CN113485454A - Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field - Google Patents
Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field Download PDFInfo
- Publication number
- CN113485454A CN113485454A CN202110967307.9A CN202110967307A CN113485454A CN 113485454 A CN113485454 A CN 113485454A CN 202110967307 A CN202110967307 A CN 202110967307A CN 113485454 A CN113485454 A CN 113485454A
- Authority
- CN
- China
- Prior art keywords
- aerial vehicle
- unmanned aerial
- obstacle
- obstacle avoidance
- force
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 230000004888 barrier function Effects 0.000 claims description 21
- 230000001133 acceleration Effects 0.000 claims description 5
- 230000006872 improvement Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 238000007670 refining Methods 0.000 claims 2
- 230000008569 process Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 12
- 230000006870 function Effects 0.000 description 10
- 230000006399 behavior Effects 0.000 description 9
- 230000000694 effects Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000009471 action Effects 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an unmanned aerial vehicle obstacle avoidance maneuvering method and system for a geometrically improved artificial potential field, which comprises the following steps: when the unmanned aerial vehicle is determined to need to make an obstacle avoidance maneuver, determining an obstacle avoidance maneuver angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment, and determining an improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuver angle; determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle; and determining the obstacle avoidance control force of the unmanned aerial vehicle together according to the speed obstacle avoidance force of the unmanned aerial vehicle and the improved obstacle repulsion field repulsion force, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle. The invention effectively avoids the unnecessary obstacle avoidance maneuvering behavior of the unmanned aerial vehicle, can improve the excessive obstacle avoidance behavior, and has shorter generated obstacle avoidance path; the speed obstacle avoidance force of the unmanned aerial vehicle considers an expected critical obstacle avoidance angle determined according to an additional obstacle avoidance distance influenced by uncertain factors, and the determined speed obstacle avoidance force improves the probability of successful obstacle avoidance.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle autonomous obstacle avoidance, in particular to an unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improving an artificial potential field.
Background
The unmanned aerial vehicle has the characteristics of portability, flexibility, strong maneuverability, good concealment and the like, and is widely applied to the civil and military fields. With the continuous development of unmanned aerial vehicles, the unmanned aerial vehicle task environment is more and more complex and the task environment is complex and changeable, so that the flight safety problem of the unmanned aerial vehicle in the task execution process cannot be completely guaranteed by a preset global air route, and therefore the autonomous obstacle avoidance technology of the unmanned aerial vehicle is also a key component of an unmanned aerial vehicle system when encountering sudden obstacles; the unmanned aerial vehicle is an important premise for realizing the autonomous flight of the unmanned aerial vehicle; the unmanned aerial vehicle is an important basis for ensuring that the unmanned aerial vehicle smoothly completes tasks and accurately hits enemy targets; the unmanned aerial vehicle is a powerful guarantee for realizing automatic control of the unmanned aerial vehicle. The whole level of the current mission planning can be improved by developing the research of the unmanned aerial vehicle obstacle avoidance maneuver strategy, and the method has important practical significance for further research of the mission planning.
The existing unmanned aerial vehicle obstacle avoidance methods are mainly divided into two categories, namely (1) an obstacle avoidance method based on route planning; the method is mainly characterized in that the obstacle avoidance problem is converted into a route planning problem, such as a genetic algorithm, an artificial potential field method and an A-star algorithm. (2) An obstacle avoidance method based on a geometric relationship; the main idea of the method is to calculate the evasive route according to key information such as relative distance, speed, acceleration and angle between the unmanned aerial vehicle and the obstacle. For example, an ellipsoid can be adopted to model the obstacle, and the obstacle avoidance waypoint is calculated according to an ellipsoid equation and the unmanned aerial vehicle velocity vector. The characteristic points of the obstacles can be acquired by detecting the position relation between the unmanned aerial vehicle and the obstacles in real time, a safe flight boundary is obtained, and the unmanned aerial vehicle flies along the boundary to avoid the obstacles.
However, the existing obstacle avoidance method based on the geometric relationship is difficult to avoid unnecessary obstacle avoidance maneuvers, so that excessive obstacle avoidance behaviors are caused.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field, aiming at the technical problem that the existing obstacle avoidance maneuvering method of the geometrical relationship is difficult to avoid unnecessary obstacle avoidance maneuvering behaviors, so that excessive obstacle avoidance behaviors are caused.
In order to achieve the technical purpose, the invention provides an unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field, which comprises the following steps: when the unmanned aerial vehicle is determined to need to make an obstacle avoidance maneuver, determining an obstacle avoidance maneuver angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment, and determining an improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuver angle;
determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle; and determining obstacle avoidance control force of the unmanned aerial vehicle together according to the speed obstacle avoidance force of the unmanned aerial vehicle and the improved obstacle repulsion field repulsion force, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle.
Further, the method for determining that the unmanned aerial vehicle needs to make an obstacle avoidance maneuver comprises the following steps: after the sudden obstacle is detected, determining obstacle avoidance maneuver judgment conditions according to the relative geometric relationship between the current state of the unmanned aerial vehicle and the obstacle, and determining whether the unmanned aerial vehicle makes obstacle avoidance maneuvers according to the obstacle avoidance maneuver judgment conditions; the obstacle avoidance maneuver judgment condition is expressed as follows:
|η＜ζ,
Dio≤Davoid
if the two formulas are met simultaneously, determining that the unmanned aerial vehicle makes obstacle avoidance maneuver; otherwise, the unmanned aerial vehicle does not make obstacle avoidance maneuver;
wherein eta is the relative angle between the flight speed direction of the unmanned aerial vehicle and the obstacle, zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle, DioIs the relative distance of the unmanned aerial vehicle and the obstacle, DavoidAnd an obstacle avoidance maneuvering obstacle avoidance distance is needed for the unmanned aerial vehicle.
Still further, the critical impingement angle ζ of the drone for the obstacle is expressed as:
where ζ is the critical impingement angle of the drone on the obstacle, riRadius of unmanned plane, roRadius of the obstacle, DioIs made withoutThe relative distance between the human and the obstacle.
