CN110703804A - Layering anti-collision control method for fixed-wing unmanned aerial vehicle cluster - Google Patents
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Abstract
The invention discloses a layered anti-collision control method of a fixed-wing unmanned aerial vehicle cluster, which comprises the following steps: s1, dividing a local conflict airspace of the unmanned aerial vehicle into a plurality of layers according to the relative distance between the unmanned aerial vehicle and a conflict object, and constructing to obtain a local layered conflict airspace model; s2, acquiring flight state information and expected flight state information of the controlled unmanned aerial vehicle in real time and motion state information of adjacent unmanned aerial vehicles and environmental barriers in the current local airspace, judging the relative state relationship between the unmanned aerial vehicle and the adjacent unmanned aerial vehicles and the environmental barriers in the local airspace, determining the current corresponding local conflict airspace level of the controlled unmanned aerial vehicle, and performing conflict detection according to the determined local conflict airspace level; and S3, executing collision avoidance according to the currently corresponding local collision airspace level of the controlled unmanned aerial vehicle. The method has the advantages of simple implementation method, capability of comprehensively avoiding adjacent unmanned aerial vehicles and environmental obstacles, capability of covering various collision and danger scenes, good avoiding effect and the like.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a layered anti-collision control method for a fixed-wing unmanned aerial vehicle cluster.
Background
Fixed wing Unmanned Aerial Vehicles (UAVs) have been widely used in military as well as civil fields, and it is also a trend that their cluster systems are used to cooperatively perform tasks. For the unmanned aerial vehicle cluster flight, how to deal with the obstacle threat in the unknown environment and the uncertainty of the processing environment become a great challenge for the unmanned aerial vehicle cluster flight. Meanwhile, with the enlargement of the cluster scale, the situation that a plurality of unmanned aerial vehicles execute tasks in a dense airspace with a plurality of obstacles cannot be avoided, the situation that the unmanned aerial vehicles collide with the unmanned aerial vehicles and the environmental obstacles is likely to occur, and great threat is caused to the flight safety of the cluster system.
In the prior art, some researches on obstacle avoidance and conflict resolution of fixed-wing unmanned aerial vehicles exist, but at present, only avoidance control of a single machine is concerned, namely avoidance collision is realized only for the single machine, and no clear classification analysis is performed on an avoidance object of the unmanned aerial vehicle, and systematic researches and solutions for a small-sized fixed-wing unmanned aerial vehicle cluster system are lacked for the cluster collision avoidance problem under complex conditions.
To sum up, to the obstacle avoidance, conflict resolution problem of fixed wing unmanned aerial vehicle, mainly there are following problems at present:
1. the single anti-collision method in the prior art can not simultaneously solve the problem of avoiding obstacles and preventing collision among individuals in a cluster;
2. in the prior art, an anti-collision control law is usually solved based on a class of methods and models, the overall modeling and solving of complex collision and conflict scenes of a cluster system are lacked, various possible collision and danger scenes cannot be well covered, and the actual avoidance effect is poor.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the layered anti-collision control method of the fixed-wing unmanned aerial vehicle cluster, which is simple in implementation method, can comprehensively avoid adjacent unmanned aerial vehicles and environmental obstacles, can cover various collision and dangerous scenes and has a good avoiding effect.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a layered anti-collision control method for a fixed-wing unmanned aerial vehicle cluster comprises the following steps:
s1, constructing a layered model: dividing a local conflict airspace of the unmanned aerial vehicle into a plurality of layers according to the relative distance between the unmanned aerial vehicle and a conflict object, and constructing to obtain a local layered conflict airspace model;
s2, layered conflict detection: acquiring flight state information of a controlled unmanned aerial vehicle, expected flight state information within a future specified time length and motion state information of adjacent unmanned aerial vehicles and environmental barriers in a current local airspace in real time, judging a relative state relationship between the unmanned aerial vehicle and the adjacent unmanned aerial vehicles and the environmental barriers in the local airspace, determining a local conflict airspace level currently corresponding to the controlled unmanned aerial vehicle according to the relative state relationship, and performing conflict detection according to the determined local conflict airspace level;
s3, layered collision avoidance: and executing collision avoidance according to the local collision airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle.
Further, when the local conflict airspace is partitioned in step S1, the local hierarchical conflict airspace model is constructed and obtained based on the constraint condition of any one or more of the flight speed, turning radius, and safety radius of the unmanned aerial vehicle and the conflict early warning time generated at different relative distances.
Further, in step S1, the local collision airspace is divided into three layers, i.e., an outer layer, a middle layer, and an inner layer, based on the relationship between the collision early warning time τ and the relative distance between the unmanned aerial vehicle and the collision object, and a local three-layer collision airspace model is constructed, where the local three-layer collision airspace model specifically is:
Ωe＝{Pt|Rm＜|Pt-P|≤Re}
Ωm＝{Pt|Ri＜|Pt-P|≤Rm}
Ωi＝{Pt|Rs＜|Pt-P|≤Ri}
wherein omegae、Ωm、ΩiRespectively correspond to an outer layer, a middle layer and an inner layer, Re，Rm，RiRespectively, the distance threshold value of the conflict airspace of the outer layer, the middle layer and the inner layer, P, the position of the controlled unmanned aerial vehicle, PtThe position of any point in the local conflict airspace of the controlled unmanned aerial vehicle.
Further, when performing collision detection in step S2, if the local collision airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle is the outer layer, a collision detection method based on an expected path is adopted, and the specific steps include: dividing a bounded flight area omega of the controlled unmanned aerial vehicle along the reference path by taking a preset reference path as a center and taking the upper bound of the transverse tracking error of the flight of the controlled unmanned aerial vehicle as a widthrtAnd using the nearest point of the current position of the controlled unmanned aerial vehicle to the expected path as a starting point, and pushing a distance R forward to the advancing direction of the flighteTo obtain a forward-pushed collision detection flight zone omegaaheadAnd judging whether the environmental barrier O meets the following conditions:
min{|PO-P||P∈Ωahead}≤Rs+RO
if yes, judging that outer layer collision conflict exists between the environmental barrier O and the unmanned aerial vehicle, wherein POCoordinates of characteristic points, R, for an environmental obstacle OsLimiting safety radius for controlled drone, ROKeeping a distance for the controlled unmanned aerial vehicle to the edge of the environmental barrier O.
