CN116301051A - Unmanned aerial vehicle cluster control and obstacle avoidance method and device - Google Patents
Unmanned aerial vehicle cluster control and obstacle avoidance method and device Download PDFInfo
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Abstract
The invention relates to a group control and obstacle avoidance method and device for unmanned aerial vehicles, belongs to the technical field of unmanned aerial vehicles, and solves the problems that the sensing capability, dynamic transformation capability and obstacle avoidance capability of the existing unmanned aerial vehicle to the external environment are insufficient to meet the obstacle avoidance requirement of the existing unmanned aerial vehicle. The method comprises the following steps: when part of unmanned aerial vehicles in the unmanned aerial vehicle cluster sense an obstacle, each unmanned aerial vehicle in the part of unmanned aerial vehicles positions the obstacle according to the self-positioning and obstacle sensing results and broadcasts the obstacle position in the unmanned aerial vehicle cluster; the unmanned aerial vehicle cluster carries out self-adaptive change according to the position of the obstacle and autonomously adjusts the self course of the unmanned aerial vehicle, so that the obstacle is always kept in the sight range of the unmanned aerial vehicle cluster; after the drone cluster passes the obstacle, the tail of the drone cluster remains facing the obstacle to ensure that the drone cluster is entirely out of range of the obstacle. The unmanned aerial vehicle cluster carries out self-adaptive change according to the obstacle and autonomously adjusts the course of the unmanned aerial vehicle so as to realize the large-range obstacle perception capability of the cluster.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for controlling a group of unmanned aerial vehicles and avoiding obstacles.
Background
Unmanned aerial vehicle clusters fly in open fields at present, and the dynamic transformation capacity, collision avoidance capacity and obstacle avoidance capacity of the clusters are all required to be higher when tasks are executed in the complex environments of cities; the types and the quantity of the sensors carried by the miniaturized unmanned aerial vehicle platform are limited, so that the perception capability of individuals in the cluster to the external environment is insufficient to meet the obstacle avoidance requirement of the individuals.
Disclosure of Invention
In view of the above analysis, the embodiments of the present invention aim to provide a method and apparatus for controlling a group of unmanned aerial vehicles and avoiding obstacles, which are used for solving the problem that the existing unmanned aerial vehicle has insufficient sensing capability, dynamic transformation capability and obstacle avoidance capability to the external environment to meet the obstacle avoidance requirements of itself.
In one aspect, an embodiment of the present invention provides a method for controlling a group of unmanned aerial vehicles and avoiding an obstacle, including: when part of unmanned aerial vehicles in the unmanned aerial vehicle cluster sense an obstacle, each unmanned aerial vehicle in the part of unmanned aerial vehicles positions the obstacle according to the self-positioning and obstacle sensing results and broadcasts the obstacle position in the unmanned aerial vehicle cluster; the unmanned aerial vehicle cluster carries out self-adaptive change according to the position of the obstacle and autonomously adjusts the course of the unmanned aerial vehicle, so that the obstacle is always kept in the sight range of the unmanned aerial vehicle cluster; and after the unmanned aerial vehicle cluster passes the obstacle, the tail of the unmanned aerial vehicle cluster remains facing the obstacle to ensure that the unmanned aerial vehicle cluster is entirely out of the range of the obstacle.
The beneficial effects of the technical scheme are as follows: the unmanned aerial vehicle cluster carries out self-adaptive change according to the obstacle position and autonomously adjusts the self course of the unmanned aerial vehicle, and the large-range obstacle sensing capability of the cluster is still realized under the condition that a single machine carries a small number of visual sensors, so that the size and weight limit of an engine body platform are effectively reduced.
Based on further improvement of the method, the unmanned aerial vehicle cluster adaptively changes according to the obstacle position, including the unmanned aerial vehicle cluster adaptively changes according to the obstacle position according to an aggregation criterion, a separation criterion and a speed matching criterion, wherein each unmanned aerial vehicle in the unmanned aerial vehicle cluster obtains position information of other unmanned aerial vehicles in the neighborhood according to the aggregation criterion, and the position and control algorithm of the unmanned aerial vehicle cluster are combined to calculate an expected state offset and feed the expected state offset back to a controller of the unmanned aerial vehicle cluster, so that the distance between the current unmanned aerial vehicle and the central position of the unmanned aerial vehicle cluster is gradually shortened to reach an aggregation state; the separation criterion is that distance offset between the position of each unmanned aerial vehicle and other unmanned aerial vehicle positions in the neighborhood is obtained, the distance offset is fed back to a self controller, when the distance offset is smaller than the safety distance between unmanned aerial vehicles, the self controller generates repulsive force inversely proportional to the distance offset, and the unmanned aerial vehicle clusters are separated in motion due to the repulsive force; and each unmanned aerial vehicle acquires the speed information of other unmanned aerial vehicles in the neighborhood in real time, compares the speeds of the other unmanned aerial vehicles with the current unmanned aerial vehicle to acquire a speed offset, and feeds the speed offset back to a self controller to carry out speed adjustment, so that the speed and the direction of all unmanned aerial vehicles in the unmanned aerial vehicle cluster are consistent.
Based on the further improvement of the method, the unmanned aerial vehicle cluster autonomously adjusts the course of the unmanned aerial vehicle according to the obstacle position, including the unmanned aerial vehicle cluster adjusts the course of the unmanned aerial vehicle according to the obstacle position and the visual angle distribution criterion, wherein the visual angle distribution criterion is that each unmanned aerial vehicle in the unmanned aerial vehicle cluster is uniformly distributed by adjusting the direction, so that the sight angles of the unmanned aerial vehicle cluster take the unmanned aerial vehicle cluster as the center, and the expected course angles are fed back to the self controller by acquiring the positions and the directions of other unmanned aerial vehicles in the neighborhood, so that compared with the unmanned aerial vehicle cluster with the sight angles set as uniform distribution, the unmanned aerial vehicle cluster with the uniformly distributed sight angles obtains a larger sight range.
