CN110609552A - Method for planning formation plane flight path of underwater unmanned aircraft - Google Patents
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Abstract
The invention relates to a method for planning a formation plane flight path of an underwater unmanned vehicle, belonging to the technical field of unmanned underwater vehicles and flight path planning. Constructing a task space model according to an environmental information database or environmental information detected by a sensor; performing global track planning by adopting an improved and fused algorithm of an APF algorithm and an RRT algorithm based on the static environment information; and taking the global planning path point as a child target point, and performing local planning on the basis of the dynamic environment information in a segmented mode. According to the invention, in a global planning algorithm, through a certain improvement scheme, the problems of unreachable target and local optimum of APF are solved, three characteristics of uncertainty, non-optimum and slow convergence of RRT are improved, the deterministic track planning method and the stochastic track planning method are combined, the static planning method and the dynamic planning method are combined, the position planning method and the speed planning method are combined, the track reduction algorithm realizes the effect of further optimizing the track, and the task requirements of safety, optimality and instantaneity of the track planning of the unmanned aerial vehicle under multiple water are ensured.
Description
Technical Field
The invention relates to a method for planning a formation plane flight path of an underwater unmanned vehicle, belonging to the technical field of unmanned underwater vehicles and flight path planning.
Background
The Underwater Unmanned Vehicle is interpreted by an English word of Unmanned aircraft (UUV), is an autonomous Underwater Vehicle which does not need a mother ship to provide energy supply and information guidance, is powered by self-carried energy and is guided by an automatic control technology to complete expected tasks and works below the water surface for a long time. Due to the complex and dangerous environment for executing tasks and the full of unknown factors, in order to ensure the safety of operators, an unmanned aircraft is required; meanwhile, as the communication process has certain delay, and a certain part of tasks have high requirements on real-time performance, the aircraft is required to realize autonomous control. Therefore, the method has indispensable application value in the aspects of exploring ocean bottom resources, researching ocean science and technology, maintaining national ocean rights and interests, carrying out underwater military activities and the like. Grouping formed by multiple aircrafts has stronger reliability and fault tolerance, not only can improve the working efficiency, but also can realize complex tasks which cannot be completed by a single aircraft, realize the effect of '1 +1> 2', and has more universal application significance.
In fact, the tasks that require the use of multiple aircraft formation systems are often fraught with threat factors. Subject to some constraints, environmental information for a task area may not be fully available until the task is performed and even before the consist of the aircraft reaches the target area. Meanwhile, part of tasks can face the time-varying threat of the task space in the execution process, or the possibility of temporarily changing the target task exists, so that the real-time performance requirement of the tasks is high. With the increasingly complex structure of the aircraft, the requirement on the cooperativity of the marshalling is increasingly enhanced, and the task environment is increasingly changeable, so that the requirement of having an autonomous planning track function is provided for the aircraft and the marshalling thereof, and the requirement of having both offline and online planning and coexistence of local and global planning is provided for the track planning process.
The flight path planning technology, also called path planning technology, is a core technology of a mission planning system, and is generally applied to navigation systems of carriers such as underwater unmanned vehicles, surface ships, unmanned vehicles, autonomous mobile robots, unmanned vehicles and the like. The trajectory plan may be defined as: a process of finding a navigable trajectory from an initial position and pose to a target position and pose based on a determined criterion, given the target position, within a constrained task space. Constraints generally include obstacle space constraints (including fixed obstacles and moving obstacles), vehicle physical constraints (including maximum rotation angle constraints, minimum straight-ahead length constraints, altitude or depth constraints, maximum speed and acceleration constraints, and energy consumption constraints), enemy threat constraints (including non-own radar detection areas and military restricted areas), weather and environmental threats (including strong wind areas, strong ocean current areas and severe weather-affected areas).
From the viewpoint of target parameters of the flight path planning, the flight path planning is divided into position planning and speed planning. In the position planning problem, an effective position track needs to be found, so that the essence is to solve a geometric solution under a constraint condition, and the problem belongs to kinematics; in the speed planning problem, a real-time speed change strategy needs to be proposed, which can also be called an effective speed trajectory, and the nature of which will be a dynamic problem.
The general track planning problem is divided into two levels. The first level is global flight path planning, large-range low-precision detection is carried out on the environment from a macroscopic level, coarse planning is carried out on the flight path, and key nodes are reserved as guiding information of local flight path planning. And the second level is local track planning and collision avoidance, the environment is detected in a small range and high precision from the local layer by regions, the found real-time threat and the occurred emergency are avoided, and the track obtained by rough planning is optimized in a segmented manner.
The existing algorithms have defects in some aspects, such as the fact that the deterministic algorithm is easy to fall into local optimization, the randomness algorithm is poor in flight path optimization, and how to improve the defects or synthesize two or more algorithms to make up for the deficiencies is to be solved; meanwhile, the general flight path planning algorithm only carries out position planning, and does not consider speed planning, so that the problem of collision avoidance of dynamic obstacles cannot be solved, and how to supplement the short plate needs to be solved.
Disclosure of Invention
The invention aims to overcome the defects of local optimization of a deterministic algorithm and poor optimization of a stochastic algorithm, provides a method for planning a formation plane track of an underwater unmanned vehicle, and relates to a safe, optimized and real-time track planning scheme which combines static planning with dynamic planning and combines position planning with speed planning.
