CN110908395A - Improved unmanned aerial vehicle flight path real-time planning method - Google Patents
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Abstract
The invention discloses an improved unmanned aerial vehicle track real-time planning method, which is used for solving the technical problem that the existing unmanned aerial vehicle target tracking track planning method is poor in moving target tracking real-time performance. The technical scheme is that according to the state information of the unmanned aerial vehicle and the tracked target, a flight route is planned in advance, and the unmanned aerial vehicle is guided to fly along the preset route. Because the tracking target is a maneuvering target, different from global planning of flying from a point A to a point B, the route planning can not be completed off line at one time, and the route planning must be performed on line in real time according to the positions of the unmanned aerial vehicle and the target, and the planning is performed while flying, so that the planned route reacts to maneuvering of the target in time. The method adopts model predictive control to replace global optimization with local optimization, so that the route planning of the unmanned aerial vehicle can respond to the dynamic change of the battlefield environment in time. Because the local optimization is needed to be calculated, near real-time solution can be achieved, the target is predicted in real time, the position of the target is updated in time, and the real-time performance of the flight path planning is better.
Description
Technical Field
The invention relates to a method for planning a target tracking flight path of an unmanned aerial vehicle, in particular to an improved method for planning the flight path of the unmanned aerial vehicle in real time.
Background
Aiming at the target tracking problem of the unmanned aerial vehicle, more researches are carried out at home and abroad. Eric studies the way that drones track transfer troops for troop transfer shield missions, assuming that troop locations are sent to drones in real time, i.e., the target is a cooperative target, therefore, the problem to be considered by drones is how to keep track of transfer troops at a specified distance. Dobrookodv introduces the state estimation of the target into a motion control strategy of the platform for consideration, estimates the state of the moving target while tracking the moving target, and then adopts a Lyapunov control method to keep the sensor continuously covering the target. However, the two methods do not fully consider the dynamic constraints of the drone. A target tracking algorithm of the fixed-wing unmanned aerial vehicle based on track point control is designed vigorously, and simulation results show that the algorithm is strong in real-time performance and high in reliability. The royal gao designs a subsection guiding law for generating an expected angle and an unmanned aerial vehicle steering control rate, and realizes the tracking of a ground non-cooperative static target. However, the two methods do not consider the barriers which can be met during the flight process, such as terrain, firepower threats and the like, which cause non-flight areas. The YaoYongjie proposes a tangent dynamic planning method to avoid threats in the process of tracking a moving target by an unmanned aerial vehicle, but only aims at the threats which are single and positioned between the starting point and the target of the unmanned aerial vehicle, cannot solve the situation of complex threats, and does not consider the influence of other factors such as terrain and the like. UgurZengin and Atilla Dogan put forward a depth gradient search algorithm, a simulation environment under a real-time path following strategy and enemy threats is given, the depth gradient search method determines the direction of the unmanned aerial vehicle, minimizes the threat degree and avoids a limited area, and a simulation result shows that the strategy can guide the unmanned aerial vehicle to avoid a no-fly zone and can well track a target, but the algorithm is high in complexity and difficult to realize real-time calculation on an onboard computer. Tay et al, in the paper "Development and Implementation for video based target Tracking System on Board Unmanned Aerial vehicle", provide a target Tracking strategy for Unmanned Aerial Vehicles, but have poor real-time Tracking for moving targets.
Disclosure of Invention
In order to overcome the defect that the existing unmanned aerial vehicle target tracking track planning method is poor in moving target tracking real-time performance, the invention provides an improved unmanned aerial vehicle track real-time planning method. The method is characterized in that a flight route is planned in advance according to state information of the unmanned aerial vehicle and a tracked target, and the unmanned aerial vehicle is guided to fly along the preset route. Because the tracking target is a maneuvering target, different from global planning of flying from a point A to a point B, the route planning can not be completed off line at one time, and the route planning must be performed on line in real time according to the positions of the unmanned aerial vehicle and the target, and the planning is performed while flying, so that the planned route reacts to maneuvering of the target in time. The method adopts model predictive control to replace global optimization with local optimization, so that the route planning of the unmanned aerial vehicle can respond to the dynamic change of the battlefield environment in time. Because the local optimum is calculated, near real-time solution can be realized, and the method is very suitable for real-time planning of the air route of the unmanned aerial vehicle; and the target is predicted in real time, and the position of the target is updated in time, so that the real-time performance of the flight path planning is better.
The technical scheme adopted by the invention for solving the technical problems is as follows: an improved unmanned aerial vehicle flight path real-time planning method is characterized by comprising the following steps:
step one, model prediction control. At each sampling moment, the current state of the system is used as an initial condition, and the optimal control sequence is obtained by optimizing the performance index so as to minimize the deviation between the output and the expectation. And at the next sampling moment, repeating the optimization process, and repeatedly rolling the whole control process along with the advance of time.