Based on the further improvement of the method, the unmanned aerial vehicle cluster control and obstacle avoidance method further comprises the step of carrying out cluster control on unmanned aerial vehicle clusters with random distribution of initial positions by using a cluster control algorithm to realize the following steps: distances among the unmanned aerial vehicles in the unmanned aerial vehicle cluster tend to be consistent; the unmanned aerial vehicle cluster moves to an expected direction by taking a virtual pilot as a center; automatically avoiding an obstacle when the unmanned aerial vehicle cluster encounters the obstacle; and (5) gathering the clusters towards the center again after passing through the range of the obstacle, and recovering formation.
Based on a further improvement of the above method, the cluster control algorithm comprises: control input u of ith unmanned aerial vehicle i And w i Wherein based on the control input u i Controlling the relative position and speed between the unmanned aerial vehicles in the unmanned aerial vehicle cluster based on a control input w i And controlling the line-of-sight angular distribution of the unmanned aerial vehicle in the unmanned aerial vehicle cluster.
Based on the further improvement of the method, the unmanned aerial vehicle-mounted vision sensor is fixedly connected to the machine body, and the control input w of the ith unmanned aerial vehicle is expressed by the following formula i ：
w i ＝k i *(2π*i/N i -γ i )
Wherein k is i Gain, gamma, of course control of unmanned aerial vehicle i i For the heading angle of the ith unmanned aerial vehicle, N i For the i-th unmanned plane neighborhood:
N i ＝{||q j -q i ||＜r，j≠i，j＝1，...n}
i and j are unmanned aerial vehicle numbers, and r is unmanned aerial vehicle perception radius.
Based on a further improvement of the method, the control input u of the ith unmanned aerial vehicle is determined according to the general potential energy V (q) of the unmanned aerial vehicle cluster expressed by the following formula i ：
Wherein q is a set of positions of all unmanned aerial vehicles in the unmanned aerial vehicle cluster, ψ α (z) is the current distance z to the desired distance d α Potential energy integral of (2)：
φ α Is the attractive force between two unmanned aerial vehicles:
wherein a, b, c, h is a curve parameter, r α As the sensing distance of the unmanned aerial vehicle is equal to or larger than the norm z, the norm z is equal to or larger than the z=0, and the norm z is equal to or larger than the norm z which is equal to or larger than the norm z and is equal to or larger than the norm z σ Norm z σ For constructing a smooth artificial potential field function, epsilon being a norm parameter.
Based on a further development of the above method, the control input u of the ith drone is expressed by the following formula i Control input for formation entry of ith drone
Virtual pilot feedback item control input of unmanned plane i>
Virtual agent feedback item control input to unmanned plane i>
And (2) sum:
wherein a is ij Indicating whether the unmanned aerial vehicle i and j are communicated before, 0 indicates non-communication, 1 indicates communication, n ij Directional vector q for unmanned plane i to unmanned plane j l For the position vector of the virtual pilot, p l A velocity vector, c, for the virtual pilot 1 And c 2 The position parameter and the speed parameter of the virtual pilot respectively,
for the set of virtual agents, < >>
And
the position vector and the speed vector of the jth virtual intelligent agent and the communication condition of the jth virtual intelligent agent and the unmanned plane i are respectively n ij A direction vector pointing to the virtual agent j for the drone i.
Based on further improvement of the method, the virtual intelligent body is generated closest to the unmanned aerial vehicle from the obstacle, repulsive force is generated for the unmanned aerial vehicle when the virtual intelligent body is within the safety distance of the unmanned aerial vehicle, the closer the distance is, the larger the distance is, the unmanned aerial vehicle can decelerate under the influence of the repulsive force and move in the direction away from the obstacle when approaching the obstacle, and the unmanned aerial vehicle can automatically avoid the obstacle when moving away from the virtual intelligent body under the action of the repulsive force until the obstacle is in the safety distance.
In another aspect, an embodiment of the present invention provides an unmanned aerial vehicle cluster control and obstacle avoidance apparatus, including: the obstacle data acquisition module is used for positioning the position of an obstacle according to the self positioning and obstacle sensing results of each unmanned aerial vehicle in the part of unmanned aerial vehicles when the part of unmanned aerial vehicles in the unmanned aerial vehicle cluster senses the obstacle; a data transmission module for broadcasting the obstacle location in the unmanned aerial vehicle cluster; the adjusting module is used for carrying out self-adaptive change according to the position of the obstacle through the unmanned aerial vehicle cluster and automatically adjusting the self course of the unmanned aerial vehicle, so that the obstacle is always kept in the sight range of the unmanned aerial vehicle cluster; and a post-processing module, configured to keep a tail of the unmanned aerial vehicle cluster facing the obstacle after the unmanned aerial vehicle cluster passes the obstacle so as to ensure that the unmanned aerial vehicle cluster is entirely out of the range of the obstacle.
Compared with the prior art, the invention has at least one of the following beneficial effects:
1. the unmanned aerial vehicle cluster adaptively changes a control algorithm of the cluster formation according to the distribution of the obstacles in the environment, so that the flight requirement of the cluster in a complex environment is met;
2. the unmanned aerial vehicle cluster carries out self-adaptive change according to the obstacle position and autonomously adjusts the self course of the unmanned aerial vehicle, and the large-range obstacle sensing capability of the cluster is still realized under the condition that a single machine carries a small number of visual sensors, so that the size and weight limit of an engine body platform are effectively reduced.