Further, the speed obstacle avoidance force of the unmanned aerial vehicle is expressed as follows:
wherein, FvAvoiding the barrier force for the speed of the unmanned aerial vehicle; k is a radical ofvThe velocity obstacle avoidance coefficient; v is the speed of the unmanned aerial vehicle at the current moment; v. ofζExpecting an obstacle avoidance flight speed for the unmanned aerial vehicle based on the critical collision angle at the current moment, wherein eta is a relative angle between the flight speed direction of the unmanned aerial vehicle and an obstacle, zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle, and rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The extent of influence of the repulsive potential field formed by the obstacle O.
Further, a critical obstacle avoidance angle determined according to an additional obstacle avoidance distance affected by uncertain factors is considered when determining the speed obstacle avoidance force of the unmanned aerial vehicle, and the speed obstacle avoidance force of the unmanned aerial vehicle is represented as follows:
Still further, the desired critical obstacle avoidance angle is expressed as:
wherein ζexpAt a desired critical angle of avoidance, riRadius of unmanned plane, roRadius of the obstacle, DioThe distance between the unmanned aerial vehicle and the obstacle is the relative distance between the unmanned aerial vehicle and the obstacle, and delta is an additional obstacle avoidance distance considering the influence of uncertain factors.
Further, the improved obstacle repulsive force field is expressed as:
wherein FdThe repulsion force generated by the obstacle O to the unmanned aerial vehicle is the repulsion force of the improved obstacle repulsion field; rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The influence range of the repulsive potential field formed by the obstacle O; zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle O at the current moment; eta is the relative angle between the flight direction of the unmanned aerial vehicle and the obstacle at the current moment; c is an intermediate parameter, and C is more than 0 to determine the change speed of the repulsive force;
the unit vector pointing to drone i for obstacle O.
Further, the unmanned aerial vehicle obstacle avoidance control force is the sum of the unmanned aerial vehicle speed obstacle avoidance force and the improved obstacle repulsive force field repulsive force.
Further, after the unmanned aerial vehicle removes the collision risk, the state consistent force of the virtual sub-machine is used for replacing the gravity of a target point, the state regression control force of the unmanned aerial vehicle is adjusted in real time through model prediction, and the state regression control force of the unmanned aerial vehicle after the unmanned aerial vehicle breaks away from the collision risk is expressed as:
wherein: fattReturning the control force to the state of the unmanned aerial vehicle;
the acceleration of the virtual sub machine is obtained; x, XfRespectively is the position vector of the unmanned aerial vehicle and the virtual sub-machine thereof; v, vfThe flight speeds of the unmanned aerial vehicle and the virtual sub-machine are respectively; alpha and beta are control force parameters alpha > 0, beta > 0, v0Is the speed of the drone at the previous moment,
the expected obstacle avoidance flight speed is the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle is just facing the center of the obstacle or deviates to the right side at the current moment;
the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle flies towards the left side of the obstacle at the current moment; thetapoThe relative azimuth angle of the unmanned aerial vehicle and the obstacle at the current moment; zetaexpAn expected critical obstacle avoidance angle of the unmanned aerial vehicle relative to the obstacle at the current moment; the relative angle between the speed and the obstacle is
Delta t is the time difference from the moment t to the moment t +1, and rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The extent of influence of the repulsive potential field formed by the obstacle O.
When the unmanned aerial vehicle has collision danger, due to the existence of obstacle avoidance force, the unmanned aerial vehicle avoids obstacles under the combined action of the obstacle avoidance force and the state regression force of the virtual sub-machine, after the collision danger is relieved, the obstacle avoidance force disappears, the unmanned aerial vehicle returns to an expected state under the control of the state regression force, the state regression control force is adjusted through model prediction control, the state regression control force of the virtual sub-machine is avoided, the unmanned aerial vehicle is prevented from being trapped in the collision danger again, and therefore the shaking behavior of obstacle avoidance, regression, obstacle re-avoidance and re-regression of the unmanned aerial vehicle is caused.
The invention provides an unmanned aerial vehicle obstacle avoidance maneuvering system of a geometric improvement artificial potential field, which comprises an obstacle repulsion field determining module, a speed obstacle avoidance force determining module and an obstacle avoidance control module;
the obstacle repulsion field determining module is used for determining an obstacle avoidance maneuvering angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment when the unmanned aerial vehicle needs to make the obstacle avoidance maneuvering, and determining the improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuvering angle;
the speed obstacle avoidance force determining module is used for determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle;
and the obstacle avoidance control module is used for determining obstacle avoidance control force of the unmanned aerial vehicle together according to the improved obstacle repulsive force field repulsive force determined by the obstacle repulsive force field determination module and the speed obstacle avoidance force of the unmanned aerial vehicle determined by the speed obstacle avoidance force determination module, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle.
The invention has the following beneficial technical effects: the obstacle avoidance method provided by the invention can effectively avoid unnecessary obstacle avoidance maneuvering behaviors of the unmanned aerial vehicle, can improve excessive obstacle avoidance behaviors, and has a short generated obstacle avoidance path; according to the method, the influence of uncertain factors such as communication time delay and interference in the obstacle avoidance process is considered, the obstacle avoidance process is close to the surface of the obstacle, and the obstacle avoidance failure is possibly caused, so that the critical obstacle avoidance angle determined according to the additional obstacle avoidance distance influenced by the uncertain factors is considered in the speed obstacle avoidance force of the unmanned aerial vehicle, and the probability of successful obstacle avoidance is improved through the determined speed obstacle avoidance force.
The unmanned aerial vehicle flies towards the center point of the obstacle more, the collision risk is larger, the required obstacle avoidance maneuver is larger, the unmanned aerial vehicle flies away from the center point of the obstacle more, the collision risk is smaller, the required obstacle avoidance maneuver is smaller, and when the obstacle avoidance maneuver is smaller and is very close to the obstacle, according to the traditional artificial potential field, the larger obstacle avoidance control amount is inevitably generated, unnecessary large maneuvers are generated, and the system stability and the quality of the obstacle avoidance airway are further influenced. Therefore, the repulsion field is improved by utilizing the angle relation, the repulsion of the obstacle to the unmanned aerial vehicle is adjusted in real time, and the unmanned aerial vehicle is prevented from making unnecessary obstacle avoidance behaviors while the obstacle is effectively avoided.