The invention aims to realize the method for planning the formation plane flight path of the underwater unmanned vehicle, which specifically comprises the following steps:
step one, constructing a task space model according to an environmental information database or environmental information detected by a sensor;
secondly, performing global track planning by adopting an improved and fused algorithm of an artificial potential field method (APF algorithm) and a fast extended random tree method (RRT algorithm) based on the static environment information;
and thirdly, taking the global planning path point as a child target point, and performing local planning in a segmented manner based on the dynamic environment information.
The invention also includes such features:
1. the first step specifically comprises dividing a two-dimensional task space with a certain depth into an obstacle space omega based on known environment informationobstacleAnd navigable space omegafree(ii) a Wherein, the area with the threat of obstacles, ocean currents, enemies and restricted navigation areas is an obstacle space, which is replaced by overlapping a certain number of circles with a certain radius, and the basic unit circle of the threat area is expanded; in the task space, the non-obstacle space is a navigable space, and the starting position and the target position are located in the navigable space.
2. The second step specifically comprises the following steps:
step 2.1, grouping the whole aircraft into particles according to the global planning situation, performing large-range, static and off-line track planning according to the obtained partial prior information, and outputting the global track in a position sequence form;
2.2, in an initial state, detecting and recording local optimal traps in real time by adopting an improved APF algorithm of an additional trap potential field and an improved repulsion potential field; when the flight path loiters on the equipotential line, the trap position is immediately brought into a real-time planned task space environment information base, and the generated repulsive potential field can be superposed for multiple times until the local lowest potential is filled; when the new expansion position given by the algorithm is overlapped with the obstacle area or the connecting line of the current position and the new position passes through the obstacle area, defining the new position as an infeasible point, carrying out 18 equal division search on a circle with the radius of one search step length by taking the current position as the center of the circle, and taking the lowest point of the closed potential field as a next suggested point after excluding the infeasible point;
step 2.3, detecting the marshalling position and the stress state of the aircraft in real time, jumping off the local optimal trap by adopting an improved RRT algorithm guided by a variable probability target when the track is not stopped, and switching back to the improved APF algorithm when judging that the track is separated from a local optimal point;
2.4, setting a time threshold for the complex terrain, switching to an improved RRT with bidirectional balanced expansion when the improved APF algorithm cannot give a complete track after the deadline time is reached, and expanding the latest path point and target point given by the improved APF algorithm to the inside by taking the latest path point and target point as reference points at two ends until the track is found to finish planning;
step 2.5, when planning the flight path, carrying out feasibility detection on each new state, and carrying out re-planning on infeasible new states to avoid falling into a threat area in the next step;
and 2.6, after the flight path planning is finished, carrying out flight path reduction.
3. The artificial potential field method, namely the APF algorithm in the step two specifically comprises the following steps:
step 1, inputting initial pose, target pose, central position, radius and number information of an obstacle, performing initial setting, and expanding the step length if the expansion distance is the case;
step 2, calculating the resultant force applied to the aircraft under the current pose each time from the initial pose, advancing an expansion step length along the direction, transforming to the next pose, and circulating the process until the target pose is reached;
step 3, outputting a pose sequence connecting the initial pose to the initial pose if the feasible flight path exists; otherwise it indicates its absence.
4. The resultant force of the aircraft in the current pose is calculated by the following steps:
step 1, calculating by adopting a basic APF algorithm:
the gravitational potential function generated at any point p (x, y) target point in the task space is:
the gravitational function generated by the target point is:in the formula: k is a radical ofattTo attract potential field gain, pgoal(xgoal,ygoal) As coordinates of the position of the target point, egoalIs a unit vector pointed to the target point by the aircraft;
the repulsive force potential generated by each obstacle at any point p (x, y) in the task space is as follows:
each obstacle generates a repulsive force ofIn the formula: k is a radical ofobsRepelling potential field gain, R, for barrier regionsobsIs the actual radius of the obstacle area, domIs the puffing distance, p0To influence the threshold of the range by the repulsive force, pobs(xobs,yobs) Coordinates of the center position of the obstacle area;
the global potential function is:resultant force ofIn the formula: n is the number of barrier regions; when setting the extension step deltaiThen, the next step position coordinate is obtained by expanding one step length along the direction of resultant force based on the current position;
step 2, adopting an APF algorithm for improving the repulsive potential field to calculate:
aiming at the problem that the target can not be reached in the basic APF algorithm, the repulsive potential field function is improved as follows:
in the formula: n is2＝2；kobsRepelling potential field gain, R, for barrier regionsobsIs the actual radius of the obstacle area, domIs the puffing distance, p0As a threshold value of the influence range of repulsion forces, pobs(xobs,yobs) Coordinates of the center position of the obstacle area;
the repulsion function is improved as
In the formula:
and 3, calculating by adopting an APF algorithm of an additional trap:
aiming at the problem of wandering local minimum value existing in a basic APF algorithm, a detection circle is set, the circle center is the latest N position coordinate average value planned by the algorithm, N is 19, the radius is an expansion step length, if the planned latest position is located in the detection circle, the circle center of the detection circle is added into a 'trap library', all points in the trap library generate an additional trap potential field with a repulsive effect, and the potential function is as follows:
in the formula: p is a radical oftrapFor "trap" position, ktrapA trap potential field gain;
the repulsion force is as follows:
in the formula: e.g. of the typetrapTo be a unit vector directed to the aircraft by the "trap" position,
the global potential function is thusResultant force ofWhen setting the extension step deltaiThen, the next step position coordinates will be obtained based on the current position by one step in the direction of the resultant force.