In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
Fig. 1 is a flowchart of a method for controlling a group of unmanned aerial vehicles and avoiding an obstacle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of cluster control and obstacle avoidance according to an embodiment of the present invention;
FIG. 3 is a block diagram of a cluster control interaction relationship according to an embodiment of the invention;
FIGS. 4a, 4b, 4c and 4d are respectively aggregation, classification, velocity matching and perspective profiles according to embodiments of the present invention;
fig. 5a, 5b, 5c, 5d, 5e and 5f are graphs of a cluster formation process according to an embodiment of the present invention, respectively;
FIG. 6 is a diagram of the principle of an obstacle avoidance algorithm according to an embodiment of the present invention;
FIGS. 7a, 7b, 7c, 7d, 7e and 7f are graphs of cluster control and obstacle avoidance algorithm tests, respectively, according to embodiments of the present invention;
FIG. 8 is a block diagram of a cluster simulation system in accordance with an embodiment of the invention;
FIG. 9 is a block diagram of a clustered semi-physical simulation system in accordance with an embodiment of the invention;
fig. 10 is a block diagram of a drone cluster control and obstacle avoidance device according to an embodiment of the invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
Referring to fig. 1, a method for controlling a group of unmanned aerial vehicles and avoiding obstacles is disclosed, which comprises: in step S102, when a part of unmanned aerial vehicles in the unmanned aerial vehicle cluster sense an obstacle, each unmanned aerial vehicle in the part of unmanned aerial vehicles locates an obstacle position according to the self-location and obstacle sensing results and broadcasts the obstacle position in the unmanned aerial vehicle cluster; in step S104, the unmanned aerial vehicle cluster adaptively changes according to the position of the obstacle and autonomously adjusts the own heading of the unmanned aerial vehicle, so that the obstacle is always kept in the sight range of the unmanned aerial vehicle cluster; and in step S106, after the unmanned aerial vehicle cluster passes the obstacle, the tail of the unmanned aerial vehicle cluster remains facing the obstacle to ensure that the unmanned aerial vehicle cluster is entirely out of the range of the obstacle.
Compared with the prior art, in the unmanned aerial vehicle cluster control and obstacle avoidance method provided by the embodiment, the unmanned aerial vehicle cluster adaptively changes according to the position of the obstacle and autonomously adjusts the course of the unmanned aerial vehicle, so that the large-range obstacle sensing capability of the cluster is still realized under the condition that a single machine carries a small number of visual sensors, and the size and weight limit of an organism platform are effectively reduced.
Hereinafter, each step of the unmanned aerial vehicle cluster control and obstacle avoidance method according to the embodiment of the present invention will be described in detail with reference to fig. 1.
In step S102, when a part of the unmanned aerial vehicles in the unmanned aerial vehicle cluster sense an obstacle, each unmanned aerial vehicle in the part of the unmanned aerial vehicles locates an obstacle position according to the self-location and obstacle sensing result and broadcasts the obstacle position in the unmanned aerial vehicle cluster.
In step S104, the unmanned aerial vehicle cluster performs adaptive change according to the position of the obstacle and autonomously adjusts the own heading of the unmanned aerial vehicle, so that the obstacle is always kept within the line of sight of the unmanned aerial vehicle cluster. The unmanned aerial vehicle cluster adaptively changes according to the obstacle position, which comprises the unmanned aerial vehicle cluster adaptively changes according to the obstacle position, an aggregation criterion, a separation criterion and a speed matching criterion, wherein the aggregation criterion is that each unmanned aerial vehicle in the unmanned aerial vehicle cluster obtains the position information of other unmanned aerial vehicles in the neighborhood, and the expected state offset is calculated by combining the position of the unmanned aerial vehicle cluster and a control algorithm and fed back to a controller of the unmanned aerial vehicle cluster, so that the distance between the current unmanned aerial vehicle and the central position of the unmanned aerial vehicle cluster is gradually shortened to reach an aggregation state; the separation criterion is that distance offset between the position of each unmanned aerial vehicle and other unmanned aerial vehicle positions in the neighborhood is obtained, the distance offset is fed back to the self controller, and when the distance offset is smaller than the safety distance between unmanned aerial vehicles, the self controller generates repulsive force inversely proportional to the distance offset, and the unmanned aerial vehicle clusters are separated in motion due to the repulsive force; and (3) acquiring speed information of other unmanned aerial vehicles in the neighborhood by each unmanned aerial vehicle in real time according to a speed matching criterion, comparing the speeds of the other unmanned aerial vehicles with the current speed of the unmanned aerial vehicle to acquire a speed offset, feeding the speed offset back to a self controller to carry out speed adjustment, and enabling the speeds and directions of all unmanned aerial vehicles in the unmanned aerial vehicle cluster to be consistent. Unmanned aerial vehicle cluster is according to barrier position autonomous adjustment unmanned aerial vehicle self course includes unmanned aerial vehicle cluster according to barrier position according to visual angle distribution criterion adjustment unmanned aerial vehicle self course, wherein, visual angle distribution criterion is for every unmanned aerial vehicle in the unmanned aerial vehicle cluster through adjustment orientation, make unmanned aerial vehicle cluster's sight angle regard as central evenly distributed, and through obtaining other unmanned aerial vehicle's in the neighborhood position and orientation, feedback the expected course angle to self controller, make compare in the sight angle for setting for evenly distributed unmanned aerial vehicle cluster, sight angle evenly distributed's unmanned aerial vehicle cluster obtains great sight scope. Specifically, the directions of the sight lines of unmanned aerial vehicles in the formation of the prior art are the same, and the sight line range is enlarged compared with the prior art after the unmanned aerial vehicle group control and obstacle avoidance method is improved.
In step S106, after the drone cluster passes the obstacle, the tail of the drone cluster remains facing the obstacle to ensure that the drone cluster is entirely out of range of the obstacle.
