local motion planning method for high-agility wheeled vehicles to deal with sudden air risks. It introduces spatial potential field and velocity potential field to judge air risks, and can take into account the difference in hitting points caused by vehicle elevation. Compared with the two-dimensional ground obstacle avoidance planning strategy, the present invention can handle scenarios where there is an air risk and the vehicle is about to be hit by an angled flight trajectory. The judgment scheme is more reasonable and more suitable for mission scenarios with air risks. By introducing a risk coefficient assessment based on risk category identification, the dodge trajectory is automatically adjusted according to the target risk level to improve the safety of the planned trajectory. The planned local motion trajectory can simultaneously meet the vehicle's extreme performance constraints, the vehicle's geometric mechanical constraints, the vehicle's sideslip dynamics constraints, and the vehicle's obstacle avoidance safety constraints.

[0034] Through the above embodiments, the local motion planning method for a high-agility wheeled vehicle to cope with sudden air risks can realize stress trajectory planning for sudden air risks, improve the agility, intelligence and safety of vehicle maneuvers, and enhance the survivability of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1Schematic diagram of the local planning strategy for dealing with air risks according to the present invention.

[0036] Figure 2 This is a flow chart of the trajectory solution for local motion planning of a high-agility wheeled vehicle for responding to sudden aerial risks according to the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, content, and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings and examples.

[0038] To solve the above technical problems, the present invention provides a local motion planning method for a highly agile wheeled vehicle to cope with sudden air risks. The local motion planning method comprises the following steps:

[0039] Step 1: Initial risk screening;

[0040] After detecting an incoming air risk, the system quickly predicts risk information and vehicle trajectory, and uses a composite risk assessment based on an artificial potential field to map the risk location and velocity attributes to the spatial potential field model and velocity potential field model to complete the initial risk screening.

[0041] Step 2: Risk screening;

[0042] Based on the initial screening results, collision point analysis is performed to identify the air risk trajectory predicted to hit the vehicle, complete risk screening, and generate a risk threat coefficient based on the risk category coefficient;

[0043] Step 3: candidate trajectory sampling;

[0044] Based on the vehicle status and performance constraint information, local expected trajectory sampling is performed to generate a set of feasible candidate trajectories for the vehicle in the prediction domain;

[0045] Step 4: Optimal trajectory evaluation;

[0046] Calculate the performance evaluation function value of each candidate trajectory based on the risk threat coefficient, and evaluate the optimal trajectory that meets risk avoidance and vehicle driving stability;

[0047] Step 5: Generate optimal planning trajectory;

[0048] According to the rolling optimization theory, the optimal trajectory planning instructions are selected in real time and sent to the chassis controller for execution, dynamically achieving air risk avoidance.

[0049] Through the above steps, the local motion planning method for a high-agility wheeled vehicle to cope with sudden air risks can automatically assess air risks and calculate risk avoidance trajectories in real time.

[0050] In the risk initial screening part of step 1, the artificial potential energy field adopts a coupled potential energy field model based on a space potential field model and a velocity potential field model;

[0051] The spatial potential field model uses the coordinates of air risk and vehicle as research objects. It analyzes whether the danger threshold is reached in the future. If so, it is determined that the air risk will pose a threat to the vehicle in the future, and the air risk is considered a threat target.

[0052] The velocity potential field model takes the current velocity vector of the air risk and the vehicle as the research objects; it judges whether the angle between the current composite velocity vector and the risk pointing vector is within the expansion angle. If so, it is determined to be a risk target.

[0053] In the risk fine screening part of step 2, for the predicted air risk trajectory, the risk category coefficient is obtained by mapping and evaluating according to the identified target category.

[0054] The air threat assessment part of steps 1 and 2 inputs the air risk prediction trajectory point sequence and the vehicle prediction trajectory point sequence, undergoes Euclidean distance coarse screening, vehicle coordinate system conversion, and coordinate threshold fine screening operations, and completes the air threat risk assessment at the current moment based on the spatial field of view model and the velocity field of view model;

[0055] If it is determined that the aerial threat will hit the vehicle, the remaining hit time, the coordinates of the hit point, and the hit point velocity vector parameters are output to the subsequent steps;

[0056] If it is determined that the vehicle will not be hit, the time of hitting the ground and the predicted landing point coordinates are output to the subsequent steps;

[0057] If the incoming air object has not yet landed, the system will proceed to the next sampling moment and re-evaluate the air threat under the new state.