Further, when performing collision detection in step S2, if the local collision airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle is the middle level, a collision detection method based on state prediction is adopted, and the specific steps include:
calculating the movement speed and direction of the environmental barrier in the local space of the controlled unmanned aerial vehicle, and constructing a discrete motion equation of the environmental barrier O as follows:
according to the constructed discrete motion equation of the environmental obstacle O and the discrete kinematic equation of the unmanned aerial vehicle, predicting an N-step relative state sequence of the environmental obstacle O and the controlled unmanned aerial vehicle in a time domain dt, wherein N is dt/delta T, and delta T is a control period, and obtaining the shortest distance between the controlled unmanned aerial vehicle and the environmental obstacle O in the prediction time domain dt as follows:
within each control period DeltaT, if the environmental obstacle O is satisfiedIt is determined that there is a middle tier collision conflict.
Further, when performing collision detection in step S3, if the local collision airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle is the inner layer, a collision-collision detection method based on a collision-free sufficiency condition is adopted, and the specific steps include:
configuring sufficient conditions that collision conflict does not exist between a controlled unmanned aerial vehicle i in the unmanned aerial vehicle cluster and an unmanned aerial vehicle j in an inner-layer conflict airspace as follows:
|Pij(k)|≥2Rs
wherein i, j belongs to omega, i is not equal to j, omega is the set of all unmanned aerial vehicles in the unmanned aerial vehicle cluster,
representing the included angle between the relative velocity vector and the relative position vector of the two unmanned aerial vehicles;
and if the initial relative states of the two unmanned aerial vehicles meet the sufficient condition, judging that no collision conflict exists between the unmanned aerial vehicles, otherwise, judging that a collision conflict exists between the unmanned aerial vehicles.
Further, when performing collision avoidance in step S3, if the local collision airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle is the outer layer, the method is implemented by selecting a collision-free minimum-deviation sub-target point and generating a smooth collision-free avoidance curve by 3-time B-spline fitting, by using a Subtargets ((sub-target point algorithm) and a 3-time B-spline combined online re-planning avoidance control method, and the specific steps include:
generating a group of collision-free path point sequences along the barrier edge by adopting a Subtargets algorithm based on the known reference path and the position of a collision object and by adopting an iterative point-taking method according to the principle of minimum deviation
Based on the obtained collision-free path point sequence
And interpolating and fitting the path point sequence by adopting a 3-time B spline algorithm to generate an expected smooth avoidance path, and tracking the generated smooth avoidance path by the controlled unmanned aerial vehicle to realize obstacle avoidance.
Further, the collision-free path point sequence
The generation steps are specifically as follows:
① determining the conflicted obstacle set on the forward path by taking the current position of the controlled unmanned aerial vehicle as a starting coordinate point
② in the collection
To determine the first obstacle in the forward path
③ determining inclusion of obstacles
Set of all obstacles to be avoided
④ determining a set of avoidance obstacles
Includes determining forward/reverse bias directions of the secondary target points relative to the forward path, and determining a setThe minimum deflection angle required by each obstacle included in the direction, and the coordinates of the secondary target point are calculated according to the required maximum deflection angle and the corresponding obstacle position
⑤ repeating steps ① - ④ with the newly generated secondary target point as the starting coordinate point until the set of conflicting obstacles between the starting point and the stage target pointIf the sequence is empty, all the secondary target point sequences are returned to obtain the collision-free path point sequence
Further, when collision avoidance is performed in step S3, if the local collision airspace hierarchy corresponding to the controlled unmanned aerial vehicle at present is the middle level, a distributed model predictive control method is used, an optimized objective function based on model predictive control is constructed by using a path tracking deviation amount and a control amount based on the controlled unmanned aerial vehicle, and a relative distance and a relative speed between the controlled unmanned aerial vehicle and a collision object, and an optimized control avoidance sequence is generated by performing rolling optimization with a finite step length in each control cycle, which specifically includes:
configuring a first objective function of collision avoidance control optimization based on model predictive control for non-cooperative barriers in an environment as follows:
wherein k ise,kωIs the cost coefficient of the image to be displayed,
for path tracking off-costs, eiIn order for the path to track the error,
to control energy consumption cost, UiIs the output of the control, and is,in order to be a dangerous cost term for the collision,
for the set of all non-cooperative obstacles in the local airspace of the controlled drone at time k,
is the obstacle O in the setjA resulting collision risk cost function, and:
wherein k isd,kvRelative distance and relative velocity threat cost coefficients,
is the relative distance between the drone and the obstacle,
is the relative velocity between the drone and the obstacle;
the second objective function for building distributed model predictive control for adjacent unmanned aerial vehicles in the cluster is as follows:
wherein the content of the first and second substances,
a set of neighbor drones representing drones at time k,
and representing a collision danger cost function generated by the neighbor unmanned plane j to the unmanned plane i.
Synthesizing the first objective function and the second objective function to obtain an overall avoidance control objective function, and iteratively solving an optimized control sequence { U ] in each control period T0,U1,…,UN-1Generating an optimized avoidance control sequence by rolling optimization, taking N as a prediction step length, and using a first item U in the optimized avoidance control sequence0Act on the controlled unmanned aerial vehicle to realize collision avoidance.