The unmanned aerial vehicle cluster control and obstacle avoidance method further comprises the step of carrying out cluster control on unmanned aerial vehicle clusters with random distribution of initial positions by using a cluster control algorithm to realize the following steps: the distances among the unmanned aerial vehicles in the unmanned aerial vehicle cluster tend to be consistent; the unmanned aerial vehicle cluster moves to an expected direction by taking the virtual pilot as a center; the method comprises the steps that when an unmanned aerial vehicle cluster encounters an obstacle, the obstacle is automatically avoided; and (5) gathering the clusters towards the center again after passing through the range of the obstacle, and recovering formation.
The cluster control algorithm comprises the following steps: control input u of ith unmanned aerial vehicle i And w i Wherein based on the control input u i Controlling relative position and speed between unmanned aerial vehicles in unmanned aerial vehicle cluster based on control input w i And controlling the line-of-sight angle distribution of the unmanned aerial vehicle in the unmanned aerial vehicle cluster.
The unmanned aerial vehicle airborne vision sensor is fixedly connected onto the machine body, and the control input w of the ith unmanned aerial vehicle is represented by the following formula i ：
w i ＝k i *(2π*i/N i -γ i )
Wherein k is i Gain, gamma, of course control of unmanned aerial vehicle i i For the heading angle of the ith unmanned aerial vehicle, N i Is the neighborhood of the ith unmanned aerial vehicle
N i ＝{||q j -q i ||＜r，j≠i，j＝1，...n}
i and j are unmanned aerial vehicle numbers, and r is unmanned aerial vehicle perception radius.
The total potential energy is the sum of potential energy describing unmanned aerial vehicles, and can be proved to be the lowest under the input of design in mathematics, and the corresponding is that the clusters stably form the formation. Determining a control input u of an ith unmanned aerial vehicle based on an overall potential V (q) of the unmanned aerial vehicle cluster expressed by the following formula i ：
Wherein q is the position set of all unmanned aerial vehicles in the unmanned aerial vehicle cluster, ψ α (z) is the current distance z to the desired distance d α Potential energy integration of (2):
φ α is the attractive force between two unmanned aerial vehicles:
wherein a, b, c, h is a curve parameter for comprehensively determining a change curve of virtual force between unmanned aerial vehicles along with distance between unmanned aerial vehicles, r α For sensing distance, specifically, the unmanned aerial vehicle tracks the distance of other unmanned aerial vehicles, and as the norm z is not led at z=0, the norm z is rewritten as the everywhere led norm z σ Norm z σ For constructing a smooth artificial potential field function, epsilon being a norm parameter.
The control input u of the ith unmanned aerial vehicle is represented by the following formula i Control input for formation entry of ith drone
Virtual pilot feedback item control input of unmanned plane i>
Virtual agent feedback item control input to unmanned plane i>
And (2) sum:
wherein a is ij Indicating whether the unmanned aerial vehicle i and j are communicated before, 0 indicates non-communication, 1 indicates communication, n ij Directional vector q for unmanned plane i to unmanned plane j l Position vector p for virtual pilot l Velocity vector, c, being the virtual pilot 1 And c 2 The position parameter and the speed parameter of the virtual pilot respectively,
for the set of virtual agents, < >>
And->
The position vector and the speed vector of the jth virtual intelligent agent and the communication condition of the jth virtual intelligent agent and the unmanned plane i are respectively n ij A direction vector pointing to the virtual agent j for the drone i.
The virtual intelligent body is generated closest to the unmanned aerial vehicle from the obstacle, repulsive force is generated for the unmanned aerial vehicle when the virtual intelligent body is within the safety distance of the unmanned aerial vehicle, the closer the distance is, the larger the distance is, the unmanned aerial vehicle can decelerate under the influence of the repulsive force and move in the direction away from the obstacle when approaching the obstacle, and the unmanned aerial vehicle can automatically avoid the obstacle until the obstacle is in the safety distance when moving away from the virtual intelligent body under the action of the repulsive force.
Referring to fig. 10, in one embodiment of the present invention, a cluster control and obstacle avoidance device for unmanned aerial vehicles is disclosed, comprising: the obstacle data acquisition module 1002 is configured to locate an obstacle position according to a self-location and an obstacle sensing result by each unmanned aerial vehicle in the part of unmanned aerial vehicles when the part of unmanned aerial vehicles in the unmanned aerial vehicle cluster senses an obstacle; a data transmission module 1004, configured to broadcast an obstacle location in the unmanned aerial vehicle cluster; the adjusting module 1006 is configured to perform adaptive change according to the position of the obstacle through the unmanned aerial vehicle cluster and autonomously adjust the own heading of the unmanned aerial vehicle, so that the obstacle is always kept within the line of sight of the unmanned aerial vehicle cluster; and a post-processing module 1008 for maintaining the tail of the drone cluster facing the obstacle after the drone cluster passes the obstacle to ensure that the drone cluster is entirely out of range of the obstacle.
Hereinafter, a detailed description will be given of a method and apparatus for controlling and avoiding a group of unmanned aerial vehicles according to an embodiment of the present invention, by way of specific examples, with reference to fig. 2 to 9.
Individuals in the unmanned aerial vehicle cluster perform local environment sensing based on the airborne sensors and the computing unit, and environment information sharing and distributed processing are performed among the individuals through data links. By increasing the number of individuals in the cluster, the large-range environment sensing capability is developed, and the self-adaptive formation transformation and autonomous obstacle avoidance of the cluster are realized. As shown in fig. 2, when the unmanned aerial vehicle cluster encounters an obstacle, only a part of unmanned aerial vehicles find the obstacle due to the limitation of the perception range, and the part of unmanned aerial vehicles position the obstacle according to the self-positioning and obstacle perception results and broadcast in the cluster, so that the cluster adaptively changes according to the obstacle distribution to cope with a narrow channel. When the clusters pass through the channel, the unmanned aerial vehicle automatically adjusts own course according to surrounding obstacle distribution, so that the obstacles are always in the sight range of the clusters, and when the clusters pass through the obstacles, the tail of the clusters keeps facing the obstacles, so that the whole obstacle range is ensured to be separated.