The method takes the expected state of the unmanned aerial vehicle as a target, not only can solve the problem of local optimization, but also can enable the unmanned aerial vehicle to quickly return to the expected state, and has little influence on subsequent tasks of the unmanned aerial vehicle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts;
fig. 1 is a schematic flow chart of an unmanned aerial vehicle obstacle avoidance maneuver method for a geometrically improved artificial potential field according to an embodiment of the present invention;
fig. 2 is a schematic diagram of decision of obstacle avoidance maneuver of the unmanned aerial vehicle according to the embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating model prediction solution according to an embodiment of the present invention;
fig. 4 shows the obstacle avoidance effect of the conventional artificial potential field method, in which 4(a) sets an excessively close effect for a target point, and 4(b) sets a far effect for the target;
fig. 5 shows an obstacle avoidance maneuver method for an unmanned aerial vehicle with a geometrically improved artificial potential field according to an embodiment of the present invention, where 5(a) is an obstacle avoidance result in a conventional situation, 5(6) is an obstacle avoidance result in a collinear situation, 5(c) is an obstacle avoidance result in a collision-free risk situation, and 5(d) is a whole-process flight altitude of the unmanned aerial vehicle.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
In the description of the present patent application, it is noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
Example 1: as shown in fig. 1, an unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field includes: when the unmanned aerial vehicle is determined to need to make an obstacle avoidance maneuver, determining an obstacle avoidance maneuver angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment, and determining an improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuver angle;
determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle; and determining obstacle avoidance control force of the unmanned aerial vehicle together according to the speed obstacle avoidance force of the unmanned aerial vehicle and the improved obstacle repulsion field repulsion force, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle.
In this embodiment, optionally, the method for determining that the unmanned aerial vehicle needs to make an obstacle avoidance maneuver includes:
and step S1, acquiring the position of the sudden obstacle, determining a newly-increased sudden threat area in the flight environment, and further determining whether the unmanned aerial vehicle needs to enter an obstacle avoidance mode to execute obstacle avoidance maneuvers.
The specific implementation process is as follows:
utilize unmanned aerial vehicle to carry the sensor, constantly perceive the environment around at unmanned aerial vehicle flight in-process, detect at a certain moment that there is unknown proruption barrier in advance in flight the place ahead, as shown in figure 2, unmanned aerial vehicle's airspeed is viAnd the relative distance between the unmanned plane and the barrier is DioAnd the relative angle between the flight direction of the unmanned aerial vehicle and the obstacle is eta.
Setting the safe distance of the unmanned aerial vehicle to D according to the performance limit of the unmanned aerial vehicleavoidThen the threat zone of the outburst obstacle can be represented by the dotted circle in fig. 2 when the distance between the unmanned aerial vehicle and the obstacle is greater than DavoidThe obstacle avoidance method comprises the steps that time and distance of the unmanned aerial vehicle are enough to avoid the obstacle, so that the obstacle is considered to have no threat to the unmanned aerial vehicle temporarily, and the obstacle avoidance is not carried out; when the distance between the unmanned aerial vehicle and the obstacle is smaller than DavoidAnd if the unmanned aerial vehicle has collision risks, obstacle avoidance maneuver should be immediately adopted, otherwise, the unmanned aerial vehicle may collide with the obstacle due to maneuver performance limitation.
At Dio＜DavoidAnd under the condition, further judging whether the unmanned aerial vehicle has collision risk by using a collision cone detection method. To view the drone as a particle, the radius of the drone is expanded to the obstacle, so the drone can be viewed as a particle, as shown in fig. 2, where the radius of the obstacle is ri+ro(riRadius of unmanned plane, roThe radius of the obstacle itself), consider the critical condition, when unmanned aerial vehicle just and the obstacle between not bumping, the shortest distance with the obstacle barycenter on the unmanned aerial vehicle flight course is exactly equal to the radius of the obstacle, and the relative angle of unmanned aerial vehicle speed direction and obstacle at this moment is the critical angle of impact of unmanned aerial vehicle to this obstacle promptly.
Therefore, the critical impingement angle can be obtained by the following equation.
In the formula, ζ is the critical collision angle of the unmanned aerial vehicle to the obstacle, riRadius of unmanned plane, roRadius of the obstacle, DioIs the relative distance between the drone and the obstacle.
When the absolute value of the relative angle between the speed direction of the unmanned aerial vehicle and the obstacle is smaller than the critical collision angle, the unmanned aerial vehicle is represented to have collision risk, and otherwise, the collision risk does not exist.
The decision condition for the drone to execute the obstacle avoidance maneuver can be represented by the following equation:
|η|＜ζ,Dio≤Davoid (2)
in the formula, η is the relative angle between the flight speed direction of the unmanned aerial vehicle and the obstacle, ζ is the critical collision angle of the unmanned aerial vehicle to the obstacle, and DioIs the relative distance of the unmanned aerial vehicle and the obstacle, DavoidAnd an obstacle avoidance maneuvering obstacle avoidance distance is needed for the unmanned aerial vehicle. If the formula is established, the unmanned aerial vehicle executes obstacle avoidance maneuver; if not, the unmanned aerial vehicle does not execute obstacle avoidance maneuver.
When | η | < ζ, Dio≤DavoidWhen the unmanned aerial vehicle is in a right position, determining that the unmanned aerial vehicle needs to make obstacle avoidance maneuvers, and calculating obstacle avoidance control force of the unmanned aerial vehicle; otherwise, the unmanned aerial vehicle does not need to make obstacle avoidance maneuver, and the obstacle avoidance force is 0.
The method for calculating the obstacle avoidance control force of the unmanned aerial vehicle specifically comprises the following steps:
step S21, an unmanned aerial vehicle obstacle avoidance control force caused by the obstacle repulsive potential field is calculated.