And (4) multi-step prediction. And taking the state information of the moving target at the current moment as an input parameter, performing multi-step prediction on the moving target by using a UKF algorithm, calculating the moving state information of the target at a plurality of unit times after the current moment, and selecting a prediction result as reference data tracked by the unmanned aerial vehicle.
And (4) optimizing rolling. In the optimization process, only the performance index from the current moment to a fixed time period later is considered, and when the next sampling moment comes, the considered time period of the performance index is also pushed backwards. In the process of target tracking of the unmanned aerial vehicle, planning q step lengths, optimizing only p step lengths, and searching the maximum value or the minimum value of an objective function:
the search result of the objective function is:
or
Then, determining a flight path with q steps.
And (5) feedback correction. And correcting the position of the navigation plan at each sampling moment by using the target measurement information from the last sampling moment to the sampling moment.
And step two, predicting the maneuvering target state. When the unmanned aerial vehicle tracks the maneuvering target, prediction estimation is carried out on the maneuvering target according to the measured value.
The target observation model is set as follows:
Z(k)＝HX(k)+V(k) (4)
wherein Z (k) is a measurement value; v (k) is the observed noise, and V (k) N (0, R), R is the noise covariance matrix.
Let W (k) and V (k) be independent of each other, and H is a sensor observation matrix and is an ideal measurer. For the collaborative turn model there are:
for the current statistical model there are:
the UKF algorithm is selected for the prediction of maneuver targets.
And step three, planning track points. Discretizing the flight path, and converting the path curve in the space into a sequence of path points to be suitable for the calculation of a computer program.
Suppose that at time t, the position of the unmanned aerial vehicle is O, and the unmanned aerial vehicle is at the maximum yaw rate
Fly within the allowable range, sayΔ t is the step size, there are 3 choices at each position, and the drone can choose to maintain heading or at angular rate
Left yaw δ or right yaw δ, there are 27 routings available within 3 Δ t.
Let the drone course point be (x, y, ψ), where x, y denote the two-dimensional plane position and ψ denotes the yaw angle. Suppose the track point O at time t is (x)0,y0,ψ0) Taking Δ t as 1, the state information (x) of the extended course point a is calculated from the geometric relationship0-Rsinψ1+Rsinψ0,y0+Rcosψ1-Rcosψ0,ψ1) Wherein
For unmanned aerial vehicle turning radius, V is unmanned aerial vehicle's speed.
The planning steps of the track point are as follows:
① current t0At the moment, the optimal track point sequence of the next N steps is planned by taking delta t as a step length;
②, taking the route points planned in the previous K steps as actual flight route points, wherein K is more than 0 and less than N;
③ at t0And the + K delta t moment repeats ①② steps until the task is finished.
And step four, solving a track point sequence by an A-star algorithm. Defining a merit function:
f(k)＝wgg(k)+whh(k) (7)
the specific steps of the algorithm are as follows:
① initializing OpenList and CloseList to be null, adding the start node S into OpenList;
② traversing OpenList, searching the node with the minimum f, setting the node as the current node, and simultaneously transferring the node from OpenList to CloseList;
③ If the current node is the target node
④ the following operations are performed on the neighbor nodes of the current node:
⑤ loop through steps ② - ⑤.
The evaluation function f (k) in the algorithm includes a consumption cost g (k) and an estimation cost h (k). Since the drone performs the target tracking task, the consumption cost g (k) is 0 in a non-threat environment. Estimating the cost h (k), and improving the heuristic function as follows:
the invention has the beneficial effects that: the method is characterized in that a flight route is planned in advance according to state information of the unmanned aerial vehicle and a tracked target, and the unmanned aerial vehicle is guided to fly along the preset route. Because the tracking target is a maneuvering target, different from global planning of flying from a point A to a point B, the route planning can not be completed off line at one time, and the route planning must be performed on line in real time according to the positions of the unmanned aerial vehicle and the target, and the planning is performed while flying, so that the planned route reacts to maneuvering of the target in time. The method adopts model predictive control to replace global optimization with local optimization, so that the route planning of the unmanned aerial vehicle can respond to the dynamic change of the battlefield environment in time. Because the local optimum is calculated, near real-time solution can be realized, and the method is very suitable for real-time planning of the air route of the unmanned aerial vehicle; and the target is predicted in real time, and the position of the target is updated in time, so that the real-time performance of the flight path planning is better.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
Fig. 1 is a flow chart of the improved real-time unmanned aerial vehicle flight path planning method of the present invention.
FIG. 2 is a block diagram of unmanned aerial vehicle target tracking path planning according to the method of the present invention.
FIG. 3 is a schematic diagram of a result of unmanned aerial vehicle target tracking flight path planning by the method of the present invention.