Further, when collision avoidance is executed in step S3, if the local conflict airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle is the inner layer, a reactive avoidance control law is generated based on a collision-free sufficiency condition to implement avoidance of an inner-layer collision conflict, where the reactive avoidance control law specifically is:
φρ＝∠-Pij(k)-φi(k)
where ρ isiIs a direction factor, wherein if the course adjustment is positive in the clockwise direction, the course angles of the two unmanned planes at the current moment are respectively phii(k)＝∠Vi(k) And phij(k)＝∠Vj(k)，ωmaxIs the maximum heading angular rate.
Compared with the prior art, the invention has the advantages that:
1. according to the layered anti-collision control method for the fixed-wing unmanned aerial vehicle cluster, a local layered collision airspace model of the unmanned aerial vehicle is built, the collision avoidance problem of the unmanned aerial vehicle cluster is divided into different levels according to different scenes of the relative distance between the unmanned aerial vehicle and a collision object, corresponding collision detection and collision avoidance are executed according to the different levels, layered obstacle avoidance of the unmanned aerial vehicle in the path tracking process is achieved, adjacent unmanned aerial vehicles and environmental obstacles can be comprehensively avoided, and reasonable avoidance strategies can be selected to cover various collision and dangerous scenes to avoid collision collisions, so that collision-free cluster flight is ensured.
2. The layered anti-collision control method for the fixed-wing unmanned aerial vehicle cluster is characterized in that a cluster flight scene is modeled in a classified mode according to the complexity of a task scene and the feasibility of avoidance control, collision risks and various avoidance control are integrated, a three-layer cluster avoidance control system architecture of 'outer layer planning, middle layer control and inner layer reaction' of the fixed-wing unmanned aerial vehicle cluster is realized, and the problem of avoidance control of the unmanned aerial vehicle cluster in various scenes can be systematically solved.
3. The layered anti-collision control method of the fixed-wing unmanned aerial vehicle cluster further realizes three-layer collision detection of 'an outer layer based on an expected path, a middle layer based on state prediction and an inner layer based on no-collision sufficient condition' according to the division of a local collision airspace model and the occurrence conditions and characteristics of collision collisions in a corresponding layered airspace, so that the unmanned aerial vehicle can timely and accurately detect collision collisions existing in the environment.
4. The layered anti-collision control method for the fixed-wing unmanned aerial vehicle cluster further aims at collision conflicts detected in a three-layer collision airspace, adopts a three-layer collision avoidance control strategy according to the flight characteristics of the fixed-wing unmanned aerial vehicle, and can quickly and effectively realize collision avoidance through online re-planning, predictive control and reactive avoidance maneuver.
5. The layered anti-collision control method of the fixed-wing unmanned aerial vehicle cluster is further based on a model prediction control method and a distributed model prediction control method respectively aiming at high dynamics and uncertainty in a middle-layer collision airspace, obtains a control sequence through rolling optimization by configuring a non-cooperative avoidance objective function aiming at a dynamic environment obstacle and a cooperative avoidance objective function aiming at a neighbor unmanned aerial vehicle, and accordingly can achieve collision avoidance with minimum path deviation, minimum control energy consumption and minimum danger cost.
Drawings
Fig. 1 is a schematic diagram of an implementation flow of the layered collision avoidance control method of the fixed-wing drone cluster according to the embodiment.
Fig. 2 is a schematic diagram of a layered conflict airspace model of a fixed-wing drone constructed in the embodiment.
Fig. 3 is a schematic diagram showing the comparison result of path tracking errors in a simple and barrier-free situation by using the method of the present invention and the conventional method.
FIG. 4 is a schematic diagram of an avoiding effect of non-cooperative obstacle avoidance control in a multi-dynamic obstacle scene by using the method of the present invention.
FIG. 5 is a schematic diagram of the method and the avoidance effect of cooperative avoidance control under a time-varying communication topology.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 1, the layered collision avoidance control method for the fixed-wing drone cluster of the present embodiment includes the steps of:
s1, constructing a layered model: dividing a local conflict airspace of the unmanned aerial vehicle into a plurality of layers according to the relative distance between the unmanned aerial vehicle and a conflict object, and constructing to obtain a local layered conflict airspace model;
s2, layered conflict detection: acquiring flight state information of a controlled unmanned aerial vehicle, expected flight state information within a future specified time length and motion state information of adjacent unmanned aerial vehicles and environmental barriers in a current local airspace in real time, judging a relative state relationship between the unmanned aerial vehicle and the adjacent unmanned aerial vehicles and the environmental barriers in the local airspace, determining a local conflict airspace level corresponding to the controlled unmanned aerial vehicle at present according to the relative state relationship, and performing conflict detection according to the determined local conflict airspace level;
s3, layered collision avoidance: and executing collision avoidance according to the currently corresponding local collision airspace hierarchy of the controlled unmanned aerial vehicle.
According to the embodiment, a local hierarchical conflict airspace model of the unmanned aerial vehicle is built, the collision avoidance problem of an unmanned aerial vehicle cluster is divided into different levels according to different scenes of relative distances between the unmanned aerial vehicle and conflict objects, corresponding conflict detection and collision avoidance are executed according to the different levels, hierarchical obstacle avoidance of the unmanned aerial vehicle in a path tracking process is realized, adjacent unmanned aerial vehicles and environmental obstacles can be comprehensively avoided, reasonable avoidance strategies can be selected to maneuver collision conflicts by covering various collision and dangerous scenes, and therefore the cluster flight without collision is ensured.
In this embodiment, when the local conflict airspace is partitioned in step S1, specifically, based on the constraint conditions of the flight speed, turning radius, safety radius, and the like of the unmanned aerial vehicle, and the conflict early warning time generated in different relative distances, a local layered conflict airspace model is constructed, and the local conflict airspace of the controlled unmanned aerial vehicle is layered according to the constraint conditions and the length of the conflict early warning time generated in different relative distance conditions, so that a corresponding reasonable conflict detection method and a collision avoidance method can be used according to different local airspace levels, and conditions are provided for the accurate detection and pre-judgment of the unmanned aerial vehicle on the collision conflict.