5. The fast expansion random tree method (RRT algorithm) in the second step specifically comprises the following steps:
step 1, inputting initial pose, target pose, center position of an obstacle, radius and quantity information, performing initial setting, expanding step length if expanding distance, and taking an initial position as a first node of a tree;
step 2, generating random sample points, wherein the horizontal and vertical coordinates of the random sample points can be selected from any point in the map;
step 3, searching a nearest node in the existing node of the tree, expanding an expansion step length from the nearest node to the random sample point to obtain a point to be detected, if a connecting line from the nearest node to the point to be detected is overlapped with an obstacle area, giving up the expansion, and returning to the step 2; otherwise, adding the point to be detected into the tree, returning to the step 2, and taking the target point as the last node of the tree when the distance between the point to be detected and the target point is less than an expansion step length, and finishing the circulation;
and 4, reversely inquiring the father node of the last node until reaching the root node, and finally forming a path.
6. The fast spreading random tree method, namely the improved algorithm of the RRT algorithm in the step two specifically comprises the following contents:
(1) and the improved RRT algorithm for improving the non-optimal characteristic comprises the following steps:
on the basis of a basic framework, a constraint condition is proposed for the point to be checked in step 3 of the RRT algorithm: the distances from the point to be checked to all nodes of the tree are not less than an expansion step length, otherwise, the growth is abandoned, and the step 2 of the RRT algorithm is returned;
(2) and a variable probability target oriented RRT algorithm:
on the basis of a basic framework, setting a random event A which obeys two-point distribution in the random sample point expansion process of the step 2 of the RRT algorithm, wherein when the event A occurs, an expanded new node target point is still a random point; otherwise, the expanded new node target point is a task target point;
(3) the bidirectional extended balanced connection improved RRT algorithm comprises the following steps:
on the basis of a basic frame, simultaneously expanding two trees from a starting point and a target point, wherein the two trees respectively take a node or a random point which is closest to a self tree in all nodes of an opposite tree as a temporary target point, any one tree finishes one-time expansion action to detect whether the two trees can be communicated, and when the closest distance between the two trees is less than an expansion step length, the two trees are determined to be communicated, and the query of the trees is started; and performing reverse query on the tree expanded from the starting position, and performing forward query on the tree expanded from the target position.
7. Step 2.6, the route reduction specifically includes that for the planned route path point set, starting from the starting point, checking whether the connecting lines from all the path points to the point have obstacles after the point, and defining that the connecting lines have no obstacles, and the connecting lines have 'through vision' among the path points; let current point location be piChecking all pj(j > i), then the "see-through" condition is e (p)i,pj)∈Ωfree(ii) a Greedy-based strategyIn the path points of all the sight-through, the one with the newest generation sequence is selected as the second point in the reduction track, namely p meeting the sight-through conditionjAdding a reduction track set to the maximum value of the middle j; starting from the second point, continuously searching the next path point which conforms to the rule, and repeating the processes until the target point is searched, thus completing the reduction; the above reduction is repeated until irreducible.
8. The third step specifically comprises the following steps:
step 3.1, proposing a suggested speed for the aircraft, pointing the direction to a target point, and ensuring that the linear speed value is in a proper interval;
step 3.2, detecting whether collision risks between the aircrafts and obstacles exist or not by adopting a speed obstacle method, namely a VO algorithm, when sailing at the suggested speed;
3.3, if the navigation has collision risk according to the original speed, correcting the suggested speed by an algorithm based on the principle, wherein the given correction quantity meets the safety firstly, the linear speed variation as small as possible secondly and the course variation as small as possible finally; if the navigation is free of collision risk according to the original speed, the suggested speed planned next step keeps the direction unchanged, and the linear speed value is adjusted to be within an ideal interval through negative feedback.
9. The principle of the VO algorithm is: defining λ (p, v) { p + vt | t ≧ 0} as a ray extending from the end point p in the direction v, defining a relative obstacle(whereinAs the minkowski vector sum) as the center position p of the obstacle BBAs the center of circle, the radius R of the aircraft AAAnd obstacle radius (R)B+dom) Circles with a sum of radii defining the relative velocity vrIs the speed v of the aircraftAWith barrier velocity vBVector difference, tangent to relative obstacle from central position O of aircraft, starting from relative speed vectorThe point is the position of the aircraft, and when the relative velocity vector end point is positioned between two tangent lines, namelyThere is a risk of collision; translating the two tangent lines along the obstacle velocity vector, and defining the translated two tangent lines and the small-angle sector between the two tangent lines as the speed obstacle area with A opposite to BThus, according to the VO algorithm, when there are multiple moving obstacles, a sufficient condition that there is no risk of collision is that the vehicle speed vector end point is outside the union of multiple speed obstacle zones.
Compared with the prior art, the invention has the beneficial effects that: in the global planning algorithm, the problems of unreachable target and local optimal of the APF are solved through a certain improvement scheme, three characteristics of non-determination, non-optimal and slow convergence of the RRT are improved, and the deterministic algorithm and the stochastic algorithm are combined to realize mutual advantage complementation. The track reduction algorithm achieves the effect of further optimizing the track. In a local planning algorithm, aiming at the problem of dynamic obstacle avoidance which cannot be solved by position planning, VO is adopted for speed planning, and coordination of dynamic obstacle avoidance and multi-aircraft navigation conflict is realized.