And communication is carried out between unmanned aerial vehicles in the cluster and between the unmanned aerial vehicles and the ground station through a data link. The individual unmanned aerial vehicle carries the difference positioning system, the vision sensor and the airborne computing unit, can accurately obtain the position information of the individual unmanned aerial vehicle and the position information of the obstacle, and the design scheme is shown in fig. 3 on the basis. The formation control module input of the individual unmanned aerial vehicle is three parts: 1) Ground station data including formation control instructions such as take-off, landing, formation, and release, etc., and formation parameters such as formation and formation spacing, etc.; 2) Status data of other unmanned aerial vehicles in the cluster; 3) And the obstacle data acquired by all unmanned aerial vehicles in the cluster are global obstacle databases formed by obstacle matching. The formation control module outputs expected postures, speeds or positions based on the input data and the control algorithm, and the collision avoidance optimization module corrects expected values according to the states of other surrounding individuals and then transmits the corrected expected values to the autopilot so as to control the individual motions to meet the cluster motion requirements.
And finally, carrying out unmanned aerial vehicle cluster flight test, integrating a cluster control and obstacle avoidance algorithm into an unmanned aerial vehicle platform, and further verifying the effectiveness of the algorithm.
The specific implementation process is as follows:
(1) Cluster bee congestion control algorithm with virtual pilot
Reynolds puts forward three rules of group behaviors according to the behavior consistency of biological groups such as shoal birds, shoal fish and the like in the foraging process and the non-collision phenomenon of competing for food: aggregation (Flocking Centering), separation (Collision Avoidance) and speed matching (Velocity Matching), combining them with the range and distance limitations of the unmanned aerial vehicle on-board visual sensor, summarizing four basic criteria for unmanned aerial vehicle clusters:
(1) polymerization: all individuals within each individual neighborhood in the cluster remain in a compact formation, in terms of consistency of individual location and cluster cohesion. The aggregation principle is shown in the following figure 4a, because the individuals have the perception capability, each individual can obtain the position information of other individuals in the neighborhood, and the expected state offset is calculated by combining the positions of the individuals and the control algorithm and fed back to the controller, so that the distance between the individual and the central position of the cluster is gradually shortened, and finally the aggregation state is achieved.
(2) Separating: each individual in the cluster can keep a certain distance with all individuals in the field, collision is avoided in the formation process of the aggregation state, the separation principle is as shown in the following figure 4b, and the difference value is obtained by judging the distance between the positions of other individuals in the neighborhood and the position of the individual, the distance offset is fed back to the controller of the individual, when the offset is smaller than the safety distance between the individuals, the individual generates a repulsive force, the repulsive force is inversely proportional to the distance offset, and the separation of the individuals is realized in the movement of the cluster due to the existence of the repulsive force.
(3) Speed matching: the speed of the individuals in the cluster is consistent with that of all other individuals in the neighborhood. The speed matching principle is shown in the following figure 4c, and also because of the perception of the individuals, the individuals can acquire the speed information of other individuals in the neighborhood in real time, and the speed offset obtained by comparing the speed information with the own speed is fed back to the own control system for speed adjustment, so that the speed and the direction of all the individuals in the cluster are consistent.
(4) View angle distribution: the individual in the cluster is oriented by adjusting the direction, so that the sight angles of the cluster are uniformly distributed by taking the cluster as the center. The view angle distribution principle is shown in the following figure 4d, and also because of the individual perception, the positions and orientations of other individuals in the neighborhood can be obtained, and the expected course angle is calculated according to the control algorithm and fed back to the own controller, so that the cluster obtains a larger view range.
Considering that a rotor unmanned aerial vehicle is usually carried with an autopilot to realize position and gesture control and motion characteristics of hovering in the air and rotating heading, a mathematical model of a cluster is simplified into a particle model, and the particle model is shown in the following formula, wherein n is the number of unmanned aerial vehicles in the cluster, and p i 、q i And gamma i Position vector, speed vector and course angle of ith unmanned aerial vehicle, u i And w is equal to i Control input for the ith drone, where u i The control input acts to control the relative position and speed, w, between the individuals in the cluster i The control input functions to control the angular distribution of the line of sight of the individuals in the cluster.
In an outdoor environment, the number of other unmanned aerial vehicles that can be perceived by the unmanned aerial vehicle depends on the performance of the communication system, and the unmanned aerial vehicle can be assumed to sense other unmanned aerial vehicles within the own neighborhood radius, and the neighborhood (the neighborhood set representing unmanned aerial vehicle i) is defined as:
N i ＝{||q j -q i ||＜r，j≠i，j＝1，...n}
wherein i and j are unmanned aerial vehicle numbers, and r is unmanned aerial vehicle perception radius. If the drone is considered a node and connects each node and all nodes in its neighborhood with undirected edges, then these nodes are available:
V＝{1，...，n}
edge-blending
E＝{(i，j)∈V×V：i≠j}
The composed network can be represented by an undirected graph G. For a certain unmanned aerial vehicle in the network, some unmanned aerial vehicles in the neighborhood range of the unmanned aerial vehicle in the previous period move out of the neighborhood of the unmanned aerial vehicle at a certain moment, or some unmanned aerial vehicles in the neighborhood range of the unmanned aerial vehicle in the previous period do not move into the neighborhood of the unmanned aerial vehicle at a certain moment, so that the system has a switching topological structure.