In step S1, the local collision airspace is divided into three layers, i.e., an outer layer, a middle layer and an inner layer, based on the relationship between the collision early warning time τ and the relative distance between the unmanned aerial vehicle and the collision object, and a local three-layer collision airspace model is constructed, so that the collision avoidance problem of the unmanned aerial vehicle cluster can be divided into three scenarios, i.e., an outer layer with a relatively long distance, a middle layer with high dynamics and uncertainty and a moderate relative distance, and an inner layer with a short relative distance and a high threat degree, to implement corresponding collision detection and avoidance control for the three scenarios.
At any sampling moment, the unmanned aerial vehicle acquires state information of the unmanned aerial vehicle and the adjacent unmanned aerial vehicle and the obstacle in a local airspace, predicts and judges relative states of the unmanned aerial vehicle and the adjacent unmanned aerial vehicle and the obstacle, and under the condition that the unmanned aerial vehicle does not take any evading action, if the unmanned aerial vehicle and the adjacent unmanned aerial vehicle collide within a limited time tau through prediction, the unmanned aerial vehicle and the adjacent unmanned aerial vehicle or the obstacle are judged to have collision conflict, and the time tau is defined as conflict early warning time. Since the collision early warning time tau is proportional to the relative distance between the unmanned aerial vehicle and the collision object. That is, the larger the relative distance is, the longer the time left for the unmanned aerial vehicle to perform avoidance control is, and the lower the collision threat of the collision is. Knowing the position P of the unmanned aerial vehicle, and according to the relative distance between the unmanned aerial vehicle and the conflict object, carrying out comparison on any point P in the local airspace of the unmanned aerial vehicletThe local three-layer collision airspace model constructed in this embodiment specifically includes:
wherein omegae、Ωm、ΩiRespectively correspond to an outer layer, a middle layer and an inner layer, Re，Rm，RiThe distance threshold values of the outer layer, the middle layer and the inner layer conflict airspace are respectively determinedDefining a linear function of the flight speed of the unmanned aerial vehicle; p, position of the controlled drone itself, PtThe position of any point in the local conflict airspace of the controlled unmanned aerial vehicle.
In this embodiment, the relative state relationship in step S2 specifically includes a relative distance and a relative movement speed, that is, the local conflict airspace level currently corresponding to the controlled unmanned aerial vehicle is determined according to the relative distance and the relative movement speed between the controlled unmanned aerial vehicle and the adjacent unmanned aerial vehicle and the environmental obstacle in the local airspace. When the unmanned aerial vehicle cluster is controlled, the current flight state of the controlled unmanned aerial vehicle and the expected state of the controlled unmanned aerial vehicle in a future period of time are firstly obtained, the perception information of the motion states of the neighboring unmanned aerial vehicles and the environmental barriers in the local airspace is obtained, the relative distance and the relative motion speed between the unmanned aerial vehicle and the neighboring unmanned aerial vehicles and the environmental barriers in the local airspace are calculated, the corresponding local conflict airspace layer is divided according to the calculated relative distance and motion speed, then the corresponding collision conflict judgment method is selected according to the divided local conflict airspace layer, and the existing collision conflict is detected and judged.
In this embodiment, when performing collision detection in step S2, if the current local collision airspace hierarchy corresponding to the controlled unmanned aerial vehicle is an outer layer, a collision detection method based on an expected path is adopted, and the specific steps include:
s211, dividing a bounded flight area omega of the controlled unmanned aerial vehicle along the reference path by taking a preset reference path as a center and taking the upper bound of the transverse tracking error of the flight of the controlled unmanned aerial vehicle as a widthrtAnd using the nearest point of the current position of the controlled unmanned aerial vehicle to the expected path as a starting point, and pushing a distance R forward to the advancing direction of the flighteTo obtain a forward-pushed collision detection flight zone omegaahead；
S212, judging whether the environmental barrier O meets the following conditions:
min{|PO-P||P∈Ωahead}≤Rs+RO(2)
if yes, judging that outer layer collision conflict exists between the environmental barrier O and the unmanned aerial vehicle, wherein POCoordinates of characteristic points, R, for an environmental obstacle OsFor controlled unmanned aerial vehiclesLimiting the safety radius, ROKeeping a distance for the controlled unmanned aerial vehicle to the edge of the environmental barrier O.
Path tracking is one of the most common basic mission modes for fixed-wing drones to perform missions, and the flight state of a drone is heavily dependent on the shape of the reference curve path. For the outer layer with a relatively long distance in the local three-layer collision airspace model, the collision detection is realized based on the expected path, and for the fixed-wing unmanned aerial vehicle flying along the preset reference path, the bounded flight area omega of the unmanned aerial vehicle along the reference curve is divided according to the methodrtAnd obtaining a forward-pushed collision detection flight region omegaaheadAnd then, whether outer layer collision conflict exists between the environmental barrier O in the local airspace and the unmanned aerial vehicle is judged by the formula (2), and the outer layer collision conflict in the local collision airspace of the controlled unmanned aerial vehicle can be timely and accurately detected.
In this embodiment, when performing collision detection in step S2, if the currently corresponding local collision airspace hierarchy of the controlled unmanned aerial vehicle is the middle level, a collision detection method based on state prediction is adopted, and the specific steps include:
s221, for the environmental barriers in the local airspace, calculating the movement speed and direction of the environmental barriers in the local airspace of the controlled unmanned aerial vehicle based on the current and historical perception information of the unmanned aerial vehicle, assuming that the environmental barriers O perform uniform linear motion in a sufficiently small time period, and constructing a discrete motion equation of the environmental barriers O as follows:
s222, according to the constructed discrete motion equation of the environmental obstacle O and the discrete kinematic equation of the unmanned aerial vehicle, predicting an N-step relative state sequence of the environmental obstacle O and the controlled unmanned aerial vehicle in a time domain dt, wherein N is dt/delta T, and delta T is a control period, and obtaining that the shortest distance which can be obtained by the controlled unmanned aerial vehicle and the environmental obstacle O in the prediction time domain dt is as follows:
s223. in each control period delta T, if the environmental barrier O is satisfied
It is determined that there is a middle tier collision conflict, i.e., at each control cycle, the obstacle O is determined to be there is a middle tier collision and only if it is satisfied
And for the neighboring unmanned aerial vehicles in the same airspace range, based on state information and control information obtained by communication, the collision and collision detection method based on state prediction is used for predicting the motion state and the nearest distance, and further collision and collision detection is carried out.