Assuming that the detected sudden obstacles are all spherical, the external ball is also used for representing irregular obstacles. The conventional repulsive force potential field function is as follows:
in the formula of Uo(p) a repulsive field of p points; c > 0 is a repulsive force field coefficient; rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The extent of influence of the repulsive potential field formed by the obstacle O. The repulsion force of the obstacle on the unmanned aerial vehicle at the p point is a negative gradient of the repulsion field, and can be expressed by formula (4):
in the formula (I), the compound is shown in the specification,
a unit vector pointing to unmanned aerial vehicle i for obstacle O; fd(p) the unmanned aerial vehicle at point p is subjected to the repulsive force of the obstacle; c > 0 is a repulsive force field coefficient; rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The extent of influence of the repulsive potential field formed by the obstacle O.
Analysis can obtain that the relative angle between the flight direction of the unmanned aerial vehicle and the obstacle also plays a crucial role in obstacle avoidance of the unmanned aerial vehicle. Obviously, the unmanned aerial vehicle flies more towards the central point of barrier, and its collision risk is bigger, and the obstacle avoidance maneuver that needs is also bigger, and the central point that deviates from the barrier flies more, and its collision risk is smaller, and the obstacle avoidance maneuver that needs is also smaller, and when needing to avoid the barrier maneuver less but very close to the barrier, according to traditional artifical potential field, must produce great obstacle avoidance control volume, will produce unnecessary big maneuver, and then influence system stability and the quality of avoiding the obstacle course.
In a specific embodiment, optionally, the repulsive force field is improved by utilizing an angle relationship, the repulsive force of the obstacle to the unmanned aerial vehicle is adjusted in real time, and the unmanned aerial vehicle is prevented from making unnecessary obstacle avoidance behaviors while the obstacle is effectively avoided. The calculation formula of the repulsion force of the improved barrier repulsion field is as follows:
in the formula, FdRepulsion force generated by the obstacle O to the unmanned aerial vehicle; rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The influence range of the repulsive potential field formed by the obstacle O; zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle O at the current moment; eta is the relative angle between the flight direction of the unmanned aerial vehicle and the obstacle at the current moment; c > 0 determines the change speed of the repulsive force;
the unit vector pointing to drone i for obstacle O.
And step S22, calculating obstacle avoidance control force of the unmanned aerial vehicle determined by the expected obstacle avoidance speed, namely the speed obstacle avoidance force of the unmanned aerial vehicle.
Known by collision awl theory, when unmanned aerial vehicle's flying speed direction just in time was located critical collision angle direction (like pA in fig. 2, pB direction), unmanned aerial vehicle just can avoid the barrier, so, when unmanned aerial vehicle had the collision risk, if can make unmanned aerial vehicle's flying speed direction tend to critical collision angle direction and keep unanimous, just can break away from the collision danger, and the distance that breaks away from the collision risk also will be the shortest.
Through the above analysis, according to the speed coincidence rule, the speed-based obstacle avoidance control force can be expressed by the following formula:
in the formula, FvAvoiding the barrier force for the speed of the unmanned aerial vehicle; k is a radical ofvThe speed obstacle avoidance coefficient is more than 0; v is the speed of the unmanned aerial vehicle at the current moment; v. ofζAnd setting the right direction as positive and the left direction as negative for the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle at the current moment. When the unmanned aerial vehicle flies to the left side of the obstacle (namely eta is less than 0), the direction of the critical collision angle on the left side is taken, when the unmanned aerial vehicle flies to the right side of the obstacle (namely eta is more than 0), the direction of the critical collision angle on the right side is taken, and when the unmanned aerial vehicle flies to the obstacle (namely eta is 0), the direction of the critical collision angle on the right side is taken.
In other embodiments, considering that the obstacle avoidance process may be influenced by uncertain factors such as communication delay and interference, the obstacle avoidance process may be caused by the obstacle avoidance close to the surface of the obstacle, and therefore the obstacle avoidance failure may be caused, an additional obstacle avoidance distance δ is added, and thus the critical obstacle avoidance angle becomes:
in the formula, ζexpA desired critical obstacle avoidance angle; r isi、roRadii of unmanned aerial vehicle i and obstacle O, respectively; delta is an additional obstacle avoidance distance considering the influence of uncertain factors such as communication time delay, disturbance and the like; dioFor the unmanned aerial vehicle at the current moment andrelative distance of the obstacle.
From this, combine unmanned aerial vehicle and the relative deviation direction of flying of obstacle, unmanned aerial vehicle's speed obstacle avoidance force can further be expressed as:
in the formula, kvThe speed obstacle avoidance coefficient is more than 0; v is the speed of the unmanned aerial vehicle at the current moment;
the expected obstacle avoidance flight speed is the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle is just facing the center of the obstacle or deviates to the right side at the current moment;
the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle flies towards the left side of the obstacle at the current moment; thetapoThe relative azimuth angle of the unmanned aerial vehicle and the obstacle at the current moment; zetaexpAnd obtaining the expected critical obstacle avoidance angle of the unmanned aerial vehicle relative to the obstacle at the current moment. .
The obstacle avoidance control force of the unmanned aerial vehicle consists of a distance repulsion item based on an angle improvement artificial potential field and an expected obstacle avoidance speed consistency item based on an obstacle avoidance cone, and is represented as follows: frep＝Fd+Fv
And constructing a speed consistent field and an improved obstacle repulsion field by using the obstacle avoidance critical flight speed to guide the unmanned aerial vehicle to avoid the obstacle together. Known by collision awl theory, when unmanned aerial vehicle's flight speed direction just in time was located critical collision angle direction, unmanned aerial vehicle just can avoid the barrier, so, when unmanned aerial vehicle had the collision risk, if the flight speed direction that enables unmanned aerial vehicle tended to critical collision angle direction and kept unanimous, just can break away from the collision danger, and the distance that breaks away from the collision risk also will be the shortest.