The invention takes marine geoscience research and resource survey search as application backgrounds, takes multi-underwater unmanned aircraft marshalling as objects, and develops research of autonomous flight path planning algorithm design and application on a two-dimensional level according to a logic sequence of global first and local second. After the interference of static obstacles and dynamic obstacles on normal navigation is fully considered, flight path key path points and real-time speed change strategies of each aircraft are given according to different task space terrains, and the successful execution of tasks under the premise of safe navigation of aircraft grouping is guaranteed.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a diagram of a model of an obstacle region in step one of the present invention;
FIG. 3 is a flow chart of step two of the present invention;
FIG. 4 is a schematic illustration of aircraft forces according to the modified APF algorithm;
FIG. 5 is an expanded schematic diagram of the RRT tree;
FIG. 6 is a schematic diagram of a query of an RRT tree;
FIG. 7 is a scenario where the RRT is not valid for expansion under additional geometric constraints;
FIG. 8 is a scenario where the RRT is extended in effect under additional geometric constraints;
FIG. 9 is a case of simultaneous expansion of RRT dual trees;
FIG. 10 is a result of a full-run global planned trajectory simulation using the modified APF algorithm;
FIG. 11 is a reduced track after global planning using the modified APF algorithm;
FIG. 12 is a global planned trajectory simulation result using improved APF planning to locally optimal traps and using RRT to jump out;
FIG. 13 is a reduced track after global planning using modified APF planning to locally optimal traps and using RRT to jump out;
FIG. 14 is a global flight path simulation result for continued planning with an improved APF planning timeout and with a bidirectional extended balanced connected RRT;
FIG. 15 is a reduced track following global planning with improved APF planning timeout and RRT continuation with bi-directional extended balanced connections;
FIG. 16 is a flow chart of step three of the present invention;
FIG. 17 is a schematic view of a velocity barrier zone for a single barrier;
FIG. 18 is a schematic view of a velocity barrier zone for multiple barriers;
FIG. 19 is an initial state of the local trajectory planning simulation process;
FIG. 20 is a state of completion of the local trajectory planning simulation process;
FIG. 21 is a velocity profile of a local trajectory planning simulation process;
FIG. 22 is a path of a partial path planning simulation process.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
A method for planning a formation plane track of an underwater unmanned aircraft specifically comprises the following steps:
the method comprises the following steps: according to the environment information detected by the environment information database or the sensor, a task space model is constructed, and as the flight path planning process is carried out in a two-dimensional space, the task space is described as follows: Ω { (x, y) | xmin≤x≤xmax,ymin≤y≤ymaxZ ≡ C } where the obstacle area has a substantially unit circle center of (x ≡ C) }obstacle,yobstacle) Radius RobstacleThe puffing distance is dover-measureThe model is shown in figure 2;
step two: and performing global track planning by adopting an improved and fused algorithm of an artificial potential field method (APF) and a fast extended random tree method (RRT) based on the static environment information. The global track plan is a first level of multi-aircraft consist track planning. The sailor carries out large-range, static and off-line flight path planning according to the obtained partial prior information. And obtaining a rough feasible flight path through global flight path planning. The detectable global environmental information is typically a large-scale topographical feature, and thus is grouped into particles throughout the aircraft under global planning.
In the second step, the global track planning process is as follows:
(I) under the global planning condition, the whole aircraft is grouped into particles, large-range, static and offline flight path planning is carried out according to the obtained partial prior information, and the global flight path is output in a position sequence mode;
(II) in an initial state, a modified APF algorithm employing an additional trap potential field and a modified repulsive potential field. Detecting and recording in real time the local optimal traps experienced: when the flight path loiters on the equipotential line, the trap position is immediately brought into a real-time planned task space environment information base, and the generated repulsive potential field can be superposed for multiple times until the local lowest potential is filled;
(III) detecting the marshalling position and the stress state of the aircraft in real time, when the flight path given by the algorithm is not stopped, jumping off the local optimal trap by adopting an improved RRT algorithm guided by a variable probability target, and switching back to the improved APF algorithm when judging that the flight path is separated from the local optimal point;
(IV) setting a time threshold for an excessively complex terrain, switching to an improved RRT with bidirectional balanced expansion when the improved APF algorithm cannot give a complete track or the motion range cannot exceed a certain area after the deadline is reached, and expanding the latest path point and the target point given by the improved APF algorithm to the inside by taking the two reference points as two ends until the track is found to finish planning;
(V) when planning the flight path, carrying out feasibility detection on each new state, and re-planning infeasible new states to avoid falling into a threat area in the next step;
(VI) after the flight path planning is finished, carrying out flight path reduction;
the flow chart is shown in figure 3.