For the middle layer with high dynamics and uncertainty and moderate relative distance in the local three-layer collision airspace model, the collision conflict detection based on state prediction is adopted in the embodiment, so that the middle layer collision conflict in the local collision airspace of the controlled unmanned aerial vehicle can be timely and accurately detected.
In this embodiment, when performing collision detection in step S3, if the currently corresponding local collision airspace hierarchy of the controlled unmanned aerial vehicle is an inner layer, a collision and collision detection method based on a collision-free sufficient condition is adopted, and the specific steps include:
s231, configuring sufficient conditions that collision conflict does not exist between the controlled unmanned aerial vehicle i in the unmanned aerial vehicle cluster and the unmanned aerial vehicle j in the inner conflict airspace as follows:
|Pij(k)|≥2Rs(5)
wherein i, j belongs to omega, i is not equal to j, omega is the set of all unmanned aerial vehicles in the unmanned aerial vehicle cluster,
representing the included angle between the relative velocity vector and the relative position vector of the two unmanned aerial vehicles;
s232, if the initial relative states of the two unmanned aerial vehicles meet the sufficient conditions, judging that no collision conflict exists between the unmanned aerial vehicles, and otherwise, judging that a collision conflict exists between the unmanned aerial vehicles.
For the inner layer with short relative distance and high threat degree in the local three-layer collision airspace model, the collision conflict detection method based on the conflict-free sufficient condition is adopted in the embodiment, the sufficient condition without collision conflict between the unmanned aerial vehicles is configured according to the state parameters between the unmanned aerial vehicles and the unmanned aerial vehicles, whether collision conflict exists between the unmanned aerial vehicles is judged according to the sufficient condition, and the conflict detection of the local collision airspace inner layer of the controlled unmanned aerial vehicle can be timely and accurately realized.
Through the steps, the three-layer collision conflict detection corresponding to the three-layer collision airspace model of which the outer layer is based on the expected path, the middle layer is based on state prediction and the inner layer is based on the conflict-free sufficient condition can be realized according to the characteristics and conditions of collision conflicts in the three-layer collision airspace, collision conflicts in various different scenes can be timely and accurately detected, and in step S3, the corresponding three-layer collision avoidance strategy is selected through the unmanned aerial vehicle according to the judged collision conflict level, so that effective avoidance maneuver can be generated to realize collision-free flight.
In this embodiment, when collision avoidance is executed in step S3, if the currently corresponding local collision airspace level of the controlled unmanned aerial vehicle is an outer layer, the method is implemented by selecting a collision-free sub-target point with minimum deviation and generating a smooth collision-free avoidance curve through 3-time B-spline fitting by using a Subtargets (sub-target point algorithm) algorithm and a 3-time B-spline combined online re-planning avoidance control method. By combining the targets algorithm and the 3-time B-sample, online re-planning and avoiding control is realized, collision avoidance can be realized aiming at the collision and collision characteristics of the outer layer with a relatively long distance, the realization method is simple, and collision can be effectively avoided in time.
The specific steps of implementing the collision avoidance by the outer layer in the embodiment include:
s311, generating a group of obstacle edges by adopting a Subtargets algorithm based on a known reference path and the position of a collision object and an iterative point-taking method according to the principle of minimum deviationCollision-free path point sequence
S312. based on the obtained collision-free path point sequence
And interpolating and fitting the path point sequence by adopting a 3-time B spline algorithm to generate an expected smooth evasion path, and tracking the generated smooth evasion path by the controlled unmanned aerial vehicle to realize obstacle avoidance.
In this embodiment, the collision-free path point sequence
The generation steps are specifically as follows:
① determining the conflicted obstacle set on the forward path by taking the current position of the controlled unmanned aerial vehicle as a starting coordinate point
② in the collection
To determine the first obstacle in the forward path
③ determining inclusion of obstacles
Set of all obstacles to be avoided
④ determining a set of avoidance obstaclesIncludes determining forward/reverse bias directions of the secondary target points relative to the forward path, and determining a set
The minimum deflection angle required by each obstacle included in the direction, and the coordinates of the secondary target point are calculated according to the required maximum deflection angle and the corresponding obstacle position
⑤ repeating steps ① - ④ with the newly generated secondary target point as the starting coordinate point until the set of conflicting obstacles between the starting point and the stage target point
If the sequence is empty, all the secondary target point sequences are returned to obtain the collision-free path point sequence
In this embodiment, when collision avoidance is executed in step S3, if the current local collision airspace level corresponding to the controlled unmanned aerial vehicle is the middle level, a distributed model predictive control method is used, an optimized objective function based on model predictive control is constructed by using the path tracking deviation amount and the control amount of the controlled unmanned aerial vehicle, and the relative distance and the relative speed between the controlled unmanned aerial vehicle and the collision object, and an optimized avoidance control sequence is generated by performing finite-step rolling optimization in each control cycle. Aiming at middle-layer collision conflicts with high dynamics and uncertainty and moderate relative distance, the embodiment starts with the problems of non-cooperative environmental obstacle avoidance and cooperative adjacent unmanned aerial vehicles in avoiding two types of conflicts, adopts a distributed model predictive control method, and generates an optimized avoidance control sequence through rolling optimization, wherein an optimized objective function comprehensively considers the path tracking deviation cost, the control energy consumption cost and the collision danger cost of the unmanned aerial vehicles, so that the collision avoidance can be realized with the optimal performance.