If the speed obstacle avoidance force of the unmanned aerial vehicle is shown in the formula (8), the obstacle avoidance control force of the unmanned aerial vehicle is expressed as:
example 2: the present embodiment further includes, on the basis of the above embodiment, step S3: and a virtual sub-machine is arranged to replace an obstacle avoidance target point, so that the unmanned aerial vehicle is attracted to return to an expected state in real time. The virtual sub-machine is used for representing the real-time expected position of the unmanned aerial vehicle under the condition of no emergent threat, the obstacle avoidance control force disappears after the unmanned aerial vehicle relieves the collision danger, and the virtual sub-machine is used for attracting the unmanned aerial vehicle in real time so that the unmanned aerial vehicle can return to the original expected position as soon as possible. The virtual sub-machine is created to attract the unmanned aerial vehicle to return to the original route, the target point is not artificially added, the original expected position of the unmanned aerial vehicle is used as the real-time target point, the unmanned aerial vehicle can return to the original state more quickly, and the subsequent established task is less influenced.
In order to keep the unmanned aerial vehicle consistent with the virtual sub-machine as soon as possible after the unmanned aerial vehicle is separated from the collision risk, the regression control force of the design state is as follows:
in the formula, FattReturning the control force to the state of the unmanned aerial vehicle;
the acceleration of the virtual sub machine is obtained; x, XfRespectively is the position vector of the unmanned aerial vehicle and the virtual sub-machine thereof; v, vfThe flight speeds of the unmanned aerial vehicle and the virtual sub-machine are respectively; alpha > 0, beta > 0 are control force parameters.
Furthermore, model prediction control is added in the unmanned aerial vehicle state regression process to adjust the state regression control force, so that the unmanned aerial vehicle state regression momentum is changed, and severe jitter of a flight path in the unmanned aerial vehicle state regression process is avoided. Assuming that the unmanned aerial vehicle has removed the collision risk at the time t, predicting that the unmanned aerial vehicle is influenced by the consistent force of the expected state within the time t-t +1 to return to the original track (as shown in fig. 3), wherein the acceleration of the unmanned aerial vehicle is as follows:
assuming that the unmanned aerial vehicle does uniform linear motion within one step length, the expected speed of the unmanned aerial vehicle within the time period from t to t +1 is as follows:
equation (12) is a predictive model, i.e., a model of the motion of the drone, the relative angle between the velocity and the obstacle being
the critical expected obstacle avoidance angle of the unmanned aerial vehicle relative to the obstacle at the moment t is
The design optimization function is:
wherein, K is more than 1, Relu function is a nonlinear activation unit f (x) max (0, x), etat～t+1The relative angle between the speed of the unmanned aerial vehicle and the obstacle in the time period of t-t +1 is also an optimized object.
Solving this optimization function, as shown in FIG. 3, when
Time, show that the unmanned aerial vehicle will face collision danger again after t moment, it is obvious eta this momentt～t+1The closer to each other
The smaller the value of the optimization function is,
the time is optimal; when in use
In the meantime, the unmanned aerial vehicle cannot face collision danger again, and eta is obtained at the momentt～t+1The closer to each other
The smaller the value of the optimization function is,
the optimum is reached.
According to the optimized etat～t+1The expected flying speed of the unmanned aerial vehicle within the time of t-t +1 can be obtained, and the expected state regression force is solved by the speed.
Finally, the model prediction refined trajectory regression force can be represented by equation (14):
wherein v is0The speed of the unmanned aerial vehicle at the previous moment; x, XfRespectively is the position vector of the unmanned aerial vehicle and the virtual sub-machine thereof; v, vfThe flight speeds of the unmanned aerial vehicle and the virtual sub-machine are respectively; alpha is more than 0, beta is more than 0 is a control force parameter;
the expected obstacle avoidance flight speed is the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle is just facing the center of the obstacle or deviates to the right side at the current moment;
the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle flies towards the left side of the obstacle at the current moment; thetapoThe relative azimuth angle of the unmanned aerial vehicle and the obstacle at the current moment; zetaexpAnd obtaining the expected critical obstacle avoidance angle of the unmanned aerial vehicle relative to the obstacle at the current moment.
The unmanned aerial vehicle carries out the maneuver of avoiding the sudden obstacle under the combined action of the obstacle avoiding force and the state regression force, and the maneuver strategy of the whole obstacle avoiding process can be represented by the resultant force.
F＝Fatt+Frep (15)
In the formula, F is the total control force applied to the unmanned aerial vehicle; fattThe state regression force of the virtual sub machine to the unmanned aerial vehicle is obtained; frepThe total obstacle avoiding force received by the unmanned aerial vehicle.
And step S4, the unmanned aerial vehicle performs obstacle avoidance maneuvering behavior under the action of the resultant force of the obstacle avoidance force and the state regression force.
The specific implementation mode is as follows:
the total control force that unmanned aerial vehicle received does:
therefore, the obstacle avoidance maneuvering control input quantity of the unmanned aerial vehicle is as follows:
in the formula, VdThe input quantity is flexibly controlled for avoiding obstacles; v0The velocity vector of the unmanned aerial vehicle at the current moment; f is the total control force borne by the unmanned aerial vehicle; m is the mass of the unmanned aerial vehicle; and delta t is the maneuvering interval time of the unmanned aerial vehicle.
The bottom layer controller of the unmanned aerial vehicle receives VdAnd then, controlling the unmanned aerial vehicle to follow the input control instruction so as to complete the whole obstacle avoidance process.
After the collision danger is relieved, the gravity of a traditional target point is replaced according to the state of the virtual sub machine, the magnitude of the unmanned aerial vehicle state regression control force is adjusted in real time through model prediction, and the unmanned aerial vehicle is guided to return to the expected state. The invention can also ensure that the unmanned aerial vehicle can quickly return to the expected state, and has little influence on the subsequent tasks of the unmanned aerial vehicle.
With four rotors six degree of freedom models as embodiment unmanned aerial vehicle model, unmanned aerial vehicle is flying on predetermineeing the air route, and flying speed v is 1m/s, pitchesThe elevation angle theta is 0 degree, and the yaw angle psi is 90 degrees; radius of obstacle ro10m, radius r of the dronei0.25 m; the obstacle avoidance area is set as DavoidThe added additional obstacle avoidance distance δ is 1m in consideration of the influence of interference, communication delay and the like at 10 m. Setting a distance repulsion force variation coefficient c in an obstacle avoidance item to be 0.01 and setting a speed consistency coefficient kv8. The state regression coefficient of consistency α is set to 3 and β is set to 6.