In step two, the APF algorithm includes the steps of:
step 2.1: inputting an initial pose, a target pose and barrier information (including a central position, a radius and the number), and performing initial setting (such as a bulking distance, an expanding step length and the like);
step 2.2: calculating resultant force applied to the aircraft in the current pose from the initial pose each time, advancing an expansion step length along the direction, transforming to the next pose, and circulating the process until the target pose is reached;
for the resultant force, the following method was used:
(I) basic APF algorithm
The gravitational potential function generated at any point p (x, y) target point in the task space is:
the gravitational function generated by the target point is:in the formula: k is a radical ofattFor attracting potential field gain，pgoal(xgoal,ygoal) As coordinates of the position of the target point, egoalIs a unit vector pointed to the target point by the aircraft; the repulsive force potential generated by each obstacle at any point p (x, y) in the task space is as follows:
the repulsion force generated by each obstacle is:
in the formula: k is a radical ofobsRepelling potential field gain, R, for barrier regionsobsIs the actual radius of the obstacle area, domIs the puffing distance, p0To influence the threshold of the range by the repulsive force, pobs(xobs,yobs) Coordinates of the center position of the obstacle area;
the global potential function is:resultant force ofIn the formula: n is the number of barrier regions; when setting the extension step deltaiThen, the next step position coordinate is obtained by expanding one step length along the direction of resultant force based on the current position;
(II) APF algorithm for improving repulsive potential field
When the target point is located near the threat zone, the effect of the superimposed field formed may be that the "lifting" effect of the repulsive potential field on the potential field is much larger than the "depressing" effect of the attractive potential field on the potential field, so that the target point will no longer be the global lowest potential point. According to the algorithm design, the aircraft is far away from the threat zone, and meanwhile, the side effect of being far away from the target point is generated. Aiming at the problem that the target cannot be reached in the basic APF algorithm, the repulsive potential field function is improved as follows:
in the formula: n is2＝2；
The repulsion function is improved as:
in the formula:
(III) APF algorithm for additional trap potential field
Aiming at the problem of wandering local minimum value existing in a basic APF algorithm, a detection circle is set, the circle center is the latest N position coordinate average value planned by the algorithm (N is 19), the radius is an expansion step length, if the planned latest position is located in the detection circle, the circle center of the detection circle is added into a trap library, all points in the trap library generate an additional trap potential field with a repulsive effect, and the potential function is as follows:
in the formula: p is a radical oftrapFor "trap" position, ktrapA trap potential field gain;
the repulsion force is as follows:
in the formula: e.g. of the typetrapTo be a unit vector directed to the aircraft by the "trap" position,
wherein, the pseudo code of the trap judgment is as follows:
TABLE 1 "trap" decision algorithm pseudocode
Step 2.3: if the feasible flight path exists, outputting a pose sequence connecting the initial pose to the initial pose; otherwise it indicates its absence.
According to the above description, the APF algorithm pseudo code is:
TABLE 2 APF Algorithm pseudo-code
The forces are shown in fig. 4 according to the algorithm described above.
In the second step, the content of the re-planning algorithm is as follows:
and when the new expansion position given by the algorithm is overlapped with the obstacle area or a connecting line of the current position and the new position passes through the obstacle area, defining the new position as an infeasible point, carrying out 18 equal division search on a circle with the radius of one search step length by taking the current position as the center of the circle, and taking the lowest point of the closed potential field as a next step suggestion point after eliminating the infeasible point.
According to the above description, the algorithm pseudo code is:
TABLE 3 Reconfiguration Algorithm pseudo code
In step two, the RRT and its modified content are:
(I) basic RRT algorithm:
step 2.4: inputting an initial pose, a target pose and barrier information (including a central position, a radius and the number), performing initial setting (such as a bulking distance, an expanding step length and the like), and taking an initial position as a first node of a tree;
step 2.5: generating random sample points, wherein the horizontal and vertical coordinates of the random sample points can be selected from any point in the map;
step 2.6: searching a nearest node in the existing node of the tree, expanding an expansion step length from the nearest node to the random sample point to obtain a point to be detected, if a connecting line from the nearest node to the point to be detected is overlapped with an obstacle area, giving up the expansion, and returning to the step 2.5; otherwise, adding the point to be detected into the tree, returning to the step 2.5, and taking the target point as the last node of the tree until the distance between the point to be detected and the target point is less than an expansion step length, and finishing the circulation;
in the above, steps 2.4 to 2.6 are tree expansion processes, the expansion process is shown in fig. 5, and the pseudo code thereof is:
TABLE 4 extended pseudo code of RRT Tree
Step 2.7: reversely inquiring the father node of the last node until reaching the root node, and finally forming a path;
this step is a tree query, the query process is shown in fig. 6, and the pseudo code is:
TABLE 5 query pseudocode for RRT Tree
(II) improved RRT algorithm to improve non-optimal characteristics:
on the basis of the basic RRT algorithm, a constraint condition is provided for the point to be detected in the step 2.6: the distance from the point to be checked to all nodes of the tree is not less than an expansion step length, otherwise, the growth is abandoned, and the step 2.5 is returned; the case of extension invalidation is shown in fig. 7 and the case of extension validation is shown in fig. 8.
The additional geometry constraint pseudo-code is:
TABLE 6 RRT additional geometry constraint pseudo-code
(III) variable probability target oriented RRT algorithm:
on the basis of a basic RRT algorithm, a random event A which obeys two-point distribution is set in the random sample point expansion process in the step 2.5, and when A occurs, the expanded new node target point is still a random point; when in useWhen the task happens, the expanded new node target point is a task target point; the pseudo code is:
TABLE 7 variable probability target steering RRT algorithm pseudocode
(IV) the bi-directionally extended balanced join improvement RRT algorithm:
on the basis of a basic RRT algorithm, two trees are expanded from a starting point and a target point at the same time, the two trees respectively take a node or a random point which is closest to a self tree in all nodes of an opposite tree as a temporary target point, any one tree completes one expansion action to detect whether the two trees can be communicated, and when the closest distance between the two trees is less than one expansion step length, the two trees are determined to be communicated, and the query of the trees is started. And performing reverse query on the tree expanded from the starting position, and performing forward query on the tree expanded from the target position. The expansion process is shown in fig. 9, and the pseudo code is:
TABLE 8 Bi-directionally extended balanced concatenation improved RRT Algorithm pseudo code
In step two, the contents of the track reduction algorithm are as follows:
for the planned track path point set, starting from the starting point, checking whether the connecting lines from all path points to the point after the point have obstacles or not, and defining that the connecting lines have no obstacles and are in 'communication' with each other. And selecting the path point with the latest generation sequence as a second point in the reduction track from all the path points of the full sight based on the greedy strategy. And then sending from the second point, and continuously searching the next path point which accords with the rule. And repeating the above processes until the target point is searched, and completing the reduction once. The pseudo code is:
TABLE 9 Primary track reduction pseudocode
The above reduction is repeated until irreducible. The pseudo code is:
TABLE 10 repeat track reduction pseudocode
Carrying out classification simulation experiment on the whole of the step two, and setting a threshold value rho of the influence range of the repulsive force0100, puffing distance domThe map range is (-100,700) × (-100,700), and in the random map, the whole-course global planning track using the modified APF algorithm is shown in fig. 10, and the reduced track is shown in fig. 11. The global planned route which is planned to the local optimal trap by adopting the improved APF and jumped out by adopting the RRT is shown in the attached figure 12, and the reduced route is shown in the attached figure 13. The path planned by planning timeout by the improved APF and continuing the planning by the RRT of the bidirectional expansion balanced connection is shown in the attached figure 14, and the reduced path is shown in the attached figure 15.