The specific steps of the embodiment for realizing middle-layer collision avoidance by adopting a distributed model predictive control method comprise:
s321, aiming at non-cooperative obstacles in the environment, configuring a first objective function of collision avoidance control optimization based on a model prediction control method as follows:
wherein k ise,kωIs the cost coefficient of the image to be displayed,
for path tracking off-costs, eiIn order for the path to track the error,
to control energy consumption cost, UiIs the output of the control, and is,
in order to be a dangerous cost term for the collision,for the set of all non-cooperative obstacles in the local airspace of the controlled drone at time k,
is the obstacle O in the setjA resulting collision risk cost function, and:
wherein k isd,kvRelative distance and relative velocity threat cost coefficients,
is the relative distance between the drone and the obstacle,
is the relative velocity between the drone and the obstacle;
s322, a second objective function for constructing distributed model prediction control for adjacent unmanned aerial vehicles in the cluster is as follows:
wherein the content of the first and second substances,
a set of neighbor drones representing drones at time k,
and representing a collision danger cost function generated by the neighbor unmanned plane j to the unmanned plane i.
S323, synthesizing the first objective function and the second objective function to obtain a total avoidance control objective function, and iteratively solving an optimized control sequence { U ] in each control period T0,U1,…,UN-1Generating an optimized avoidance control sequence by rolling optimization, taking N as a prediction step length, and enabling a first item U0Act on the controlled unmanned aerial vehicle to realize collision avoidance.
In this embodiment, when collision avoidance is executed in step S3, if the current local collision airspace hierarchy corresponding to the controlled unmanned aerial vehicle is an inner layer, a reactive avoidance control law is generated based on a sufficient condition of no collision to implement, and the control law can effectively avoid inner layer collision. To the inner layer collision conflict that relative distance is close, threat degree is high, this embodiment generates the reaction formula through the abundant condition of no collision and avoids the control law, does not consider unmanned aerial vehicle's path tracking effect this moment, can reduce unmanned aerial vehicle's response time and improve unmanned aerial vehicle's crashworthiness.
The reactive avoidance control law generated in this embodiment is specifically:
where ρ isiFor the directional factor, the control input U is determinediWherein if the course adjustment is positive clockwise, the course angles of the two drones at the current time are phi respectivelyi(k)＝∠Vi(k) And phij(k)＝∠Vj(k)，ωmaxIs the maximum heading angular rate.
According to the method, the outer layer conflict can be avoided by the unmanned aerial vehicle through the collision conflict steps in the three-layer conflict airspace in the simplest and most effective re-planning method according to different conflict conditions, the middle layer conflict can be avoided by the maneuvering with the minimum and optimal energy consumption, and the inner layer conflict can be avoided through the fastest effective response.
Certainly, in other embodiments, the collision detection mode and the collision avoidance control strategy may also be configured according to the actual flying speed, the cluster scale, the flying mission and the like, for example, an outer layer adopts an online planning control strategy based on a vector field, so that the position of the unmanned aerial vehicle can directly generate an avoidance curve path according to the vector field, or similar but different distance division functions are proposed for division of a local collision airspace, and avoidance control is performed hierarchically, and the like.
In a specific application embodiment, for adjacent unmanned aerial vehicles in a local airspace, a configurable conflict detection link and an avoidance control solving step are completed in an optimization solving process of a model prediction controller.
In a specific application embodiment of the invention, the detailed flow for realizing the layered anti-collision control in the cluster flight of the fixed-wing unmanned aerial vehicle by utilizing the constructed local layered conflict airspace model comprises the following steps:
step 1: path tracking and collision detection process
Step 1.1, acquiring the current flight state of the unmanned aerial vehicle and the expected state of the unmanned aerial vehicle in a future period of time;
step 1.2, acquiring the motion states of neighboring unmanned aerial vehicles and environmental obstacles in a local airspace;
step 1.3, calculating the relative distance and the relative movement speed between the unmanned aerial vehicle and adjacent unmanned aerial vehicles and environmental barriers in a local airspace, and dividing the relative distance and the relative movement speed into corresponding local conflict airspace layers;
and 4, selecting a collision conflict judgment method of a corresponding level according to the relative state relation between the unmanned aerial vehicle and the adjacent unmanned aerial vehicle and the environmental obstacle, and detecting and judging the existing collision conflict.
Step 2: collision conflict avoidance process
2.1, selecting a corresponding avoidance control method according to the type and the level of the detected conflict object;
step 2.2, solving corresponding avoidance control quantity by adopting corresponding avoidance control methods respectively to obtain an action strategy of the next period;
and 2.3, returning to the task control mode until the collision avoidance maneuver is completed.