The design objective of the cluster control algorithm is to design a proper buzzing control algorithm by combining the artificial potential energy function method aiming at the unmanned aerial vehicle cluster system, and the algorithm can obtain the following effects after applying control input to each unmanned aerial vehicle under the condition of meeting the initial topology communication of clusters:
(1) all the intelligent agents in the system are always connected in the movement process, so that the phenomenon of splitting does not occur;
(2) the speed of all the intelligent agents in the system gradually reaches a certain value, and the central speed v of the group is kept constant;
(3) the relative distances among all the intelligent agents in the system gradually converge to a certain range;
(4) the intelligent agents in the system do not collide with each other in the motion process.
Considering that an unmanned aerial vehicle-mounted vision sensor is fixedly connected to a machine body, a course angle of the unmanned aerial vehicle, namely, a direction of a sight line, is represented, and a control input w is designed i Wherein k is as follows i Gain for heading control of unmanned aerial vehicle i:
w i ＝k i *(2π*i/N i -γ i )
the overall potential energy is the sum of the potential energy describing the unmanned aerial vehicle, and can be proved in mathematics in the control input u of design i The lower global potential energy will converge to the lowest, corresponding to stable formation of clusters. According to the control input u i The general potential energy V (q) of the unmanned aerial vehicle cluster is designed to be:
q is a position set of all unmanned aerial vehicles in the cluster, and the rest of the items are defined as follows:
where z is the current distance, d α To a desired distance, ψ α (z) is the potential energy integral of the current distance to the desired distance.
Wherein a, b, c, h is a parameter, r α Is the perceived distance. Since the norm z is not derivable at z=0, it is rewritten as the everywhere derivable norm z σ The purpose of constructing the norms in this way is to construct a smooth and accessible artificial potential field function, where epsilon is the norming parameter.
The control input of the unmanned aerial vehicle is obtained by calculating the virtual force of the unmanned aerial vehicle in the potential energy field and combining the requirements of the speed consistency item, wherein the control input is shown in the following formula
Control input for a formation entry for unmanned aerial vehicle i, a ij Indicating whether the unmanned aerial vehicle i and j are communicated before, 0 indicates non-communication, 1 indicates communication, n ij And (5) pointing the unmanned aerial vehicle i to a direction vector of the unmanned aerial vehicle j. The Lyapunov two method can prove that when the initial state of the system is connected, the system finally tends to be stable and shapedIn grid-like formations as shown in fig. 5a, 5b, 5c, 5d, 5e and 5 f. The initial positions of the individuals in the unmanned aerial vehicle clusters are randomly distributed, the clusters are slowly gathered under the action of a cluster control algorithm, and finally grid-shaped formation with equal distances among the unmanned aerial vehicles is formed, wherein all the unmanned aerial vehicles are located at safe distances and face the same direction.
Considering that the unmanned aerial vehicle cluster needs to move according to a set track, it is assumed that a virtual navigator exists in the cluster, and the navigator has a position vector q l And a velocity vector p l Pilot feedback item control input of unmanned plane i
Of the formula wherein c 1 And c 2 The method is characterized in that parameters are controlled for the virtual pilot, and the function of the parameters is to ensure that no one can form an aggregation state by taking the virtual pilot as a center and ensure that the consistency speed of the clusters is the same as the speed of the pilot.
According to the unmanned aerial vehicle cluster obstacle avoidance requirement, the characteristics that the unmanned aerial vehicle can obtain object distance information in a local view field range through the binocular vision sensor are combined, a virtual intelligent object generation algorithm is designed, an onboard computer obtains the distance between the unmanned aerial vehicle and an obstacle and the obstacle surface range according to depth image information, the obstacle avoidance algorithm automatically generates a virtual intelligent object on the obstacle surface, when the intelligent object is in the safe distance of the unmanned aerial vehicle, a repulsive force can be generated for the unmanned aerial vehicle, and when the unmanned aerial vehicle is far away from the virtual intelligent object under the action of the repulsive force, the automatic obstacle avoidance is realized, as shown in fig. 6. When an obstacle appears in the sight range of the unmanned aerial vehicle, a virtual intelligent body is generated at the nearest position of the obstacle to the unmanned aerial vehicle by utilizing the distance information of the depth image, the intelligent body can generate virtual repulsive force to the unmanned aerial vehicle, the closer the distance is, the larger the distance is, the slower the unmanned aerial vehicle can decelerate under the influence of the unmanned aerial vehicle when the unmanned aerial vehicle approaches the obstacle and moves in the direction away from the obstacle, the obstacle is known to be at the safe distance, and the obstacle avoidance effect of an individual is achieved.
Therefore, pilot feedback item control input of unmanned plane i is designed
Is of the formula wherein->
For the set of virtual agents, < >>
And->
The position vector and the speed vector of the jth virtual intelligent agent and the communication condition of the jth virtual intelligent agent and the unmanned plane i are respectively n ij A direction vector pointing to the virtual agent j for the drone i.
Thus synthetically available unmanned control input u i The method comprises the following steps:
the cluster control and obstacle avoidance effects are shown in fig. 7a, 7b, 7c, 7d, 7e and 7f, the unmanned aerial vehicle clusters can move along with the virtual pilot by grid-shaped distributed formation, and when encountering an obstacle, the unmanned aerial vehicle clusters can automatically avoid the obstacle and re-form the formation after crossing the obstacle. These descriptions are actual effects of cluster control and obstacle avoidance, and the corresponding process descriptions of fig. 2 are examples.
The initial positions of the individuals in the unmanned aerial vehicle cluster are randomly distributed, and the cluster is realized under the action of a cluster control algorithm: 1) The distances between unmanned aerial vehicles tend to be consistent; 2) The clusters move to the expected direction by taking the virtual navigator as the center; 3) The clusters encounter obstacles to automatically avoid; 4) And (5) gathering the clusters towards the center again after passing through the range of the obstacle, and recovering formation.
(2) Cluster software simulation system
For the cluster control and obstacle avoidance algorithm provided in the project, firstly, research and software simulation verification are performed on a cluster simulation system, and the cluster simulation system designed in the project is shown in fig. 8:
the system mainly comprises cluster simulation software, wherein the software consists of six parts, namely an unmanned plane module, a flight control module, a sensor module, an environment module and a rendering module.
(1) Unmanned aerial vehicle module: based on the control signal of the flight control and the state information at the moment, carrying out dynamics and kinematics calculation and outputting the state information at the next moment;
(2) and a flight control module: the flight controller software simulation decomposes the control instruction of the control module into control signals and transmits the control signals to the unmanned aerial vehicle module for position and attitude control;
(3) a sensor module: the method comprises the steps of updating a mathematical model containing sensors such as an accelerometer and the like to update sensor data in the flight process of the unmanned aerial vehicle;
(4) control module: providing a sensor data acquisition interface and a single machine control interface of the unmanned aerial vehicle, acquiring cluster state data by a cluster formation and obstacle avoidance algorithm through the interface, obtaining a control instruction of each unmanned aerial vehicle in a cluster according to the algorithm, and realizing closed-loop control;
(5) an environment module: the weather environment control module is used for realizing weather change of the virtual environment;
(6) a rendering engine module: and rendering information such as objects, maps, illumination and the like in the virtual environment in real time.
(3) Cluster semi-physical simulation system
After the software simulation verification, a semi-physical simulation system is built by taking cluster formation and obstacle avoidance of the unmanned aerial vehicle clusters in a real scene as a target as far as possible, and the effectiveness and reliability of an algorithm are further verified as shown in fig. 9.
Except that the cluster simulation software provides virtual state data of the unmanned aerial vehicle, hardware equipment in a physical verification test is adopted in the system, and the system comprises a flight control device, an onboard computer and data transmission. The cluster control and obstacle avoidance algorithm are integrated in an onboard computer. The airborne computer performs cluster control and obstacle avoidance calculation by combining the vision sensor data acquired from the cluster simulation software, other unmanned aerial vehicle state data acquired from a data chain and self pose data, sends a control instruction to the flight control to calculate as a control signal, and transmits the control signal to the corresponding unmanned aerial vehicle in the cluster simulation software to realize closed-loop control.
Each set of hardware equipment in the semi-physical simulation system corresponds to one unmanned aerial vehicle in cluster simulation software, all software except for state calculation runs on the hardware equipment, and real data chains are adopted for communication among the unmanned aerial vehicles, so that a more effective verification algorithm is provided for physical demonstration verification compared with software simulation.
Compared with other similar methods, the technical scheme has the following characteristics: (1) the traditional obstacle avoidance scheme of the unmanned aerial vehicle realizes omnibearing obstacle perception by carrying a plurality of sensors, which can cause the problems of complex machine body, large weight and the like; (2) the project provides a control algorithm for adaptively changing the formation of the cluster according to the distribution of the obstacles in the environment, so that the flight requirement of the cluster in a complex environment is met.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The unmanned aerial vehicle cluster control and obstacle avoidance method is characterized by comprising the following steps of:
when part of unmanned aerial vehicles in the unmanned aerial vehicle cluster sense an obstacle, each unmanned aerial vehicle in the part of unmanned aerial vehicles positions the obstacle according to the self-positioning and obstacle sensing results and broadcasts the obstacle position in the unmanned aerial vehicle cluster;
the unmanned aerial vehicle cluster carries out self-adaptive change according to the position of the obstacle and autonomously adjusts the course of the unmanned aerial vehicle, so that the obstacle is always kept in the sight range of the unmanned aerial vehicle cluster; and
after the drone cluster passes the obstacle, the tail of the drone cluster remains facing the obstacle to ensure that the drone cluster is entirely out of range of the obstacle.
2. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 1, wherein the unmanned aerial vehicle cluster adaptively changing according to the obstacle position comprises an unmanned aerial vehicle cluster adaptively changing according to the obstacle position according to an aggregation criterion, a separation criterion, and a speed matching criterion, wherein,
the aggregation criterion is that each unmanned aerial vehicle in the unmanned aerial vehicle cluster obtains position information of other unmanned aerial vehicles in the neighborhood, and the expected state offset is calculated by combining the position of the unmanned aerial vehicle and a control algorithm and is fed back to the controller of the unmanned aerial vehicle, so that the current unmanned aerial vehicle is more and more close to the central position of the unmanned aerial vehicle cluster, and the aggregation state is achieved;
the separation criterion is that distance offset between the position of each unmanned aerial vehicle and other unmanned aerial vehicle positions in the neighborhood is obtained, the distance offset is fed back to a self controller, when the distance offset is smaller than the safety distance between unmanned aerial vehicles, the self controller generates repulsive force inversely proportional to the distance offset, and the unmanned aerial vehicle clusters are separated in motion due to the repulsive force;
and each unmanned aerial vehicle acquires the speed information of other unmanned aerial vehicles in the neighborhood in real time, compares the speeds of the other unmanned aerial vehicles with the current unmanned aerial vehicle to acquire a speed offset, and feeds the speed offset back to a self controller to carry out speed adjustment, so that the speed and the direction of all unmanned aerial vehicles in the unmanned aerial vehicle cluster are consistent.
3. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 1, wherein the unmanned aerial vehicle cluster autonomously adjusting the unmanned aerial vehicle's own heading according to the obstacle location comprises the unmanned aerial vehicle cluster adjusting the unmanned aerial vehicle's own heading according to the obstacle location according to a view angle distribution criterion, wherein,
the visual angle distribution criterion is that each unmanned aerial vehicle in the unmanned aerial vehicle cluster is evenly distributed by adjusting the orientation, so that the sight angles of the unmanned aerial vehicle cluster are evenly distributed by taking the unmanned aerial vehicle cluster as the center, and the expected course angles are fed back to the self controller by acquiring the positions and the orientations of other unmanned aerial vehicles in the neighborhood, so that the unmanned aerial vehicle cluster with the evenly distributed sight angles obtains a larger sight range compared with the unmanned aerial vehicle cluster with the evenly distributed sight angles.
4. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 1, further comprising performing cluster control on unmanned aerial vehicle clusters with random distribution of initial positions by using a cluster control algorithm to implement:
distances among the unmanned aerial vehicles in the unmanned aerial vehicle cluster tend to be consistent;
the unmanned aerial vehicle cluster moves to an expected direction by taking a virtual pilot as a center;
automatically avoiding an obstacle when the unmanned aerial vehicle cluster encounters the obstacle;
and (5) gathering the clusters towards the center again after passing through the range of the obstacle, and recovering formation.
5. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 4, wherein the cluster control algorithm comprises: control input u of ith unmanned aerial vehicle i And w i Wherein, based on controlInput u i Controlling the relative position and speed between the unmanned aerial vehicles in the unmanned aerial vehicle cluster based on a control input w i And controlling the line-of-sight angular distribution of the unmanned aerial vehicle in the unmanned aerial vehicle cluster.
6. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 5 wherein the unmanned aerial vehicle on-board vision sensor is fixedly attached to the machine body, the control input w of the ith unmanned aerial vehicle being represented by the following formula i ：
w i ＝k i *(2π*i/N i -γ i )
Wherein k is i Gain, gamma, of course control of unmanned aerial vehicle i i For the heading angle of the ith unmanned aerial vehicle, N i Is the neighborhood of the ith unmanned aerial vehicle
N i ＝{‖q j -q i ‖<r,j≠i,j＝1,...n}
i and j are unmanned aerial vehicle numbers, and r is unmanned aerial vehicle perception radius.
7. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 5, wherein the control input u of the ith unmanned aerial vehicle is determined from the overall potential V (q) of the unmanned aerial vehicle cluster expressed by the following formula i ：
Wherein q is a set of positions of all unmanned aerial vehicles in the unmanned aerial vehicle cluster, ψ α (z) is the current distance z to the desired distance d α Potential energy integration of (2):
φ α is the attractive force between two unmanned aerial vehicles:
wherein a, b, c, h is a curve parameter, r α As the perceived distance of the unmanned aerial vehicle, the norm zjj is not led at z=0, and the norm zjj is rewritten into the everywhere led norm zjj σ Norms II z II σ For constructing a smooth artificial potential field function, epsilon being a norm parameter.
8. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 5, wherein the control input u of the ith unmanned aerial vehicle is represented by the following formula i Control input for formation entry of ith drone
Virtual pilot feedback item control input of unmanned plane i>
Virtual agent feedback item control input to unmanned plane i>
And (2) sum:
wherein a is ij Indicating whether the unmanned aerial vehicle i and j are communicated before, 0 indicates non-communication, 1 indicates communication, n ij Directional vector q for unmanned plane i to unmanned plane j l For the position vector of the virtual pilot, p l A velocity vector, c, for the virtual pilot 1 And c 2 The position parameter and the speed parameter of the virtual pilot respectively,
for the set of virtual agents, < >>
And->
The position vector and the speed vector of the jth virtual intelligent agent and the communication condition of the jth virtual intelligent agent and the unmanned plane i are respectively n ij A direction vector pointing to the virtual agent j for the drone i.
9. The unmanned aerial vehicle cluster control and obstacle avoidance method of claim 8, wherein the virtual agent is generated in the closest vicinity of the obstacle to the unmanned aerial vehicle, a repulsive force is generated to the unmanned aerial vehicle when the virtual agent is within a safe distance of the unmanned aerial vehicle, the closer the distance is, the larger the repulsive force is, the unmanned aerial vehicle can decelerate under the influence of the repulsive force and move in a direction away from the obstacle when approaching the obstacle, wherein the automatic obstacle avoidance is realized when the unmanned aerial vehicle moves away from the virtual agent under the influence of the repulsive force until the obstacle is within the safe distance thereof.
10. Unmanned aerial vehicle crowd control and obstacle avoidance device, characterized by comprising:
the obstacle data acquisition module is used for positioning the position of an obstacle according to the self positioning and obstacle sensing results of each unmanned aerial vehicle in the part of unmanned aerial vehicles when the part of unmanned aerial vehicles in the unmanned aerial vehicle cluster senses the obstacle;
a data transmission module for broadcasting the obstacle location in the unmanned aerial vehicle cluster;
the adjusting module is used for carrying out self-adaptive change according to the position of the obstacle through the unmanned aerial vehicle cluster and automatically adjusting the self course of the unmanned aerial vehicle, so that the obstacle is always kept in the sight range of the unmanned aerial vehicle cluster; and
and the post-processing module is used for keeping the tail of the unmanned aerial vehicle cluster facing the obstacle after the unmanned aerial vehicle cluster passes through the obstacle so as to ensure that the unmanned aerial vehicle cluster is wholly separated from the obstacle range.
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| CN117850437A (en) * | 2024-03-08 | 2024-04-09 | 北京航空航天大学 | Method for controlling movement of intelligent agent cluster and related product |
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| CN117608318B (en) * | 2024-01-23 | 2024-04-09 | 北京航空航天大学 | Unmanned aerial vehicle formation obstacle avoidance control method and system based on bird-like phototaxis |
| CN117850437A (en) * | 2024-03-08 | 2024-04-09 | 北京航空航天大学 | Method for controlling movement of intelligent agent cluster and related product |
| CN117850437B (en) * | 2024-03-08 | 2024-05-14 | 北京航空航天大学 | Method for controlling movement of intelligent agent cluster and related product |
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