Detailed Description
Reference is made to fig. 1-3.
The hardware environment of the method of the invention is as follows: GPU: intel to strong series, memory: 8G, hard disk: 500G mechanical hard disk; the software environment is MATLAB 2014. The method comprises the following steps of (1) researching the route planning problem of an unmanned aerial vehicle by taking the target tracking executed by the unmanned aerial vehicle as a background; the UAV is subjected to real-time route planning by using the idea of model predictive control, the heuristic function in the A-star algorithm is corrected, the route planning of a target tracking part can be realized, the algorithm can be rapidly converged, and the operation rate is increased.
The improved unmanned aerial vehicle flight path real-time planning method specifically comprises the following steps:
(1) Multi-step prediction: from the analysis of the demand of the target tracking task, the target tracking system aims to control the unmanned aerial vehicle to be close to the target and keep a certain distance with the target, namely the distance between the unmanned aerial vehicle and the tracked ground target is used as the output of the system. On one hand, for the unmanned aerial vehicle, the state information of the unmanned aerial vehicle can be calculated according to the unmanned aerial vehicle model, and on the other hand, the system output is not only related to the position of the unmanned aerial vehicle, but also related to the position of the ground target, so that the ground target needs to be predicted.
(2) And (3) rolling optimization: the method is repeatedly carried out on line in a rolling mode, the optimization performance index of each sampling moment only considers a limited time period from the moment to the future, and the time period considered by the optimization index is also advanced to the next sampling moment. For the rolling optimization of the target tracking system of the invention, the mathematical description is as follows: setting q step lengths of unmanned aerial vehicle route planning, wherein the actual flight route is p step lengths, and p is less than or equal to q, and giving a performance index function at any time i:
the rolling optimization is to optimize the performance index function so as to maximize or minimize the objective function, that is:
or
Then, determining a flight path with q steps.
(3) And (3) feedback correction: the method mainly aims at calculating the position information of the ground target. Since the time interval for the unmanned aerial vehicle routings is much longer than the measurement time interval for the targets, the position of the unmanned aerial vehicle routings can be corrected at each sampling time of the routings by using the target measurement information from the last sampling time to the sampling time.
Step 2, maneuvering target state prediction: when the unmanned aerial vehicle tracks the maneuvering target, the unmanned aerial vehicle needs to be predicted and estimated according to the measured value.
The target observation model is set as follows:
Z(k)＝HX(k)+V(k) (4)
in the formula:
z (k) -measured values;
v (k) -observed noise, and V (k) -N (0, R), R is the noise covariance matrix.
Let W (k) and V (k) be independent of each other, and H is a sensor observation matrix and is an ideal measurer. For the collaborative turn model there are:
for the "current" statistical model there are:
the UKF (unknown Kalman Filter) algorithm is selected for the prediction of the maneuver targets. The UKF does not provide any additional condition for the system equation and the measurement equation, the algorithm is suitable for both linear objects and nonlinear objects, and the stronger the nonlinearity is, the stronger the superiority of the algorithm is.
Step 3, course point planning: an analytical expression method can be adopted to clearly express a route curve, but the expression form is not favorable for using a program in a computer to solve, and analytical expressions of some route curves are too complex or even cannot be analytically expressed. The invention discretizes the flight route, converts the route curve in the space into the sequence of route points, and is suitable for the calculation of a computer program.
Suppose at time t, the position of the drone is O, and then the drone can be at the maximum yaw rate
Allowing the flight within the range. Assuming a step size of Δ t (time interval between adjacent waypoints), there are 3 choices at each location, and the drone can choose to maintain heading or at angular rate
Left yaw δ or right yaw δ, there are 27 routings available within 3 Δ t.
Let the drone course point be (x, y, ψ), where x, y denote the two-dimensional plane position and ψ denotes the yaw angle. Suppose the track point O at time t is (x)0,y0,ψ0) Taking Δ t as 1, the state information (x) of the extended course point a can be calculated from the geometric relationship0-Rsinψ1+Rsinψ0,y0+Rcosψ1-Rcosψ0,ψ1) Wherein
For unmanned aerial vehicle turning radius, V is unmanned aerial vehicle's speed. Similarly, other extension node information may be obtained in turn.
The planning steps of the track point are as follows:
① current t0At the moment, the optimal track point sequence of the next N steps is planned by taking delta t as a step length;
②, taking the route points planned in the previous K steps as actual flight route points, wherein K is more than 0 and less than N;
③ at t0And the + K delta t moment repeats ①② steps until the task is finished.