Step three: and taking the global planning path point as a sub-target point to perform local planning based on the dynamic environment information segmentation. The local track planning is a second level of multi-aircraft consist track planning. And the aircraft takes the path point of the reduced track obtained by global planning as a temporary sub-target point of the segmented local track planning to carry out small-range, dynamic and online track planning. And through local track planning, a more accurate real-time feasible track is obtained in a segmented manner. In addition to global sounding environment information, local sounding environment information is typically a small scale object, so individual aircraft will no longer be considered particles in local planning scenarios.
In step three, the global track planning process is as follows:
step 3.1: a suggested speed is provided for the aircraft, the direction points to a target point, and the linear speed value is in a proper interval;
step 3.2: detecting whether collision risks between the aircrafts and obstacles exist when the aircraft sails at the suggested speed by adopting a speed obstacle method (VO);
step 3.3: if the navigation has collision risk according to the original speed, the algorithm corrects the suggested speed based on the principle, and the given correction quantity meets the safety firstly, the linear speed variation as small as possible secondly and the course variation as small as possible finally; if the navigation is free of collision risk according to the original speed, the suggested speed planned next step keeps the direction unchanged, and the linear speed value is adjusted to be within an ideal interval through negative feedback.
The flow chart is shown in figure 16.
In step three, the content of the VO algorithm is as follows:
defining a relative obstacle as a circle with the center position of the obstacle B as the center of the circle and the sum of the radii of the aircraft A and the obstacle as the radius, defining a relative speed as the vector difference between the speed of the aircraft and the speed of the obstacle, making a tangent from the center position of the aircraft to the relative obstacle, setting the starting point of the relative speed vector as the position of the aircraft, and when the terminal point of the relative speed vector is positioned between the two tangent lines, having collision risk. And translating the two tangent lines along the obstacle speed vector, defining the translated two tangent lines and a small-angle sector sandwiched by the two tangent lines as a speed obstacle area of A relative to B, wherein according to the VO algorithm, the sufficient condition that the collision risk does not exist is that the terminal point of the speed vector of the aircraft is positioned outside the speed obstacle area, and the speed obstacle area of a single obstacle is shown in figure 17. Thus, when there are multiple moving obstacles, a sufficient condition that there is no risk of collision is that the vehicle velocity vector endpoint is outside the union of multiple velocity obstacle zones, as shown in fig. 18.
Performing simulation experiments on the third step, setting the number of aircrafts to be 3 and the number of moving obstacles to be 3, wherein the movement rule of the obstacles is as follows:
TABLE 11 law of motion of moving obstacles
Initial position information is shown in fig. 19, position information of a planning completion state is shown in fig. 20, a speed change strategy provided by an algorithm for an aircraft during a voyage is shown in fig. 21, and a track formed by the strategy is shown in fig. 22.
According to the simulation result, the vehicles are kept out of the safe distance from each other and the vehicles and the obstacles are kept out of the safe distance by adjusting the speed in the process of travelling. Meanwhile, the speed of the aircraft is kept within a reasonable interval, and the target is safely reached. Therefore, the local track planning of real-time decision under the dynamic environment of multiple aircrafts is realized.
In conclusion, the invention discloses a method for planning a formation plane flight path of an unmanned underwater vehicle, and belongs to the technical field of unmanned underwater vehicles and flight path planning. The invention solves the problem of plane track planning of formation of unmanned underwater vehicles by improving and combining an artificial potential field method, a fast expansion random tree method and a speed obstacle method, and comprises the following steps: and constructing a task space model according to the known environment information, carrying out global flight path planning based on the static environment information, and carrying out local planning by taking the global planning path point as a sub-target point based on the dynamic environment information in a segmented manner. The algorithm provided by the invention combines the deterministic flight path planning method with the stochastic flight path planning method, combines the static planning with the dynamic planning, and combines the position planning with the speed planning, thereby ensuring the task requirements of safety, optimality and instantaneity of the multi-underwater unmanned aircraft flight path planning.
Claims (10)
1. A method for planning a formation plane track of an underwater unmanned aircraft is characterized by comprising the following steps:
step one, constructing a task space model according to an environmental information database or environmental information detected by a sensor;
secondly, performing global track planning by adopting an artificial potential field method (APF algorithm) and a fast extended random tree method (RRT) algorithm improvement and fusion algorithm based on the static environment information;
and thirdly, taking the global planning path point as a child target point, and performing local planning in a segmented manner based on the dynamic environment information.
