CN110488866B - Unmanned aerial vehicle formation obstacle avoidance method based on gradient function - Google Patents
Unmanned aerial vehicle formation obstacle avoidance method based on gradient function Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle formation obstacle avoidance method based on a gradient function, which comprises the following steps of (1) preparing a plurality of unmanned aerial vehicles; (2) specifying a starting position and a target position, wherein no obstacle exists around the target position; (3) starting taking off, moving each airplane to a respective target position, and scanning the position of the global airplane when one airplane moves by one step; (4) storing the value obtained by the latest scanning in the step (3) and preparing for the next updating; (5) calculating the distance between every two adjacent airplanes according to the latest stored data, and judging whether any airplane meets an obstacle avoidance condition, namely whether the closest distance is close to a safe distance; (6) entering a formation flying state, and assuming that an obstacle avoidance condition is met, establishing a model in an obstacle avoidance process; and (5) repeating the step (3), the step (4) and the step (5), and adopting the step (6) as an obstacle avoidance strategy. The invention better realizes the machine-to-machine dynamic obstacle avoidance in the cluster and makes up the defects in the traditional dynamic obstacle avoidance algorithm.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle formation obstacle avoidance and formation control, in particular to an unmanned aerial vehicle formation obstacle avoidance method based on a gradient function.
Background
With the continuous development of the current aerospace technology, the unmanned aerial vehicle is more and more widely applied in various social fields. In the occasion that the demand is constantly excavated, the application of a single unmanned aerial vehicle is more and more limited. For example, when a task is executed, the labor cost and the time cost of a single unmanned aerial vehicle are far greater than those of a plurality of unmanned aerial vehicles. As a result, the single frame has a narrow task execution range, which causes problems such as low execution efficiency.
The obstacle avoidance problem is a hot problem in the field of formation of multiple unmanned aerial vehicles, most of the traditional methods are based on two-dimensional planes, existing algorithms are improved, reliable models are not provided for the problems, and the problems in the models are not summarized.
Disclosure of Invention
The invention provides a trial model aiming at the problem of obstacle avoidance among airplanes in a cluster in the obstacle avoidance: according to the movement trend, the collision condition of the unmanned aerial vehicle in a certain range is refined and analyzed, and a front obstacle (rear-end collision) model and a rear collision (rear-end collision) model are obtained. Due explanation is given to the model, the problem of obstacle avoidance in a multi-unmanned aerial vehicle cluster under a two-dimensional plane is solved, and simulation application can be performed through experiments.
The invention is realized by the following technical scheme:
an unmanned aerial vehicle formation obstacle avoidance method based on a gradient function comprises the following steps
(1) Preparing a plurality of unmanned aerial vehicles;
(2) specifying a starting position and a target position;
a. determining the starting position of the unmanned aerial vehicle cluster;
b. determining a target position of an unmanned aerial vehicle cluster;
c. determining the position between the starting position and the target position, wherein no obstacle exists around the target position;
(3) starting to take off, moving each airplane to the respective target position, and scanning the global airplane position when one airplane moves by one step;
(4) storing the value obtained by the latest scanning in the step (3) and preparing for the next updating;
(5) calculating the distance between every two adjacent airplanes according to the latest stored data, and judging whether any airplane meets an obstacle avoidance condition, namely whether the closest distance is close to a safe distance;
(6) entering a formation flying state, and assuming that an obstacle avoidance condition is met, establishing a model and explaining an obstacle avoidance process as follows:
a. the front barrier model, r is the distance from the outermost periphery of the rotor wing to the geometric center of the airplane after the airplane takes off; r is the distance between the geometric centers of the airplanes A and B; assuming that the distance between two airplanes is L at a certain moment, the theoretical safety distance is defined as L being more than 2 r;
υ A and upsilon B Along planes A and B, respectively
A velocity component of direction and satisfies upsilon A ＜υ B That is, it means that the aircraft B approaches the safe distance of the aircraft a in a short time, and further collision may occur;
the avoidance scheme under the model is as follows: the airplane B enters an obstacle avoidance state and adopts a strategy of flying along the gradient direction to ensure that upsilon is B And (3) reducing: at this time is
Direction, velocity difference Δ ν (t) ═ ν B (t)-υ A (t), the maximum collision time is:
there are two directions of gradient direction in the plane, as required to reach the target position in the shortest time, assuming that aircraft B has selected according to the correlation algorithm
Direction, then only need to be guaranteed at t Δ (t) Δ ν (t) ═ 0; the two airplanes A and B do not collide, and the distance between every two airplanes is detected in real time in the global range, wherein the airplane A and the airplane B are the two closest airplanes detected at a certain moment;
meanwhile, according to the correlation algorithm, suppose that the airplane B is judged to be only along
The aircraft A and the aircraft B advance in the same direction and have the same track, the requirement that the delta upsilon (t) is 0 still needs to be met at the moment, and the strategy is changed to the strategy that the aircraft B follows
Decelerating the direction;
b. a rear collision model, wherein if the avoidance requirement is provided for the airplane A at the moment, the avoidance target of the airplane A is not collided by the airplane B; first, the aircraft A accelerates so that it follows
Velocity in direction v A Increase, decrease Δ ν (t), and extend t relatively Δ (t) simultaneously increasing the distance from the flight direction of the aircraft B along a certain gradient direction;
and (5) repeating the step (3), the step (4) and the step (5), and adopting the step (6) as an obstacle avoidance strategy.