Step 4, solving a track point sequence by an A-star algorithm: first, an evaluation function needs to be defined:
f(k)＝wgg(k)+whh(k) (7)
the specific steps of the algorithm are as follows:
① initializing OpenList and CloseList to be null, adding the start node S into OpenList;
② traversing OpenList, finding the node with the minimum f and setting the node as the current node, and transferring the node from OpenList to OpenList
CloseList；
③ If the current node is the target node
④ the following operations are performed on the neighbor nodes of the current node:
⑤ loop through steps ② - ⑤.
The evaluation function f (k) in the algorithm includes a consumption cost g (k) and an estimation cost h (k). Since the unmanned aerial vehicle executes the target tracking task, the oil consumption cost can be disregarded, and the consumption cost g (k) can be made 0 in a non-threat environment. The cost h (k) is estimated, sometimes also referred to as a heuristic function. In order to reflect the estimation cost of the current node well, the cost value f (k) of the previous searching node is not too small, so that the algorithm cannot be converged quickly due to the increase of iteration. Thus, the present invention modifies the heuristic function as follows:
Claims (1)
1. an improved unmanned aerial vehicle flight path real-time planning method is characterized by comprising the following steps:
step one, model prediction control; at each sampling moment, the current state of the system is used as an initial condition, and an optimal control sequence is obtained by optimizing the performance index so as to minimize the deviation between the output and the expectation; at the next sampling moment, the optimization process is repeated, and the whole control process is repeatedly rolled along with the advance of time;
multi-step prediction; taking the state information of the moving target at the current moment as an input parameter, performing multi-step prediction on the moving target by using a UKF algorithm, calculating the moving state information of the target at a plurality of unit times after the current moment, and selecting a prediction result as reference data tracked by the unmanned aerial vehicle;
optimizing rolling; in the optimization process, only the performance index from the current moment to a fixed time period later is considered, and when the next sampling moment comes, the considered time period of the performance index is also pushed backwards; in the process of target tracking of the unmanned aerial vehicle, planning q step lengths, optimizing only p step lengths, and searching the maximum value or the minimum value of an objective function:
the search result of the objective function is:
or
Then determining flight routes of q step lengths;
feedback correction; correcting the position of the navigation route planning at each sampling moment by using target measurement information from the last sampling moment to the sampling moment;
step two, predicting the maneuvering target state; when the unmanned aerial vehicle tracks the maneuvering target, carrying out prediction estimation on the maneuvering target according to the measured value;
the target observation model is set as follows:
Z(k)＝HX(k)+V(k) (4)
wherein Z (k) is a measurement value; v (k) is observed noise, and V (k) N (0, R), R is a noise covariance matrix;
setting W (k) and V (k) to be independent from each other, wherein H is a sensor observation matrix and is an ideal measurer; for the collaborative turn model there are:
for the current statistical model there are:
selecting a UKF algorithm for the prediction of the maneuvering target;
thirdly, planning track points; discretizing the flight route, and converting a route curve in the space into a sequence of route points to be suitable for resolving of a computer program;
suppose that at time t, the position of the unmanned aerial vehicle is O, and the unmanned aerial vehicle is at the maximum yaw rate
The flying is carried out within the allowable range,assuming a step size of Δ t, there are 3 choices at each location, the drone can choose to either maintain heading or at angular rate
If the left yaw delta or the right yaw delta is equal to the left yaw delta or the right yaw delta, 27 routes can be selected in 3 delta t;
let the unmanned aerial vehicle track point be (x, y, ψ), where x, y denote a two-dimensional plane position, ψ denotes a yaw angle; suppose the track point O at time t is (x)0,y0,ψ0) Taking Δ t as 1, the state information (x) of the extended course point a is calculated from the geometric relationship0-Rsinψ1+Rsinψ0,y0+Rcosψ1-Rcosψ0,ψ1) Wherein
The turning radius of the unmanned plane is V, and the speed of the unmanned plane is V;
the planning steps of the track point are as follows:
① current t0At the moment, the optimal track point sequence of the next N steps is planned by taking delta t as a step length;
②, taking the route points planned in the previous K steps as actual flight route points, wherein K is more than 0 and less than N;
③ at t0Repeating ①② steps at the moment of + K delta t until the task is finished;
step four, solving a track point sequence by an A-star algorithm; defining a merit function:
f(k)＝wgg(k)+whh(k) (7)
the specific steps of the algorithm are as follows:
① initializing OpenList and CloseList to be null, adding the start node S into OpenList;
② traversing OpenList, searching the node with the minimum f, setting the node as the current node, and simultaneously transferring the node from OpenList to CloseList;
③ If the current node is the target node
④ the following operations are performed on the neighbor nodes of the current node:
⑤ executing steps ② - ⑤;
the evaluation function f (k) in the A-algorithm comprises a consumption cost g (k) and an estimation cost h (k); since the unmanned aerial vehicle executes a target tracking task, the consumption cost g (k) is 0 in a non-threat environment; estimating the cost h (k), and improving the heuristic function as follows:
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