Fig. 4 is an obstacle avoidance effect diagram of the unmanned aerial vehicle obtained by using the conventional artificial potential field method in the simulation environment, and it can be clearly seen that the conventional method has the two problems:
1) the action of setting the target point has great influence on the obstacle avoidance effect, if the target point is too close, the problem that the target cannot be reached is caused, and if the target point is too far, the obstacle avoidance time of the unmanned aerial vehicle is too long, the obstacle avoidance path is wasted, and the execution of the subsequent established task is influenced;
2) as long as the distance with the barrier is nearer, accomplish collision danger and relieve at unmanned aerial vehicle, or be in under the less condition of collision danger, the obstacle is dodged control strategy and still is kept away from the barrier with great controlled quantity messenger unmanned aerial vehicle, has caused the excessive obstacle of keeping away of unmanned aerial vehicle, has increased extra obstacle-keeping away route.
Fig. 5 is an obstacle avoidance effect diagram of the unmanned aerial vehicle obtained in the same simulation environment according to the embodiment of the present invention, and as can be seen from fig. 5(a), when the unmanned aerial vehicle is outside the dashed circle (obstacle avoidance safe distance), even if there is a collision risk, the unmanned aerial vehicle does not take any obstacle avoidance maneuver, and continues to fly according to the preset global route; when the unmanned aerial vehicle reaches the obstacle avoidance distance, collision risks still exist, the unmanned aerial vehicle performs obstacle avoidance maneuvers, the unmanned aerial vehicle is driven by the improved artificial potential field to carry out collision danger release, meanwhile, due to the fact that the virtual sub-machine returns to attract and model predictive control in real time, the unmanned aerial vehicle flies close to the surface of the obstacle after danger release, the shaking phenomenon is eliminated, the obstacle avoidance airway is smooth, the original airway is returned to the first time, the original expected state is recovered, and the influence on subsequent established tasks is small.
Fig. 5(b) shows a case where the flight direction of the drone is directed toward the center of the obstacle. At this moment, if keep away the barrier according to the drive of traditional potential field repulsion, then because the collineation problem, unmanned aerial vehicle must be absorbed in constantly reciprocal flight, nevertheless among this paper maneuver strategy, keeping away under the unanimous item effect of barrier speed, even under the collineation condition, unmanned aerial vehicle also can be faster better completion keep away the barrier.
Fig. 5(c) shows that although there is a sudden obstacle in front of the preset global route of the drone, the preset route does not intersect with the obstacle. As can be seen from fig. 5(c), although there is a sudden obstacle in the obstacle avoidance range of the unmanned aerial vehicle, the unmanned aerial vehicle does not take any obstacle avoidance maneuver under the obstacle avoidance maneuver in the present document, and thus the obstacle avoidance maneuver designed herein can also effectively avoid unnecessary maneuvers, thereby indirectly improving flight stability and task success rate.
Fig. 5(d) shows the flying height of the drone under the obstacle avoidance maneuver proposed herein, and it is obvious that the flying height of the drone remains constant throughout the entire process, which also meets the requirement that most flying tasks require the drone to maintain a certain height.
Therefore, under the action of the obstacle avoidance maneuvering mode, the generated obstacle avoidance navigation path is short, the obstacle avoidance time is short, and the influence on the preset later-period task is small.
Correspondingly to the above embodiment, the invention also provides an unmanned aerial vehicle obstacle avoidance maneuvering system of the geometric improvement artificial potential field, which comprises; an obstacle repulsive force field determining module, a speed obstacle avoidance force determining module and an obstacle avoidance control module,
the obstacle repulsion field determining module is used for determining an obstacle avoidance maneuvering angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment when the unmanned aerial vehicle needs to make the obstacle avoidance maneuvering, and determining the improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuvering angle;
the speed obstacle avoidance force determining module is used for determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle;
and the obstacle avoidance control module is used for determining obstacle avoidance control force of the unmanned aerial vehicle together according to the improved obstacle repulsive force field repulsive force determined by the obstacle repulsive force field determination module and the speed obstacle avoidance force of the unmanned aerial vehicle determined by the speed obstacle avoidance force determination module, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle.
And further, the unmanned aerial vehicle state regression control system further comprises a state regression control force module, wherein the state regression control force module is used for determining the state regression force of the unmanned aerial vehicle through model prediction according to the state of the virtual sub machine after the collision threat is relieved, and the state regression process of the unmanned aerial vehicle is completed based on the state regression force of the unmanned aerial vehicle.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field is characterized by comprising the following steps:
when the unmanned aerial vehicle is determined to need to make an obstacle avoidance maneuver, determining an obstacle avoidance maneuver angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment, and determining an improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuver angle;
determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle; and determining obstacle avoidance control force of the unmanned aerial vehicle together according to the speed obstacle avoidance force of the unmanned aerial vehicle and the improved obstacle repulsion field repulsion force, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle.
2. The unmanned aerial vehicle obstacle avoidance maneuver method for geometrically improving the artificial potential field according to claim 1, wherein the method for determining that the unmanned aerial vehicle needs to make an obstacle avoidance maneuver comprises: after the sudden obstacle is detected, determining obstacle avoidance maneuver judgment conditions according to the relative geometric relationship between the current state of the unmanned aerial vehicle and the obstacle, and determining whether the unmanned aerial vehicle makes obstacle avoidance maneuvers according to the obstacle avoidance maneuver judgment conditions; the obstacle avoidance maneuver judgment condition is expressed as follows:
|η|＜ζ,
Dio≤Davoid
if the two formulas are met simultaneously, determining that the unmanned aerial vehicle makes obstacle avoidance maneuver; otherwise, the unmanned aerial vehicle does not make obstacle avoidance maneuver;
wherein eta is the relative angle between the flight speed direction of the unmanned aerial vehicle and the obstacle, zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle, DioIs the relative distance of the unmanned aerial vehicle and the obstacle, DavoidAnd an obstacle avoidance maneuvering obstacle avoidance distance is needed for the unmanned aerial vehicle.