The invention has the beneficial effects that:
1. the dynamic obstacle avoidance of the machine-to-machine in the machine group is better realized.
2. The defects in the traditional dynamic obstacle avoidance algorithm are overcome, and an effective reference scheme is provided.
3. An effective reference scheme is provided for the problem of complex formation transformation in the multi-unmanned aerial vehicle group cooperative formation.
Drawings
Fig. 1 is a schematic diagram of an obstacle avoidance model of the present invention.
Fig. 2 is a schematic diagram of an avoidance strategy of the front-barrier model of the present invention.
FIG. 3 is a schematic diagram of an avoidance strategy for a rear-impact model of the present invention.
FIG. 4 is a schematic flow diagram of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail in the following with reference to the attached drawings.
As shown in fig. 4, an unmanned aerial vehicle formation obstacle avoidance method based on a gradient function:
(1) preparing a plurality of unmanned aerial vehicles;
(2) specifying a start position and a target position (pattern);
a. determining the starting position of the unmanned aerial vehicle cluster;
b. determining a target position of an unmanned aerial vehicle cluster;
c. determining the position between the starting position and the target position, wherein no obstacle exists around the target position;
(3) starting taking off, moving each airplane to a respective target position, and scanning the position of the global airplane when one airplane moves by one step;
(4) storing the latest scanned value and preparing for next updating;
(5) calculating a safe distance according to the latest stored data, and judging whether an airplane meets an obstacle avoidance condition (whether the nearest distance is close to the safe distance or not);
(6) entering a formation flying state, and assuming that an obstacle avoidance condition is met, establishing a model and explaining an obstacle avoidance process as follows:
a. FIG. 1 is a model of the frontal barrier, and r is the distance from the outermost periphery of the rotor to the geometric center of the aircraft after the aircraft takes off. R is the distance between the geometric centers of aircraft a and aircraft B. Assuming that the distance between two airplanes is L at a certain time, the theoretical safe distance is defined as L > 2 r.
υ A And upsilon B Respectively being an aircraft A And B along the edge
A velocity component of direction and satisfies upsilon A ＜υ B I.e. it means that in a short time, the aircraft B will approach the safe distance of the aircraft a and a collision may occur.
The avoidance scheme under the model is as follows: the plane B enters an obstacle avoidance state and adopts a strategy of flying along a gradient direction, see figure 2, so that upsilon is enabled B And (3) reducing: at this time is
Direction, velocity difference Δ ν (t) ═ ν B (t)-υ A (t), the maximum collision time is:
there are two directions of gradient direction in the plane, according to the requirement of reaching the target position in the shortest time, assuming that aircraft B has selected according to the correlation algorithm
Direction, then only need to be guaranteed at t Δ In (t), Δ ν (t) is 0. The two airplanes a and B do not collide (the airplane B does not collide with the airplane a). It must be noted here that, globally, the distance between two airplanes is detected in real time, where the airplane a and the airplane B are the two closest airplanes detected at a certain time. Meanwhile, according to the correlation algorithm, suppose that the airplane B is judged to be only along
If the direction is advanced, the requirement that the delta upsilon (t) is 0 still needs to be met at the moment, and the strategy is changedBecome aircraft B along
The direction is decelerated.
b. The schematic diagram of the rear-end collision model is still shown in fig. 1, and assuming that an avoidance request is made for the aircraft a at the moment, as shown in fig. 3, the avoidance target of the aircraft a is not collided by the aircraft B. First, the aircraft A accelerates so that it follows
Velocity in direction v A Increase, decrease Δ ν (t), and extend t relatively Δ (t) simultaneously increasing the distance from the flight direction of the aircraft B along a certain gradient direction. And (5) repeating the steps (3), (4) and (5), and adopting the step (6) for the obstacle avoidance strategy.