2. The underwater unmanned vehicle formation plane track planning method according to claim 1, characterized in that: the first step specifically comprises dividing a two-dimensional task space with a certain depth into an obstacle space omega based on known environment informationobstacleAnd navigable space omegafree(ii) a Wherein, the area with the threat of obstacles, ocean currents, enemies and restricted navigation areas is an obstacle space, which is replaced by overlapping a certain number of circles with a certain radius, and the basic unit circle of the threat area is expanded; in the task space, the non-obstacle space is a navigable space, and the starting position and the target position are located in the navigable space.
3. The method for planning the formation plane flight path of the underwater unmanned aerial vehicle according to claim 1, wherein the second step specifically comprises the following steps:
step 2.1, grouping the whole aircraft into particles according to the global planning situation, performing large-range, static and off-line track planning according to the obtained partial prior information, and outputting the global track in a position sequence form;
2.2, in an initial state, detecting and recording local optimal traps in real time by adopting an improved APF algorithm of an additional trap potential field and an improved repulsion potential field; when the flight path loiters on the equipotential line, the trap position is immediately brought into a real-time planned task space environment information base, and the generated repulsive potential field can be superposed for multiple times until the local lowest potential is filled; when the new expansion position given by the algorithm is overlapped with the obstacle area or the connecting line of the current position and the new position passes through the obstacle area, defining the new position as an infeasible point, carrying out 18 equal division search on a circle with the radius of one search step length by taking the current position as the center of the circle, and taking the lowest point of the potential field as a next step suggestion point after excluding the infeasible point;
step 2.3, detecting the marshalling position and the stress state of the aircraft in real time, jumping off the local optimal trap by adopting an improved RRT algorithm guided by a variable probability target when the track is not stopped, and switching back to the improved APF algorithm when judging that the track is separated from a local optimal point;
2.4, setting a time threshold for the complex terrain, switching to an improved RRT with bidirectional balanced expansion when the improved APF algorithm cannot give a complete track after the deadline is reached, and expanding the latest path point and target point given by the improved APF algorithm to the inside until the track is found to finish planning by taking the latest path point and target point as reference points at two ends;
step 2.5, when planning the flight path, carrying out feasibility detection on each new state, and carrying out re-planning on infeasible new states to avoid falling into a threat area in the next step;
and 2.6, after the flight path planning is finished, carrying out flight path reduction.
4. The method for planning the formation plane track of the underwater unmanned aerial vehicle according to claim 3, wherein the Artificial Potential Field (APF) algorithm in the second step specifically comprises the following steps:
step 1, inputting initial pose, target pose, central position, radius and number information of an obstacle, performing initial setting, and expanding the step length if the expansion distance is adopted;
step 2, calculating the resultant force applied to the aircraft under the current pose each time from the initial pose, advancing an expansion step length along the direction, transforming to the next pose, and circulating the process until the target pose is reached;
step 3, outputting a pose sequence connecting the initial pose to the initial pose if the feasible flight path exists; otherwise it indicates its absence.
5. The method for planning the formation plane flight path of the underwater unmanned aircraft as claimed in claim 4, wherein the resultant force of the aircraft in the current pose is calculated by adopting the following steps:
step 1, calculating by adopting a basic APF algorithm:
the gravitational potential function generated at any point p (x, y) target point in the task space is:
the gravitational function generated by the target point is:
in the formula: k is a radical ofattTo attract potential field gain, pgoal(xgoal,ygoal) As coordinates of the position of the target point, egoalIs a unit vector pointed to the target point by the aircraft;
the repulsive force potential generated by each obstacle at any point p (x, y) in the task space is as follows:
each obstacle generates a repulsive force of
In the formula: k is a radical ofobsRepelling potential field gain, R, for barrier regionsobsIs the actual radius of the obstacle area, domIs the puffing distance, p0To influence the threshold of the range by the repulsive force, pobs(xobs,yobs) Coordinates of the center position of the obstacle area;
the global potential function is:resultant force ofIn the formula: n is the number of barrier regions; when setting the extension step deltaiThen, the next step position coordinate is obtained by expanding one step length along the direction of resultant force based on the current position;
step 2, adopting an APF algorithm for improving the repulsive potential field to calculate:
aiming at the problem that the target can not be reached in the basic APF algorithm, the repulsive potential field function is improved as follows:
in the formula: n is2＝2；kobsRepelling potential field gain, R, for barrier regionsobsIs the actual radius of the obstacle area, domIs the puffing distance, p0To influence the threshold of the range by the repulsive force, pobs(xobs,yobs) Coordinates of the center position of the obstacle area;
the repulsion function is improved as
In the formula:
and 3, calculating by adopting an APF algorithm of an additional trap:
aiming at the problem of wandering local minimum value existing in a basic APF algorithm, a detection circle is set, the circle center is the latest N position coordinate average value planned by the algorithm, N is 19, the radius is an expansion step length, if the planned latest position is located in the detection circle, the circle center of the detection circle is added into a 'trap library', all points in the trap library generate an additional trap potential field with a repulsive effect, and the potential function is as follows:
in the formula: p is a radical oftrapTo be "traps"position, ktrapA trap potential field gain;
the repulsion force is as follows:
in the formula: e.g. of the typetrapTo be a unit vector directed to the aircraft by the "trap" position,
the global potential function is thusResultant force ofWhen setting the extension step deltaiThen, the next step position coordinates will be obtained based on the current position by one step in the direction of the resultant force.