In order to verify the effectiveness of the method, the control method is used for carrying out a layered avoidance control experiment on the avoidance control of a multi-dynamic obstacle scene on a fixed-wing unmanned aerial vehicle under a basic task mode of path tracking and under a cluster flight condition with 4 frames as scales, the moving speed of the environmental obstacle in the experiment is 10m/s, the environmental obstacles are all positioned on the expected path of the unmanned aerial vehicle (in the experiment of a non-cooperative scene, each unmanned aerial vehicle treats a neighbor unmanned aerial vehicle as a non-cooperative environmental obstacle, and the basic flight speed of the unmanned aerial vehicle is 19 m/s). The flight error of the unmanned aerial vehicle executing path tracking is calculated in detail in the experiment, the avoidance keeping distance and the path deviation amount in the collision avoidance process of the unmanned aerial vehicle are obtained, and the obtained experimental results are shown in fig. 3, 4 and 5, wherein the abscissa is the avoidance keeping distance, and the ordinate is the path deviation amount, fig. 3 is a comparison result of the path tracking error under the simple and barrier-free condition by adopting the Method (MPC) of the invention and the traditional method (BS (Back-steering), vf (vector field), plos (pure push and Line of sight) algorithm), fig. 4 is a schematic diagram of the avoidance effect of non-cooperative obstacle avoidance control under the multi-dynamic obstacle scene by adopting the method of the invention, fig. 4(a) corresponds to the deviation distance between four unmanned aerial vehicles and a reference path in the flying and collision avoidance processes, fig. 4(b) corresponds to the separation distance between two unmanned aerial vehicles in the flying process, d 12-d 34 are-the distance between two unmanned planes with subscript labels, and fig. 4(c) corresponds to the actual motion trajectory of the simulation test of four unmanned planes in an obstacle environment, wherein the open circles indicate the unknown of the obstacle, and the solid circles indicate the initial unknown of the four unmanned planes; fig. 5 shows the avoidance effect of the cooperative avoidance control under the time-varying communication topology, where fig. 5(a) corresponds to the deviation distance between the unmanned aerial vehicle and the reference path during the flight, fig. 5(b) corresponds to the separation distance between __ four unmanned aerial vehicles during the flight, d 12-d 34 represents the distance between two unmanned aerial vehicles with subscript labels, and fig. 5(c) corresponds to the actual motion trajectory of the four unmanned aerial vehicles in the simulation test in the obstacle environment, where the open circles represent the unknown obstacles, and the solid circles represent the initial unknown of the four unmanned aerial vehicles. From the experimental results of fig. 3 to 5, it can be seen that the control method of the invention can realize collision-free path tracking flight of the unmanned aerial vehicle cluster in a complex task environment with multiple dynamic obstacles, the path deviation of the algorithm is small, and the unmanned aerial vehicle evasion effect is good.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (10)
1. A layered anti-collision control method for a fixed-wing unmanned aerial vehicle cluster is characterized by comprising the following steps:
s1, constructing a layered model: dividing a local conflict airspace of the unmanned aerial vehicle into a plurality of layers according to the relative distance between the unmanned aerial vehicle and a conflict object, and constructing to obtain a local layered conflict airspace model;
s2, layered conflict detection: acquiring flight state information of a controlled unmanned aerial vehicle, expected flight state information within a future specified time length and motion state information of adjacent unmanned aerial vehicles and environmental barriers in a current local airspace in real time, judging a relative state relationship between the unmanned aerial vehicle and the adjacent unmanned aerial vehicles and the environmental barriers in the local airspace, determining a local conflict airspace level currently corresponding to the controlled unmanned aerial vehicle according to the relative state relationship, and performing conflict detection according to the determined local conflict airspace level;
s3, layered collision avoidance: and executing collision avoidance according to the local collision airspace hierarchy currently corresponding to the controlled unmanned aerial vehicle.
2. The layered collision avoidance control method for the fixed-wing drone cluster according to claim 1, wherein in the step S1, when the local collision airspace is partitioned, the local layered collision airspace model is constructed and obtained based on any one or more of the constraint conditions of the flight speed, the turning radius, and the safety radius of the drone and the collision early warning time generated at different relative distances.
3. The layered collision avoidance control method for the fixed-wing drone cluster according to claim 1, wherein in step S1, the local collision airspace is divided into three layers of an outer layer, a middle layer and an inner layer based on a relationship between collision early warning time τ and a relative distance between the drone and a collision object, and a local three-layer collision airspace model is constructed, and the local three-layer collision airspace model is specifically:
Ωe＝{Pt|Rm＜|Pt-P|≤Re}
Ωm＝{Pt|Ri＜|Pt-P|≤Rm}
Ωi＝{Pt|Rs＜|Pt-P|≤Ri}
wherein omegae、Ωm、ΩiRespectively correspond to an outer layer, a middle layer and an inner layer, Re，Rm，RiRespectively as the distance threshold of the outer layer, the middle layer and the inner layer of the conflict airspace, P is the position of the controlled unmanned aerial vehicle, P istThe position of any point in the local conflict airspace of the controlled unmanned aerial vehicle.
4. According toThe layered collision avoidance control method for the fixed-wing drone cluster according to claim 3, wherein when collision detection is performed in step S2, if the local collision airspace hierarchy currently corresponding to the controlled drone is the outer layer, a collision detection method based on an expected path is adopted, and the method specifically includes: dividing a bounded flight area omega of the controlled unmanned aerial vehicle along the reference path by taking a preset reference path as a center and taking the upper bound of the transverse tracking error of the flight of the controlled unmanned aerial vehicle as a widthrtAnd using the nearest point of the current position of the controlled unmanned aerial vehicle to the expected path as a starting point, and pushing a distance R forward to the advancing direction of the flighteTo obtain a forward-pushed collision detection flight zone omegaaheadAnd judging whether the environmental barrier O meets the following conditions:
min{|PO-P||P∈Ωahead}≤Rs+RO
if yes, judging that outer layer collision conflict exists between the environmental barrier O and the unmanned aerial vehicle, wherein POCoordinates of characteristic points, R, for an environmental obstacle OsLimiting safety radius for controlled drone, ROKeeping a distance for the controlled unmanned aerial vehicle to the edge of the environmental barrier O.
5. The layered collision avoidance control method for the fixed-wing drone cluster according to claim 3, wherein when performing collision detection in step S2, if the local collision airspace level currently corresponding to the controlled drone is the middle level, a collision detection method based on state prediction is adopted, and the specific steps include:
calculating the movement speed and direction of the environmental barrier in the local space of the controlled unmanned aerial vehicle, and constructing a discrete motion equation of the environmental barrier O as follows:
according to the constructed discrete motion equation of the environmental obstacle O and the discrete kinematic equation of the unmanned aerial vehicle, predicting an N-step relative state sequence of the environmental obstacle O and the controlled unmanned aerial vehicle in a time domain dt, wherein N is dt/delta T, and delta T is a control period, and obtaining the shortest distance between the controlled unmanned aerial vehicle and the environmental obstacle O in the prediction time domain dt as follows:
within each control period DeltaT, if the environmental obstacle O is satisfied
It is determined that there is a middle tier collision conflict.