[0058] In the air threat assessment part of step 2, if it is determined that the air threat will hit the vehicle, the risk threat coefficient is calculated based on the risk category coefficient corresponding to the identified target category and the remaining hit time, and output to the subsequent step for optimal trajectory evaluation.

[0059] In the local expected trajectory sampling process of step 3, trajectory point sampling is performed based on the vehicle's own performance, and the sampling constraints include the vehicle's performance constraints, the vehicle's geometric mechanical constraints, the vehicle's sideslip dynamics constraints, and the vehicle's obstacle avoidance safety constraints.

[0060] In the optimal trajectory evaluation process of step 4, the performance evaluation function includes a target orientation evaluation item, a speed evaluation item, and an obstacle distance evaluation item considering the target type.

[0061] The obstacle distance evaluation item is related to the risk threat coefficient.

[0062] Among them, during the selection process of the optimal trajectory planning instructions in step 5, within each sampling interval, the first three control instructions are selected based on the optimal trajectory with the highest performance evaluation function value and sent to the chassis controller for platform maneuver control.

[0063] Among them, the method can realize stress trajectory planning for sudden air risks, improve the agility, intelligence and safety of vehicle maneuvers, and enhance the survivability of the vehicle.

[0064] Example 1

[0065] This embodiment provides a specific implementation method for local motion planning of a high-agility wheeled vehicle to cope with sudden aerial risks.

[0066] 1. Initial risk screening: After detecting an incoming air attack risk, the system quickly predicts risk information and vehicle trajectory. Based on a composite risk assessment using an artificial potential field, the system maps the risk location and velocity attributes to a spatial potential field model and a velocity potential field model to complete the initial risk screening.

[0067] 2. Risk Refinement Screening: Based on the initial screening results, collision point analysis is performed to identify high-risk trajectories that are expected to hit the vehicle. This completes the risk refinement screening and generates a risk assessment value based on the risk categories.

[0068] 3. Candidate trajectory sampling: Based on the vehicle status and performance constraint information, local expected trajectory sampling is performed to generate a set of feasible candidate trajectories for the vehicle in the prediction domain;

[0069] 4. Optimal trajectory evaluation: Calculate the performance function value of each candidate trajectory based on the risk assessment value, and evaluate the optimal trajectory that satisfies risk aversion and vehicle driving stability;

[0070] 5. Optimal planned trajectory generation: Based on the rolling optimization theory, the optimal trajectory planning instructions are selected in real time and sent to the chassis controller for execution, dynamically achieving air risk avoidance;

[0071] During implementation, in the initial screening part, when the presence of an aerial target is sensed and the trajectory sequence points of the aerial target are collected, the estimated time t and spatial coordinates p of the aerial target to reach the ground are solved. mis,t And obtain the spatial coordinate p of the vehicle at that moment through trajectory prediction t By comparing the distance d between the two and the threshold dr, risk determination based on the spatial potential field is performed;

[0072]

[0073] where index p1 means the unit is at risk, index p A value of 0 indicates that the unit has no risk;

[0074] During implementation, in the initial screening part, when the presence of an aerial target is sensed and the trajectory sequence points of the aerial target are collected, the current vehicle speed is used to and air threats currently move at a speed of Calculate the resultant velocity vector Threat Position Vector And the corresponding angle θ under the threat expansion radius dm s , perform risk assessment based on velocity potential field:

[0075]

[0076] where index v 1 means the unit is at risk, index v A value of 0 indicates that the unit has no risk;

[0077] During implementation, in the fine screening part, the trajectory coordinates P of the incoming object are d (t) is converted to the coordinate P in the vehicle coordinate system d '(t); if P d '(t) is not within the vehicle envelope box threshold, then the incoming object at that moment did not hit the vehicle, and the next moment is calculated; if P d If '(t) is within the vehicle envelope box threshold, it is determined that the incoming object has hit the vehicle at that moment, and this moment is recorded as t k ;

[0078] Among them, the hit point P hit Set to P d '(t k-1 ) and P d '(t k ), the midpoint of hit =(P d '(t k-1 )+P d '(t k )) / 2; the time of hit is T hit =(t k-1 +t k ) / 2; the direction vector of the incoming object when hitting (in the vehicle coordinate system) is