6. The method for planning the formation plane track of the underwater unmanned aerial vehicle according to claim 5, wherein the fast-expanding random tree (RRT) algorithm in the second step specifically comprises the following steps:
step 1, inputting initial pose, target pose, center position of an obstacle, radius and quantity information, performing initial setting, expanding step length if expanding distance, and taking an initial position as a first node of a tree;
step 2, generating random sample points, wherein the horizontal and vertical coordinates of the random sample points can be selected from any point in the map;
step 3, searching a nearest node in the existing node of the tree, expanding an expansion step length from the nearest node to the random sample point to obtain a point to be detected, if a connecting line from the nearest node to the point to be detected is overlapped with an obstacle area, giving up the expansion, and returning to the step 2; otherwise, adding the point to be detected into the tree, returning to the step 2, and taking the target point as the last node of the tree until the distance between the point to be detected and the target point is less than an expansion step length, and finishing the circulation;
and 4, reversely inquiring the father node of the last node until reaching the root node, and finally forming a path.
7. The method for planning the formation plane path of the underwater unmanned aerial vehicle according to claim 1 or claim 6, wherein the improved algorithm of the fast-expanding random tree (RRT) algorithm in the second step specifically comprises the following steps:
(1) and the improved RRT algorithm for improving the non-optimal characteristic comprises the following steps:
on the basis of a basic framework, a constraint condition is proposed for the point to be checked in step 3 of the RRT algorithm: the distances from the point to be checked to all nodes of the tree are not less than an expansion step length, otherwise, the growth is abandoned, and the RRT algorithm returns to the step 2;
(2) and a variable probability target oriented RRT algorithm:
on the basis of a basic framework, setting a random event A which obeys two-point distribution in the random sample point expansion process of the step 2 of the RRT algorithm, wherein when the event A occurs, an expanded new node target point is still a random point; otherwise, the expanded new node target point is a task target point;
(3) the bidirectional extended balanced connection improved RRT algorithm comprises the following steps:
on the basis of a basic frame, simultaneously expanding two trees from a starting point and a target point, wherein the two trees respectively take a node or a random point which is closest to a self tree in all nodes of an opposite tree as a temporary target point, any one tree finishes one-time expansion action to detect whether the two trees can be communicated, and when the closest distance between the two trees is less than one expansion step length, the two trees are determined to be communicated, and the query of the trees is started; and performing reverse query on the tree expanded from the starting position, and performing forward query on the tree expanded from the target position.
8. The underwater unmanned vehicle formation plane track planning method according to claim 3, characterized in that: step 2.6 the track reduction specifically includes for the planned set of track path points, starting from the starting pointChecking whether the connecting lines from all the path points to the point have obstacles after the point, and defining that the connecting lines have no obstacles and the path points are in 'communication'; let current point location be piChecking all pj(j > i), then the "see-through" condition is e (p)i,pj)∈Ωfree(ii) a Based on a greedy strategy, selecting the path point with the newest generation sequence from all the path points of the sight through as a second point in the reduced flight path, namely p meeting the sight through conditionjAdding a reduction track set to the maximum value of the middle j; starting from the second point, continuously searching the next path point which accords with the rule, repeating the processes until the target point is searched, and finishing the reduction; the above reduction is repeated until irreducible.
9. The method for planning the formation plane flight path of the underwater unmanned aerial vehicle according to claim 1, wherein the third step specifically comprises the following steps:
step 3.1, proposing a suggested speed for the aircraft, pointing the direction to a target point, and ensuring that the linear speed value is in a proper interval;
step 3.2, detecting whether collision risks between the aircrafts and obstacles exist or not by adopting a speed obstacle method, namely a VO algorithm, when sailing at the suggested speed;
3.3, if the navigation has collision risk according to the original speed, correcting the suggested speed by an algorithm based on the principle, wherein the given correction quantity meets the safety firstly, the linear speed variation as small as possible secondly and the course variation as small as possible finally; if the navigation is free of collision risk according to the original speed, the suggested speed planned next step keeps the direction unchanged, and the linear speed value is adjusted to be within an ideal interval through negative feedback.
10. The method for planning the formation plane flight path of the underwater unmanned aerial vehicle according to claim 9, wherein the principle of the VO algorithm is as follows: defining λ (p, v) { p + vt | t ≧ 0} as a ray extending from the end point p in the direction v, defining a relative obstacle(whereinAs the minkowski vector sum) as the center position p of the obstacle BBAs the center of circle, the radius R of the aircraft AAAnd obstacle radius (R)B+dom) Circles with a sum of radii defining the relative velocity vrIs the speed v of the aircraftAWith barrier velocity vBVector difference, namely, making tangent line from central position O of the aircraft to relative obstacle, setting the starting point of relative velocity vector as the position of the aircraft, and when the terminal point of relative velocity vector is positioned between two tangent lines, namelyThere is a risk of collision; translating the two tangent lines along the obstacle velocity vector, and defining the translated two tangent lines and the small-angle sector between the two tangent lines as the speed obstacle area with A opposite to BThus, according to the VO algorithm, when there are multiple moving obstacles, a sufficient condition that there is no risk of collision is that the vehicle speed vector end point is located outside the union of multiple speed obstacle zones.
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| CN115327914A (en) * | 2022-08-24 | 2022-11-11 | 安徽机电职业技术学院 | Robot motion planning method based on artificial gravitational field motion simulation |
| CN115327914B (en) * | 2022-08-24 | 2024-08-16 | 安徽机电职业技术学院 | Robot motion planning method based on artificial gravitational field motion simulation |
| CN115951683A (en) * | 2023-01-29 | 2023-04-11 | 南京理工大学紫金学院 | Artificial potential field path planning method for mixed gradient descent and longicorn whisker search |
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