6. The layered collision avoidance control method for the fixed-wing drone cluster according to claim 3, wherein when performing collision detection in step S3, if the local collision airspace level currently corresponding to the controlled drone is the inner layer, a collision-collision detection method based on a collision-free sufficiency condition is adopted, and the specific steps include:
configuring sufficient conditions that collision conflict does not exist between a controlled unmanned aerial vehicle i in the unmanned aerial vehicle cluster and an unmanned aerial vehicle j in an inner-layer conflict airspace as follows:
|Pij(k)|≥2Rs；
wherein i, j belongs to omega, i is not equal to j, omega is the set of all unmanned aerial vehicles in the unmanned aerial vehicle cluster,
and the included angle between the relative velocity vector and the relative position vector of the two unmanned planes is shown.
And if the initial relative states of the two unmanned aerial vehicles meet the sufficient condition, judging that no collision conflict exists between the unmanned aerial vehicles, otherwise, judging that a collision conflict exists between the unmanned aerial vehicles.
7. The layered collision avoidance control method for the fixed-wing unmanned aerial vehicle cluster according to any one of claims 3 to 6, wherein when collision avoidance is performed in step S3, if the local collision airspace level corresponding to the controlled unmanned aerial vehicle is the outer layer, an online re-planning and avoiding control method combining a targets algorithm and a 3-time B-spline is adopted, and the method is implemented by selecting a collision-free sub-target point with minimum deviation and generating a smooth collision-free avoiding curve through 3-time B-spline fitting, and specifically comprises the following steps:
generating a group of collision-free path point sequences along the barrier edge by adopting a Subtargets algorithm based on the known reference path and the position of a collision object and by adopting an iterative point-taking method according to the principle of minimum deviation
Based on the obtained collision-free path point sequence
And interpolating and fitting the path point sequence by adopting a 3-time B spline algorithm to generate an expected smooth avoidance path, and tracking the generated smooth avoidance path by the controlled unmanned aerial vehicle to realize obstacle avoidance.
8. The method of claim 7, wherein the sequence of collision-free path points is based on a hierarchical collision avoidance control of a cluster of fixed-wing drones
The generation steps are specifically as follows:
① determining the conflicted obstacle set on the forward path by taking the current position of the controlled unmanned aerial vehicle as a starting coordinate point
② in the collection
To determine the first barrier on the forward pathA block
③ determining inclusion of obstacles
Set of all obstacles to be avoided
④ determining a set of avoidance obstacles
Includes determining forward/reverse bias directions of the secondary target points relative to the forward path, and determining a set
The minimum deflection angle required by each obstacle included in the direction, and the coordinates of the secondary target point are calculated according to the required maximum deflection angle and the corresponding obstacle position
⑤ repeating steps ① - ④ with the newly generated secondary target point as the starting coordinate point until the set of conflicting obstacles between the starting point and the stage target pointIf the sequence is empty, all the secondary target point sequences are returned to obtain the collision-free path point sequence
9. The layered collision avoidance control method for the fixed-wing drone cluster according to any one of claims 3 to 6, wherein when collision avoidance is executed in step S3, if the local collision airspace level currently corresponding to the controlled drone is the middle level, a distributed model predictive control method is adopted, an optimization objective function based on model predictive control is constructed by using a path tracking deviation amount and a control amount of the controlled drone, and a relative distance and a relative speed between the controlled drone and a collision object, and an optimization avoidance control sequence is generated by performing finite-step rolling optimization in each control cycle, and the specific steps include:
configuring a first objective function of collision avoidance control optimization based on model predictive control for non-cooperative barriers in an environment as follows:
wherein k ise,kωIs the cost coefficient of the image to be displayed,
for path tracking off-costs, eiIn order for the path to track the error,
to control energy consumption cost, UiIs the output of the control, and is,
in order to be a dangerous cost term for the collision,
for the set of all non-cooperative obstacles in the local airspace of the controlled drone at time k,
is the obstacle O in the setjA resulting collision risk cost function, and:
wherein k isd,kvRelative distance and relative velocity threat cost coefficients,
is the relative distance between the drone and the obstacle,
is the relative velocity between the drone and the obstacle;
the second objective function for building distributed model predictive control for adjacent unmanned aerial vehicles in the cluster is as follows:
wherein the content of the first and second substances,
a set of neighbor drones representing drones at time k,
and representing a collision danger cost function generated by the neighbor unmanned plane j to the unmanned plane i.
Synthesizing the first objective function and the second objective function to obtain an overall avoidance control objective function, and iteratively solving an optimized control sequence { U ] in each control period T0,U1,…,UN-1Generating an optimized avoidance control sequence by rolling optimization, taking N as a prediction step length, and using a first item U in the optimized avoidance control sequence0Act on the controlled unmanned aerial vehicle to realize collision avoidance.
10. The layered collision avoidance control method for the fixed-wing unmanned aerial vehicle cluster according to any one of claims 3 to 6, wherein when collision avoidance is executed in step S3, if the local collision airspace hierarchy corresponding to the controlled unmanned aerial vehicle is the inner layer, a reactive avoidance control law is generated based on a sufficient condition of no collision to achieve avoidance of inner layer collision, and the reactive avoidance control law specifically is as follows:
φρ＝∠-Pij(k)-φi(k)
where ρ isiIs a direction factor, wherein if the course adjustment is positive in the clockwise direction, the course angles of the two unmanned planes at the current moment are respectively phii(k)＝∠Vi(k) And phij(k)＝∠Vj(k)，ωmaxIs the maximum heading angular rate.
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