Since modifications and improvements to the embodiments described above will occur to those skilled in the art based on the disclosure and teachings herein, the invention is not limited to the specific embodiments disclosed and described, and modifications and variations may be made to the invention without departing from the scope of the invention as defined in the appended claims. Furthermore, although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
Claims (1)
1. An unmanned aerial vehicle formation obstacle avoidance method based on a gradient function is characterized by comprising the following steps
(1) Preparing a plurality of unmanned aerial vehicles;
(2) specifying a starting position and a target position;
a. determining the starting position of the unmanned aerial vehicle cluster;
b. determining a target position of an unmanned aerial vehicle cluster;
c. determining the position between the starting position and the target position, wherein no obstacle exists around the target position;
(3) starting taking off, moving each airplane to a respective target position, and scanning the position of the global airplane when one airplane moves by one step;
(4) storing the value obtained by the latest scanning in the step (3) and preparing for the next updating;
(5) calculating the distance between every two adjacent airplanes according to the latest stored data, and judging whether any airplane meets an obstacle avoidance condition, namely whether the closest distance is close to a safe distance;
(6) entering a formation flying state, and establishing a model in an obstacle avoidance process on the assumption that obstacle avoidance conditions are met;
repeating the step (3), the step (4) and the step (5), wherein the step (6) is adopted as an obstacle avoidance strategy;
the model established in the obstacle avoidance process in the step (6) is explained as follows:
a. the front barrier model, r is the distance from the outermost periphery of the rotor wing to the geometric center of the airplane after the airplane takes off; r is the distance between the geometric centers of the airplanes A and B; assuming that the distance between two airplanes is L at a certain moment, the theoretical safety distance is defined as L being more than 2 r;
υ A and upsilon B Along planes A and B, respectively
A velocity component of direction and satisfies upsilon A ＜υ B That is, it means that the aircraft B approaches the safe distance of the aircraft a in a short time, and further collision may occur;
the avoidance scheme under the model is as follows: the airplane B enters an obstacle avoidance state and adopts a strategy of flying along the gradient direction to ensure that upsilon is B And (3) reducing: at this time is
Direction, velocity difference Δ ν (t) ═ ν B (t)-υ A (t), the maximum time to collision is:
there are two directions of gradient direction in the plane, as required to reach the target position in the shortest time, assuming that aircraft B has selected
Direction, then only need to be guaranteed at t Δ (t) Δ ν (t) ═ 0; the two airplanes A and B do not collide, and the distance between every two airplanes is detected in real time in the global range, wherein the airplane A and the airplane B are the two closest airplanes detected at a certain moment;
at the same time, suppose that aircraft B is determined to be only along
The aircraft A and the aircraft B advance in the same direction and have the same track, the requirement that the delta upsilon (t) is 0 still needs to be met at the moment, and the strategy is changed to the strategy that the aircraft B follows
Decelerating the direction;
b. a rear collision model, wherein if the avoidance requirement is provided for the airplane A at the moment, the avoidance target of the airplane A is not collided by the airplane B; first, the aircraft A accelerates so that it follows
Velocity in direction v A Increase, decrease Δ ν (t), and extend t relatively Δ (t) simultaneously increasing the distance from the flight direction of the aircraft B along a certain gradient direction.
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| AU3074401A (en) * | 1999-12-21 | 2001-07-03 | Lockheed Martin Corporation | Spatial avoidance method and apparatus |
| FR3050304B1 (en) * | 2016-04-19 | 2019-06-28 | Airbus Operations | METHOD AND SYSTEM FOR COLLISION AVOIDANCE FOR AN AIRCRAFT FOLLOWING AN AIRCRAFT FORMATION IN RELATION TO AN INTRUDED AIRCRAFT. |
| CN105974917B (en) * | 2016-05-11 | 2018-12-14 | 江苏大学 | A kind of vehicle obstacle-avoidance path planning research method based on novel artificial potential field method |
| CN106406354A (en) * | 2016-11-29 | 2017-02-15 | 中山大学 | Distributed aircraft formation method based on three-dimensional dynamic obstacle avoidance |
| CN106774401A (en) * | 2016-12-28 | 2017-05-31 | 深圳大漠大智控技术有限公司 | The track automatic generation method that a kind of unmanned plane is formed into columns when converting formation |
| CN108459612B (en) * | 2017-02-21 | 2021-03-23 | 北京航空航天大学 | Unmanned aerial vehicle formation control method and device based on artificial potential field method |
| CN108549407B (en) * | 2018-05-23 | 2020-11-13 | 哈尔滨工业大学（威海） | Control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance |
| CN108983825B (en) * | 2018-09-30 | 2020-04-03 | 北京航空航天大学 | Tracking method and system for time-varying formation of unmanned aerial vehicle |
| CN109814594B (en) * | 2019-01-18 | 2024-04-30 | 深圳市星图智控科技有限公司 | Unmanned aerial vehicle group anti-collision control method, device and computer readable storage medium |
| CN109976386B (en) * | 2019-04-04 | 2022-03-11 | 中国电子科技集团公司信息科学研究院 | Method and system for cooperatively tracking target by multiple unmanned aerial vehicles |
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