3. An unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically refining an artificial potential field according to claim 2, characterized in that the critical collision angle ζ of the unmanned aerial vehicle for the obstacle is expressed as:
where ζ is the critical impingement angle of the drone on the obstacle, riRadius of unmanned plane, roRadius of the obstacle, DioIs the relative distance between the drone and the obstacle.
4. An unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field according to claim 1, characterized in that the speed obstacle avoidance force of the unmanned aerial vehicle is expressed as follows:
wherein, FvAvoiding the barrier force for the speed of the unmanned aerial vehicle; k is a radical ofvThe velocity obstacle avoidance coefficient; v is the speed of the unmanned aerial vehicle at the current moment; v. ofζExpecting an obstacle avoidance flight speed for the unmanned aerial vehicle based on the critical collision angle at the current moment, wherein eta is a relative angle between the flight speed direction of the unmanned aerial vehicle and an obstacle, zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle, and rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The extent of influence of the repulsive potential field formed by the obstacle O.
5. The unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field according to claim 1, characterized in that an expected critical obstacle avoidance angle determined according to an additional obstacle avoidance distance affected by an uncertainty factor is considered when determining the speed obstacle avoidance force of the unmanned aerial vehicle, which is expressed as follows:
wherein, FvAvoiding the barrier force for the speed of the unmanned aerial vehicle; k is a radical ofvThe velocity obstacle avoidance coefficient; v is the speed of the unmanned aerial vehicle at the current moment;
the expected obstacle avoidance flight speed is the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle is just facing the center of the obstacle or deviates to the right side at the current moment;
the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle flies towards the left side of the obstacle at the current moment; thetapoThe relative azimuth angle of the unmanned aerial vehicle and the obstacle at the current moment; eta is a relative angle between the flight speed direction of the unmanned aerial vehicle and the obstacle, zeta is a critical collision angle of the unmanned aerial vehicle to the obstacle, and rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0Zeta range of repulsive potential field formed by the obstacle OexpAt the desired critical obstacle avoidance angle.
6. An unmanned aerial vehicle obstacle avoidance maneuver method of geometrically refining artificial potential fields as defined in claim 5, characterized in that said desired critical obstacle avoidance angle is expressed as:
wherein ζexpAt a desired critical angle of avoidance, riRadius of unmanned plane, roRadius of the obstacle, DioThe distance between the unmanned aerial vehicle and the obstacle is the relative distance between the unmanned aerial vehicle and the obstacle, and delta is an additional obstacle avoidance distance considering the influence of uncertain factors.
7. An unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field according to claim 1, characterized in that the improved obstacle repulsive force field repulsive force is expressed as:
wherein FdImproved barrier repulsion field repulsion; rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The influence range of the repulsive potential field formed by the obstacle O; zeta is the critical collision angle of the unmanned aerial vehicle to the obstacle O at the current moment; eta is the relative angle between the flight direction of the unmanned aerial vehicle and the obstacle at the current moment; c is an intermediate parameter, and C is more than 0 to determine the change speed of the repulsive force;
the unit vector pointing to drone i for obstacle O.
8. The unmanned aerial vehicle obstacle avoidance maneuvering method for geometrically improving an artificial potential field according to claim 1, characterized in that the unmanned aerial vehicle obstacle avoidance control force is a sum of a speed obstacle avoidance force of the unmanned aerial vehicle and the improved obstacle repulsive force field repulsive force.
9. The unmanned aerial vehicle obstacle avoidance maneuvering method of the geometrically improved artificial potential field according to claim 1, characterized in that after the unmanned aerial vehicle relieves the collision risk, the state concordance force of the virtual sub-machine is used to replace the attraction force of the target point, the state regression control force of the unmanned aerial vehicle is adjusted in real time through model prediction, the state regression control force of the unmanned aerial vehicle is completed based on the state regression control force of the unmanned aerial vehicle, and the state regression control force is expressed as:
wherein: fattReturning the control force to the state of the unmanned aerial vehicle;
the acceleration of the virtual sub machine is obtained; x, XfRespectively is the position vector of the unmanned aerial vehicle and the virtual sub-machine thereof; v, vfThe flight speeds of the unmanned aerial vehicle and the virtual sub-machine are respectively; alpha and beta are control force parameters alpha > 0, beta > 0, v0Is the speed of the drone at the previous moment,
the expected obstacle avoidance flight speed is the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle is just facing the center of the obstacle or deviates to the right side at the current moment;
the expected obstacle avoidance flight speed under the condition that the unmanned aerial vehicle flies towards the left side of the obstacle at the current moment; thetapoThe relative azimuth angle of the unmanned aerial vehicle and the obstacle at the current moment; zetaexpAn expected critical obstacle avoidance angle of the unmanned aerial vehicle relative to the obstacle at the current moment; the relative angle between the speed and the obstacle is
Delta t is the time difference from the moment t to the moment t +1, and rho is the distance from the unmanned aerial vehicle to the surface of the obstacle; rho0The extent of influence of the repulsive potential field formed by the obstacle O.