[0079] In the air threat assessment part, if it is judged that the air threat will hit the vehicle, the risk category coefficient ξ corresponding to the target identification category is i and the remaining hit time t re Calculate the risk threat coefficient risk i, i.e. the risk assessment value, is output to the planning module for optimal trajectory evaluation:

[0080]

[0081] Where i is the risk number;

[0082] During the local expected trajectory sampling process, trajectory points are sampled according to the vehicle's own performance. The sampling constraints V mainly include the vehicle's performance constraints V m , the vehicle's geometric mechanical constraints V s , the vehicle's sideslip dynamics constraint V f And the vehicle's obstacle avoidance safety constraints V a :

[0083] V∈V m ∩V s ∩V f ∩V a

[0084]

[0085] V s ={(v,ω)|δ∈[δ min, δ max}

[0086] V f ={(v,ω)|v≤v r}

[0087]

[0088] where v min , v max ,ω min ,ω max , δ min, , δ max is the minimum / maximum value of vehicle speed, angular velocity, and front wheel angle, v c and ω c are the current speed and angular velocity of the vehicle, v r is the critical rollover speed of the vehicle, and are the absolute values ​​of the acceleration and angular acceleration of the obstacle relative to the vehicle, respectively; dist(v,ω) is the risk distance between the vehicle trajectory sampling point and the target;

[0089] The optimal trajectory evaluation function G(v,ω) mainly includes three aspects: target heading evaluation (heading(v,ω,type), velocity evaluation (velocity(v,ω,type)) and obstacle distance evaluation (dist(v,ω,type)).

[0090] G(v,ω)=σ(α·heading(v,ω,type)+β·velocity(v,ω,type)+γ·dist(v,ω,type))

[0091] where σ, α, ω min , β, γ are the corresponding weight coefficients;

[0092] Among them, the type attribute in dist(v,ω,type) is composed of the risk threat coefficient risk i influence decisions;

[0093] During the planning instruction selection process, within each control sampling interval, the first three control instructions are selected based on the optimal trajectory with the highest target value of the performance evaluation function and sent to the chassis controller for platform maneuver control.

[0094] Compared with the existing technology, this embodiment provides a local motion planning method for highly agile wheeled vehicles to deal with sudden air risks. It introduces spatial potential field and velocity potential field to judge air risks, and can take into account the difference in hitting points caused by vehicle elevation. Compared with the two-dimensional ground obstacle avoidance planning strategy, this embodiment can handle scenarios where there is an air risk and the vehicle is about to be hit by an angled flight trajectory. The judgment scheme is more reasonable and more suitable for mission scenarios with air risks. By introducing a risk coefficient assessment based on risk category identification, the dodge trajectory is automatically adjusted according to the target risk level to improve the safety of the planned trajectory. The planned local motion trajectory can simultaneously meet the vehicle's extreme performance constraints, the vehicle's geometric mechanical constraints, the vehicle's sideslip dynamics constraints, and the vehicle's obstacle avoidance safety constraints.

[0095] Through the above embodiments, the local motion planning method for a high-agility wheeled vehicle to cope with sudden air risks can realize stress trajectory planning for sudden air risks, improve the agility, intelligence and safety of vehicle maneuvers, and enhance the survivability of the vehicle.

[0096] In summary, the present invention belongs to the field of motion planning technology, and specifically relates to a high-agility wheeled vehicle local motion planning method for responding to sudden air risks. It solves the problem of low path feasibility caused by the existing wheeled vehicle local motion planning algorithm ignoring the three-dimensional motion characteristics of air risks. The present invention is based on a risk assessment algorithm under a composite artificial potential field, dynamically adjusts the vehicle's local motion planning control output, and achieves a high-agility evasion effect for responding to sudden air risks. It includes a composite risk assessment based on an artificial potential field, mapping the risk position and velocity attributes to a spatial potential field model and a velocity potential field model to complete a preliminary risk screening; performing collision point analysis based on the preliminary screening results to complete a fine risk screening, and generating a risk assessment value in combination with the risk category; performing local motion sampling based on vehicle status and performance constraint information to generate a set of expected candidate trajectories; calculating the performance function of each candidate trajectory based on the risk assessment value, and evaluating the optimal trajectory that meets risk avoidance and vehicle driving stability; selecting the planning control instruction of the optimal trajectory in real time according to the rolling optimization theory, and sending it to the chassis controller for execution, to dynamically achieve air risk avoidance.

[0097] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A local motion planning method for a highly agile wheeled vehicle to cope with sudden aerial risks, characterized by: The local motion planning method comprises the following steps: Step 1: Initial risk screening; After detecting an incoming air risk, the system quickly predicts risk information and vehicle trajectory, and uses a composite risk assessment based on an artificial potential field to map the risk location and velocity attributes to the spatial potential field model and velocity potential field model to complete the initial risk screening. Step 2: Risk screening; Based on the initial screening results, collision point analysis is performed to identify the air risk trajectory predicted to hit the vehicle, complete risk screening, and generate a risk threat coefficient based on the risk category coefficient; Step 3: candidate trajectory sampling; Based on the vehicle status and performance constraint information, local expected trajectory sampling is performed to generate a set of feasible candidate trajectories for the vehicle in the prediction domain; Step 4: Optimal trajectory evaluation; Calculate the performance evaluation function value of each candidate trajectory based on the risk threat coefficient, and evaluate the optimal trajectory that meets risk avoidance and vehicle driving stability; Step 5: Generate optimal planning trajectory; According to the rolling optimization theory, the optimal trajectory planning instructions are selected in real time and sent to the chassis controller for execution, dynamically achieving air risk avoidance. Through the above steps, the local motion planning method for a high-agility wheeled vehicle to cope with sudden air risks can automatically assess air risks and calculate risk avoidance trajectories in real time.

2. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 1, characterized in that: In the risk initial screening part of step 1, the artificial potential energy field adopts a coupled potential energy field model based on a spatial potential field model and a velocity potential field model; The spatial potential field model uses the coordinates of air risk and vehicle as research objects. It analyzes whether the danger threshold is reached in the future. If so, it is determined that the air risk will pose a threat to the vehicle in the future, and the air risk is considered a threat target. The velocity potential field model takes the current velocity vector of the air risk and the vehicle as the research objects; it judges whether the angle between the current composite velocity vector and the risk pointing vector is within the expansion angle. If so, it is determined to be a risk target.

3. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 1, characterized in that: In the risk fine screening part of step 2, for the predicted air risk trajectory, the risk category coefficient is obtained by mapping and evaluating according to the identified target category.

4. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 3, characterized in that: The air risk threat assessment part of steps 1 and 2 inputs the air risk prediction trajectory point sequence and the vehicle prediction trajectory point sequence, undergoes Euclidean distance coarse screening, vehicle coordinate system conversion, and coordinate threshold fine screening operations, and completes the air threat risk assessment at the current moment based on the spatial field of view model and the velocity field of view model; If it is determined that the aerial threat will hit the vehicle, the remaining hit time, the coordinates of the hit point, and the hit point velocity vector parameters are output to the subsequent steps; If it is determined that the vehicle will not be hit, the time of hitting the ground and the predicted landing point coordinates are output to the subsequent steps; If the incoming aerial object has not yet landed, the system will proceed to the next sampling moment and re-evaluate the aerial threat under the new state.

5. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 1, characterized in that: In the air threat assessment part of step 2, if it is determined that the air threat will hit the vehicle, the risk threat coefficient is calculated based on the risk category coefficient corresponding to the identified target category and the remaining hit time, and output to the subsequent step for optimal trajectory evaluation.

6. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 1, characterized in that: During the local expected trajectory sampling process of step 3, trajectory point sampling is performed based on the vehicle's own performance. The sampling constraints include the vehicle's performance constraints, the vehicle's geometric mechanical constraints, the vehicle's sideslip dynamics constraints, and the vehicle's obstacle avoidance safety constraints.

7. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 1, characterized in that: During the optimal trajectory evaluation process of step 4, the performance evaluation function includes a target orientation evaluation item, a speed evaluation item, and an obstacle distance evaluation item considering the target type.

8. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 7, characterized in that: The obstacle distance evaluation item is related to the risk threat coefficient.

9. The method for local motion planning of a highly agile wheeled vehicle for coping with sudden aerial risks according to claim 1, characterized in that: During the selection of the optimal trajectory planning instructions in step 5, within each sampling interval, the top three control instructions are selected based on the optimal trajectory with the highest performance evaluation function value and sent to the chassis controller for platform maneuver control.

10. The local motion planning method for a highly agile wheeled vehicle to cope with sudden air risks according to claim 1, characterized in that: The method can realize stress trajectory planning for sudden air risks, improve the agility, intelligence and safety of vehicle maneuvers, and enhance the survivability of the vehicle.

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