10. Unmanned aerial vehicle of artifical potential field of geometric improvement keeps away barrier mobile system, its characterized in that includes: an obstacle repulsive force field determining module, a speed obstacle avoidance force determining module and an obstacle avoidance control module,
the obstacle repulsion field determining module is used for determining an obstacle avoidance maneuvering angle required by the unmanned aerial vehicle in an obstacle avoidance mode according to the critical collision angle and the flight angle of the unmanned aerial vehicle at the current moment when the unmanned aerial vehicle needs to make the obstacle avoidance maneuvering, and determining the improved obstacle repulsion field repulsion force by using the obstacle avoidance maneuvering angle;
the speed obstacle avoidance force determining module is used for determining the speed obstacle avoidance force of the unmanned aerial vehicle according to the expected obstacle avoidance flight speed of the unmanned aerial vehicle based on the critical collision angle;
and the obstacle avoidance control module is used for determining obstacle avoidance control force of the unmanned aerial vehicle together according to the improved obstacle repulsive force field repulsive force determined by the obstacle repulsive force field determination module and the speed obstacle avoidance force of the unmanned aerial vehicle determined by the speed obstacle avoidance force determination module, and finishing obstacle avoidance of the unmanned aerial vehicle based on the obstacle avoidance control force of the unmanned aerial vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110967307.9A CN113485454B (en) | 2021-08-23 | 2021-08-23 | Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110967307.9A CN113485454B (en) | 2021-08-23 | 2021-08-23 | Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113485454A true CN113485454A (en) | 2021-10-08 |
| CN113485454B CN113485454B (en) | 2023-04-14 |
Family
ID=77947085
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110967307.9A Active CN113485454B (en) | 2021-08-23 | 2021-08-23 | Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113485454B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114296482A (en) * | 2021-12-27 | 2022-04-08 | 北京理工大学 | Unmanned aerial vehicle cluster obstacle avoidance method based on self-changing gain strategy |
| CN117930870A (en) * | 2024-03-21 | 2024-04-26 | 天津万森科技发展有限公司 | Real-time obstacle avoidance method and system for unmanned aerial vehicle |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109407705A (en) * | 2018-12-14 | 2019-03-01 | 厦门理工学院 | A kind of method, apparatus, equipment and the storage medium of unmanned plane avoiding barrier |
| CN109521794A (en) * | 2018-12-07 | 2019-03-26 | 南京航空航天大学 | A kind of multiple no-manned plane routeing and dynamic obstacle avoidance method |
| US20190361452A1 (en) * | 2018-05-22 | 2019-11-28 | King Fahd University Of Petroleum And Minerals | Method and system for controlling a vehicle |
| CN111650961A (en) * | 2020-05-29 | 2020-09-11 | 西安理工大学 | 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field |
| CN111781948A (en) * | 2020-06-18 | 2020-10-16 | 南京非空航空科技有限公司 | Unmanned aerial vehicle formation shape transformation and online dynamic obstacle avoidance method |
| CN112783194A (en) * | 2020-12-18 | 2021-05-11 | 上海电力股份有限公司吴泾热电厂 | Obstacle avoidance method for unmanned aerial vehicle flying in indoor coal yard |
-
2021
- 2021-08-23 CN CN202110967307.9A patent/CN113485454B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190361452A1 (en) * | 2018-05-22 | 2019-11-28 | King Fahd University Of Petroleum And Minerals | Method and system for controlling a vehicle |
| CN109521794A (en) * | 2018-12-07 | 2019-03-26 | 南京航空航天大学 | A kind of multiple no-manned plane routeing and dynamic obstacle avoidance method |
| CN109407705A (en) * | 2018-12-14 | 2019-03-01 | 厦门理工学院 | A kind of method, apparatus, equipment and the storage medium of unmanned plane avoiding barrier |
| CN111650961A (en) * | 2020-05-29 | 2020-09-11 | 西安理工大学 | 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field |
| CN111781948A (en) * | 2020-06-18 | 2020-10-16 | 南京非空航空科技有限公司 | Unmanned aerial vehicle formation shape transformation and online dynamic obstacle avoidance method |
| CN112783194A (en) * | 2020-12-18 | 2021-05-11 | 上海电力股份有限公司吴泾热电厂 | Obstacle avoidance method for unmanned aerial vehicle flying in indoor coal yard |
Non-Patent Citations (2)
| Title |
|---|
| 成浩浩等: "一种基于一致性的四旋翼无人机编队避障方法", 《飞行力学》 * |
| 熊超等: "基于碰撞锥改进人工势场的无人机避障路径规划", 《计算机工程》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114296482A (en) * | 2021-12-27 | 2022-04-08 | 北京理工大学 | Unmanned aerial vehicle cluster obstacle avoidance method based on self-changing gain strategy |
| CN114296482B (en) * | 2021-12-27 | 2023-11-28 | 北京理工大学 | Unmanned aerial vehicle cluster obstacle avoidance method based on self-variable gain strategy |
| CN117930870A (en) * | 2024-03-21 | 2024-04-26 | 天津万森科技发展有限公司 | Real-time obstacle avoidance method and system for unmanned aerial vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113485454B (en) | 2023-04-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Changqiang et al. | Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization | |
| CN108121358B (en) | Unmanned aerial vehicle control method | |
| CN110502032B (en) | Unmanned aerial vehicle cluster formation flying method based on behavior control | |
| CN107608347B (en) | Distributed control unmanned ship cluster enclosure tracking method | |
| CN113485454B (en) | Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field | |
| US7228227B2 (en) | Bezier curve flightpath guidance using moving waypoints | |
| Ross et al. | Learning monocular reactive uav control in cluttered natural environments | |
| Li et al. | Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone | |
| Saska et al. | Motion planning and control of formations of micro aerial vehicles | |
| CN109871031B (en) | Trajectory planning method for fixed-wing unmanned aerial vehicle | |
| Sprinkle et al. | Encoding aerial pursuit/evasion games with fixed wing aircraft into a nonlinear model predictive tracking controller | |
| US11014650B2 (en) | Moving body, moving body control system, moving body control method, interface device, and recording medium having program recorded thereon | |
| Li et al. | Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm | |
| CN114330115B (en) | Neural network air combat maneuver decision-making method based on particle swarm search | |
| CN114063644B (en) | Unmanned fighter plane air combat autonomous decision-making method based on pigeon flock reverse countermeasure learning | |
| CN114594788A (en) | Four-rotor unmanned aerial vehicle track planning method and system in unknown environment | |
| Gautam et al. | Application of guidance laws to quadrotor landing | |
| Ferreira et al. | An approach to avoid obstacles in mobile robot navigation: the tangential escape | |
| Serres et al. | Biomimetic autopilot based on minimalistic motion vision for navigating along corridors comprising U-shaped and S-shaped turns | |
| CN114779823A (en) | Unmanned aerial vehicle cooperative capture control method under saturation attack task | |
| Hartmann et al. | Competitive driving of autonomous vehicles | |
| Rehmatullah et al. | Vision-based collision avoidance for personal aerial vehicles using dynamic potential fields | |
| Hoy et al. | Collision-free navigation of an autonomous unmanned helicopter in unknown urban environments: sliding mode and MPC approaches | |
| Moreira et al. | Interception of non-cooperative UAVs | |
| WO2023097769A1 (en) | Unmanned ground vehicle-unmanned aerial vehicle collaborative autonomous tracking and landing method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |