CN115979267A - Flapping wing aircraft route planning method based on improved RRT algorithm - Google Patents
Flapping wing aircraft route planning method based on improved RRT algorithm Download PDFInfo
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Abstract
The invention discloses a flapping wing aircraft route planning method based on an improved RRT algorithm, which comprises the steps of adjusting a basic RRT random sampling method according to a maximum yaw angle and a minimum step length constraint condition of a flapping wing aircraft, screening out sampling points which do not meet the constraint condition and reducing route planning efficiency, adopting a dynamic step length strategy and a target heuristic strategy based on a target deviation angle to improve sampling efficiency, finally modifying a pruning strategy, providing the pruning strategy based on the maximum yaw angle and fitting and smoothing the planned route through a cubic B-spline curve. Compared with the basic RRT, the improved RRT algorithm provided by the invention not only meets the flight conditions of the flapping-wing aircraft, but also has higher planning efficiency and more continuous and stable routes.
Description
Technical Field
The invention relates to the technical field of flapping wing aircrafts, in particular to a method for planning a flight path of a flapping wing aircraft based on an improved RRT algorithm.
Background
Compared with fixed-wing aircraft and rotor aircraft, the bionic flapping-wing aircraft has the advantages of good concealment, low flight noise, strong portability and the like by using the unique driving principle and mechanism, so that the bionic flapping-wing aircraft has remarkable advantages when performing military reconnaissance tasks. In military reconnaissance activities, in order to realize that the flapping wing aircraft autonomously flies to complete reconnaissance tasks, the route planning is of great importance.
Various algorithms have been proposed for the path planning problem of the traditional robot and mechanical arm, such as a and Dijkstra based on map traversal, RRT, PRM based on probability sampling, and genetic algorithm, ant colony algorithm, etc. based on artificial intelligence algorithm. The rapid expansion Random Tree (RRT) obtains a path by continuously performing Random sampling in a map and contacting with the nearest point of the map until the target point is sampled, and the algorithm has strong randomness due to the planning principle, can realize high concealment when executing a reconnaissance task by matching with an ornithopter, and effectively improves the task completion rate.
In the flight process of the flapping wing aircraft, due to the problems of the minimum turning radius, the maximum pitch angle and the like, the maximum yaw angle constraint condition needs to be considered when the route planning is carried out, and due to the problems of motion inertia, attitude adjustment delay and the like, the minimum step length in the RRT algorithm needs to be constrained, so that the occurrence of operation which cannot be executed is avoided.
The traditional RRT can not meet the maximum yaw angle and minimum step flight constraint conditions of the flapping wing air vehicle, and has the problems of low planning efficiency, tortuous planning path, redundant path and the like. In order to realize the efficient autonomous flight of the flapping wing aircraft to complete military reconnaissance tasks, the flight path planning method for the flapping wing aircraft is designed to meet the flight constraint conditions of the flapping wing aircraft, has certain randomness, can efficiently complete the flight path planning and simplify and smooth the planned flight path, so that the flight path planning method for the flapping wing aircraft is more in line with the flight characteristics of the flapping wing aircraft.
The prior art publication number CN108563243B discloses an unmanned aerial vehicle track planning method based on an improved RRT algorithm, and based on a basic fast extended random tree algorithm frame, turning radius, turning angle and total range constraint conditions are introduced, so that the density of sampling points in a planning space can be effectively reduced, the method can be used for unmanned aerial vehicle fast track planning, and can also provide reference for fast track planning of other aircrafts. The prior art publication number CN109708640a discloses a three-dimensional path planning method for a mobile robot, which improves the constraint conditions of RRT algorithm expansion and the admission criteria of new nodes, realizes three-dimensional path planning of the mobile robot, and has high practical value and popularization value. However, the route planning efficiency is not optimized under the condition that the maximum yaw angle and the minimum step length constraint conditions of the flapping wing air vehicle are met, the military reconnaissance real-time high-efficiency requirement of the flapping wing air vehicle cannot be met, the planned route is not optimized, and the problems of route redundancy, route tortuosity and the like exist.
Disclosure of Invention
1. The technical problem to be solved is as follows:
aiming at the technical problem, the invention provides a route planning method of an ornithopter based on an improved RRT algorithm, which solves the problem of low efficiency of the traditional RRT planning by setting a maximum yaw angle constraint condition and a minimum step length constraint condition of route planning; and the navigation route is simplified and smoothed based on the pruning strategy of the maximum yaw angle and cubic B-spline curve fitting, and the problems that the traditional RRT planning path is tortuous and does not meet the actual flight conditions of the flapping-wing aircraft are solved.
2. The technical scheme is as follows:
a flapping wing air vehicle route planning method based on an improved RRT algorithm is characterized in that: the method comprises the following steps:
the method comprises the following steps: initializing scenes and variables based on an RRT algorithm; the method specifically comprises the following steps: initializing the starting point position of the flight of the aircraft, namely the starting point X of the parameter init Target position, target point X goal Exploration space range, maximum iteration number K, initial step size step, distance threshold Thr and maximumA large yaw angle theta, a minimum step size step _ min and a target heuristic variable i;
step two: judging whether the exploration condition is met; judging whether the current iteration number is greater than the set maximum iteration number K, if so, continuing to execute the step III, and otherwise, ending the iteration and outputting the failure of route planning;
step three: random sampling is carried out according to a target heuristic strategy to obtain a sampling point X rand Finding the neighboring point X of the point near And neighboring point parent node X parent ；
Step four: judging the connecting line X between the sampling point and the adjacent point rand X near Line X connecting adjacent point with father node near X parent Whether the yaw angle gamma between the two points meets the maximum yaw angle theta constraint requirement or not is judged, if yes, the step five is continuously executed, otherwise, the sampling point is abandoned, and the step two is returned;
step five: obtaining the connecting line X between the adjacent point and the sampling point near X rand Line X connecting neighboring point and target point near X goal The included angle alpha between the two nodes is generated through a dynamic step length strategy based on a target deviation angle and a minimum step length constraint new ；
Step six: judgment of X near And X new If the connecting line collides with the barrier, continuing to execute the step seven if the connecting line does not collide with the barrier, otherwise, returning to the step two;
step seven: new node X new Adding to the exploration tree and mixing it with X near Connecting;
step eight: judging new node X new And target point X goal Whether the distance therebetween is less than a distance threshold Thr; if yes, continuing to execute the step nine, otherwise, returning to the step two;
step nine: new node X new And target point X goal Connecting and outputting all the route nodes;
step ten: simplifying the planned route through a pruning strategy based on the maximum yaw angle;
step eleven: and performing smoothing treatment on the pruned airway by cubic B-spline curve fitting and outputting a final airway.
Further, the third step is specifically: judging the target heuristic variable i (0)<i<1) If the value is larger than the preset threshold value, the target point X is directly connected with the target point X goal As a sampling point X rand Otherwise, random sampling is carried out; searching all points in the exploration tree T and finding out a sampling point X rand Nearest neighbor of (X) near (ii) a Then all the points in the exploration tree T are searched to find the nearest adjacent point X near Removing sampling point X rand The outer nearest neighbor near point is used as a father node X parent 。
Further, the fourth step specifically includes:
s41: calculating X rand X near And X near X parent The yaw angle γ therebetween, the following equation:
s42: comparing the yaw angle gamma with the maximum yaw angle theta; if the yaw angle gamma is smaller than the maximum yaw angle theta, executing a fifth step, and expanding according to a dynamic step strategy based on the target yaw angle to obtain a new node X new (ii) a If the yaw angle gamma is within the range of (pi-theta) -pi, the initial step length is changed into (-step) to execute the fifth step so as to reduce the screening rate and improve the sampling efficiency, and if the included angle gamma is within the range of (theta-phi-theta), the sampling point X is subjected to sampling rand Screening and returning to the step two.
Further, the fifth step specifically includes:
s51: calculating a connecting line X between the adjacent point and the sampling point according to the formula (1) near X rand Line X connecting neighboring point and target point near X goal The included angle alpha between the two; generating a step _ adapt based on the target deviation angle according to the size of the included angle alpha, wherein the step _ adapt for calculating the included angle alpha formula and generating the step _ adapt is as follows:
s52: comparing the size of the step _ adapt and the minimum step size step _ min, if step _ adapt > step _ min, the step size adopts the step _ adapt to expand the new node X new And then executing the step six, otherwise, expanding the new node X by adopting the minimum step _ min new And then step six is executed.
Further, the eighth step specifically includes: by judging new node X new And target point X goal Whether the distance between is less than the distance threshold Thr verifies the new node X new And whether or not to reach the target point X goal And if so, executing the ninth step to complete the route planning, otherwise, continuing random sampling, and avoiding the problem that the route planning cannot be quickly completed due to the fact that new nodes and target points rub against each other for many times.
Further, the tenth step specifically includes: from the target point X goal Starting to search route nodes X forward in sequence goal-k Where k =1,2, … …, n and n represents the number of nodes of the airway; and sequentially connecting the route nodes X goal-k And the starting point X init Connecting until one of the route nodes X is obtained i And the starting point X init If the connecting line has not collided, X is recorded i Is a valid node; then from the target point X goal Starting to search route nodes X forward in sequence goal-k And X i Whether collision occurs or not until X is obtained j And X i The connecting line is not collided, and the yaw angle gamma is measured at the moment; if the angle satisfies the maximum yaw angle theta constraint, X is added j And recording the shortest route as an effective node, otherwise, abandoning the node, and continuing to search forwards to finally obtain the shortest route meeting the maximum yaw angle constraint.
Further, the eleventh step specifically includes: the route planned by the RRT algorithm is a multi-section broken line, and in order to meet the requirement of smooth flight of the flapping wing aircraft, smooth transition processing is carried out on the broken line by cubic B spline curve fitting in the step, so that the final route meets the requirements of continuity and smoothness of flight of the flapping wing aircraft.
3. Has the advantages that:
(1) The invention discloses a flapping wing aircraft route planning method based on an improved RRT algorithm, which aims at solving the problems of minimum turning radius, maximum pitch angle and the like in the flight process of a flapping wing aircraft and sets the maximum yaw angle constraint condition of route planning
(2) The dynamic step length strategy based on the target deviation angle is provided, and a target heuristic strategy is adopted, so that the problem of low planning efficiency of the traditional RRT can be effectively solved.
(3) According to the method, the constraint condition of the minimum step length of the route planning is set aiming at the problems of motion inertia, attitude adjustment delay and the like in the flight process of the flapping wing air vehicle.
(4) According to the invention, the route is simplified and smoothed through a pruning strategy based on the maximum yaw angle and cubic B-spline curve fitting, and the problems that the traditional RRT planning path is tortuous and does not meet the actual flight conditions of the flapping-wing aircraft are solved.
In conclusion, the scheme disclosed by the application not only enables the planned route to meet the flight constraint condition of the flapping wing aircraft, but also overcomes the defects of low planning efficiency, tortuous planning path and the like of the traditional RRT algorithm.
Drawings
FIG. 1 is a flow chart of a method for route planning for an ornithopter based on an improved RRT algorithm;
FIG. 2 is a schematic view of the yaw angle of the present invention;
FIG. 3 is a schematic diagram of a target offset angle based dynamic step size strategy in accordance with the present invention;
FIG. 4 is a schematic diagram of a pruning strategy based on maximum yaw angle in the present invention;
FIG. 5 is a simulation diagram of a conventional RRT algorithm in an exemplary embodiment;
FIG. 6 is a simulation of an improved RRT algorithm employing the present invention in a specific embodiment;
FIG. 7 is an initial route simulation before optimization in a specific embodiment;
fig. 8 is a diagram of a route simulation after the optimization processing in step eleven in the specific embodiment.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
As shown in figure 1: a flapping wing aircraft route planning method based on an improved RRT algorithm is characterized in that: the method comprises the following steps:
the method comprises the following steps: initializing scenes and variables based on an RRT algorithm; the method specifically comprises the following steps: initializing a starting point position of flight of an aircraft, namely a parameter starting point X init Target position, target point X goal The method comprises the following steps of searching a space range, the maximum iteration number K, an initial step size and distance threshold Thr, the maximum yaw angle theta, the minimum step size step _ min and a target heuristic variable i.
According to the method, the flight starting point position and the flight ending point position of the aircraft are set according to an actual application scene, the space size of the aircraft capable of flying is defined according to needs to define an exploration space range, the maximum iteration times, the initial step length, the target heuristic variable and the distance threshold are set according to actual planning requirements, and the maximum yaw angle and the minimum step length constraint condition parameters are set according to flight parameters of the aircraft.
Step two: judging whether the exploration condition is met; and judging whether the current iteration times are larger than the set maximum iteration times K, if so, continuing to execute the step three, and if not, finishing the iteration and outputting the failure of route planning.
This step verifies whether the maximum iteration number K is exceeded at present, and checks whether the iteration condition is met.
Step three: random sampling is carried out according to a target heuristic strategy to obtain a sampling point X rand Finding the neighboring point X of the point near And neighboring point parent node X parent . The method specifically comprises the following steps: judging the target heuristic variable i (0)<i<1) If the value is larger than the preset threshold value, the target point X is directly connected with the target point X goal As a sampling point X rand Otherwise, random sampling is carried out; searching all points in the exploration tree T, finding the explorationSample point X rand Nearest neighbor of (X) near (ii) a Then all the points in the exploration tree T are searched to find the nearest neighbor point X near Removing sampling point X rand The outer nearest neighbor near point is used as a father node X parent 。
Randomly sampling by a target heuristic strategy, and determining a sampling mode by verifying the value of a target heuristic variable; due to the constraint of the maximum yaw angle theta of the flapping wing aircraft, if the route planning direction deviates from the target seriously in the early stage, the route planning direction is difficult to adjust to the correct direction step by step only through random sampling, and the sampling trend can be adjusted rapidly through a target heuristic strategy, so that the problem is avoided, and the planning efficiency is improved greatly.
Step four: judging the connecting line X between the sampling point and the adjacent point rand X near Line X connecting adjacent point with father node near X parent And if the included angle gamma meets the constraint requirement of the maximum yaw angle theta, continuing to execute the step five, otherwise, abandoning the sampling point, and returning to the step two.
The fourth step specifically comprises the following steps:
s41: calculating X rand X near And X near X parent The angle γ therebetween is given by the following formula:
s42: comparing the included angle gamma with the maximum yaw angle theta; if the included angle gamma is smaller than the maximum yaw angle theta, executing a fifth step of expansion according to a dynamic step strategy based on the target deviation angle to obtain a new node X new (ii) a If the included angle gamma is in the range of (pi-theta) -pi, the initial step length is changed into (-step) to execute the step five, so that the screening rate is reduced, the sampling efficiency is improved, and if the included angle gamma is in the range of (pi-theta), the sampling point X is subjected to sampling rand Screening and returning to the second step.
As shown in fig. 2, which is a schematic diagram for generating a yaw angle γ, in this step, the yaw angle γ can be obtained through formula (1), whether the included angle γ meets the constraint requirement of the maximum yaw angle θ is determined, and the removal rate of the sampling point is reduced through a reverse expansion mode, so that the sampling efficiency is improved to a certain extent under the constraint condition of the maximum yaw angle.
Step five: obtaining the connecting line X between the adjacent point and the sampling point near X rand Line X connecting neighboring point and target point near X goal The included angle alpha is generated by a dynamic step length strategy based on a target deviation angle and a minimum step length constraint to generate a node X new 。
S51: calculating a connecting line X between the adjacent point and the sampling point according to the formula (1) near X rand Line X connecting neighboring point and target point near X goal The included angle alpha between the two; generating a step _ adapt based on the target deviation angle according to the size of the included angle alpha, wherein the step _ adapt for calculating the included angle alpha formula and generating the step is as follows:
s52: comparing the size of the step _ adapt and the minimum step size step _ min, if step _ adapt > step _ min, the step size adopts the step _ adapt to expand the new node X new And then executing the step six, otherwise, expanding the new node X by adopting the minimum step _ min new And then step six is executed.
As shown in FIG. 3, the step is based on X near X rand And X near X goal The size of the included angle alpha generates the step size step _ adapt of a dynamic step size strategy based on the target deviation angle, and the strategy can lead the sampled point X to be rand Direction inclined target point X goal When the step length is larger than the initial step length, a positive gain expansion effect is formed; on the contrary, at the sampled point X rand Directed away from target point X goal When the step size is smaller than the initial step size, a negative benefit reduction effect is formed.
Step six: judgment of X near And X new Whether the connection is connected or notAnd C, collision is carried out with the barrier, if no collision is carried out, the step seven is continuously executed, and if no collision is carried out, the step two is returned.
This step judges X near And X new Whether the connecting line collides with the barrier or not is avoided, and an invalid air route is planned.
Step seven: new node X new Adding to the exploration tree and mixing it with X near The connection is made.
This step is confirming the new node X new After being a valid route node, the route node is connected with X near Concatenations are made and added to the exploration tree T.
Step eight: judging new node X new And target point X goal Whether the distance therebetween is less than a distance threshold Thr; if yes, the step nine is executed, otherwise, the step two is returned to.
The eighth step specifically comprises: by judging new node X new And target point X goal Whether the distance therebetween is less than the distance threshold Thr verifies the new node X new And whether or not to reach the target point X goal And if so, executing the ninth step to complete the route planning, otherwise, continuing random sampling, and avoiding the problem that the route planning cannot be quickly completed due to the fact that new nodes and target points rub against each other for many times.
This step is carried out by judging the new node X new And target point X goal If the distance between is less than the distance threshold Thr, this step enables the verification of the new node X new Whether or not to reach the target point X goal And if so, executing the ninth step to complete the route planning, otherwise, continuing to sample randomly. In the RRT algorithm, it is almost impossible to sample directly to the end point with pure random sampling, a step which is indispensable.
Step nine: new node X new And target point X goal And connecting and outputting all the route nodes.
After the route planning is finished, all route nodes are output, and preparation is made for subsequent simplification and smoothing processing.
Step ten: planning of a route by a pruning strategy based on a maximum yaw angleCarrying out simplification treatment; from the target point X goal Starting to search route nodes X forward in sequence goal-k Where k =1,2, … …, n and n represents the number of nodes of the airway; and sequentially connecting the route nodes X goal-k And the starting point X init Connecting until one of the route nodes X is obtained i And the starting point X init If the connecting line has not collided, X is recorded i Is a valid node; subsequently from the target point X goal Starting to search route nodes X forward in sequence goal-k And X i Whether collision occurs or not until X is obtained j And X i The connecting line is not collided, and the yaw angle gamma is measured at the moment; if the angle satisfies the maximum yaw angle theta constraint, X is added j And recording the shortest route as an effective node, otherwise, abandoning the node, and continuing to search forwards to finally obtain the shortest route meeting the maximum yaw angle constraint.
In the step, the traditional pruning strategy is modified and optimized, so that the function of simplifying the airway by the traditional pruning strategy can be met, whether the yaw angle gamma meets the maximum yaw angle theta constraint or not is continuously verified in the simplification process, and the airway feasibility is ensured while the airway is simplified.
Step eleven: and smoothing the pruned airway by cubic B-spline curve fitting and outputting a final airway.
And step eleven, specifically, the route planned by the RRT algorithm is a multi-section broken line, in order to meet the requirement of smooth flight of the flapping wing air vehicle, smooth transition processing is carried out on the broken line by cubic B spline curve fitting, so that the final route meets the requirements of continuity and smoothness of flight of the flapping wing air vehicle.
As shown in the attached figure 7, the planned route is a multi-section broken line, in order to meet the requirement of smooth flight of the flapping wing aircraft, smooth transition processing is carried out on the broken line through cubic B spline curve fitting in the step, the final effect is shown as a solid line in the attached figure 8, and the final route meets the requirements of continuity and smoothness of flight of the flapping wing aircraft.
The specific embodiment is as follows:
fig. 4 is a simulation diagram of the route planning of the conventional RRT algorithm, the planned route not only does not satisfy the flight constraint condition of the flapping wing aircraft, but also has low planning efficiency, and fig. 5 is a simulation diagram of the route planning of the improved RRT algorithm of the present invention, which significantly improves the planning efficiency under the condition of satisfying the flight constraint condition of the flapping wing aircraft.
FIG. 5 is a schematic diagram of a maximum yaw angle based pruning strategy by moving from target point X goal Starting to search route nodes X forward in sequence goal-k (k =1,2, … …, n), and in turn route node X goal-k (k =1,2, … …, n) and starting point X init Connecting until obtaining a certain route node X i And the starting point X init If the connecting line has not collided, X is recorded i As valid node, and then from target point X goal Starting to search route nodes X forward in sequence goal-k (k =1,2, … …, n) and X i Whether collision occurs or not until X is obtained j And X i The connecting line is not collided, the yaw angle gamma is measured at the moment, and if the angle meets the maximum yaw angle theta constraint, the X angle is measured j And recording the shortest route as an effective node, otherwise, abandoning the node, and continuing to search forwards to finally obtain the shortest route meeting the maximum yaw angle constraint.
Fig. 7 is an initial route simulation graph before optimization processing, where the route is tortuous and has more redundant route nodes, fig. 8 is a route simulation graph after the optimization processing of step eleven, where solid nodes are route nodes after pruning, broken line broken lines are planned initial routes, dot-dash line broken lines are routes after pruning, and a solid line curve is a final route path after cubic B-spline curve smoothing, and is concise and has smooth continuity.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. A flapping wing aircraft route planning method based on an improved RRT algorithm is characterized in that: the method comprises the following steps:
the method comprises the following steps: initializing a scene and a variable based on an RRT algorithm; the method specifically comprises the following steps: initializing a starting point position of flight of an aircraft, namely a parameter starting point X init The target position is the target point X goal Exploring space range, maximum iteration number K, initial step size and distance threshold Thr, maximum yaw angle theta, minimum step size step _ min and target heuristic variable i;
step two: judging whether the exploration condition is met; judging whether the current iteration number is greater than the set maximum iteration number K, if so, continuing to execute the step III, and otherwise, ending the iteration and outputting the failure of route planning;
step three: random sampling is carried out according to a target heuristic strategy to obtain a sampling point X rand Finding the neighboring point X of the point near And neighboring point parent node X parent ；
Step four: judging the connecting line X between the sampling point and the adjacent point rand X near Line X connecting adjacent point with father node near X parent Whether the yaw angle gamma between the two points meets the maximum yaw angle theta constraint requirement or not is judged, if yes, the step five is continuously executed, otherwise, the sampling point is abandoned, and the step two is returned;
step five: obtaining the connecting line X between the neighboring point and the sampling point near X rand Line X connecting neighboring point and target point near X goal The included angle alpha between the two nodes is generated through a dynamic step length strategy based on a target deviation angle and a minimum step length constraint new ；
Step six: judgment of X near And X new If the connecting line collides with the barrier, continuing to execute the step seven if the connecting line does not collide with the barrier, otherwise, returning to the step two;
step seven: new node X new Adding to the exploration tree and mixing it with X near Connecting;
step eight: judging new node X new And target point X goal Whether the distance therebetween is less than a distance threshold Thr; if yes, the step nine is continuously executed, otherwise, the step two is returned;
step nine: new node X new And target point X goal Connecting and outputting all the route nodes;
step ten: simplifying the planned route through a pruning strategy based on the maximum yaw angle;
step eleven: and smoothing the pruned airway by cubic B-spline curve fitting and outputting a final airway.
2. The ornithopter route planning method based on the improved RRT algorithm, according to claim 1, characterized in that: the third step is specifically as follows: judging the target heuristic variable i (0)<i<1) If the value is larger than the preset threshold value, the target point X is directly connected with the target point X goal As sampling point X rand Otherwise, random sampling is carried out; searching all points in the exploration tree T and finding out a sampling point X rand Nearest neighbor of (X) near (ii) a Then all the points in the exploration tree T are searched to find the nearest adjacent point X near Removing sampling point X rand The outer nearest neighbor near point is taken as a father node X parent 。
3. The ornithopter route planning method based on the improved RRT algorithm, according to claim 2, characterized in that: the fourth step specifically comprises:
s41: calculating X rand X near And X near X parent The yaw angle γ therebetween, the following equation:
s42: comparing the yaw angle gamma with the maximum yaw angle theta; if the yaw angle gamma is smaller than the maximum yaw angle theta, executing a fifth step, and expanding according to a dynamic step strategy based on the target yaw angle to obtain a new node X new (ii) a If the yaw angle gamma is in the range of (pi-theta) -pi, the initial step length is changed into (-step) to execute the fifth step so as to reduce the screening rate,the sampling efficiency is improved, and if the included angle gamma is in the range of theta to phi-theta, the sampling point X is sampled rand Screening and returning to the step two.
4. The ornithopter route planning method based on the improved RRT algorithm, according to claim 3, wherein: the fifth step specifically comprises:
s51: calculating a connecting line X between the adjacent point and the sampling point according to a formula (1) near X rand Line X connecting neighboring point and target point near X goal The included angle alpha between the two; generating a step _ adapt based on the target deviation angle according to the size of the included angle alpha, wherein the step _ adapt for calculating the included angle alpha formula and generating the step _ adapt is as follows:
s52: comparing the size of the step _ adapt and the minimum step size step _ min, if step _ adapt > step _ min, the step size adopts the step _ adapt to expand the new node X new And then executing the step six, otherwise, expanding the new node X by adopting the minimum step _ min new And then step six is executed.
5. The ornithopter routing method based on the improved RRT algorithm as claimed in claim 4, wherein: the eighth step specifically comprises: by judging new node X new And target point X goal Whether the distance between is less than the distance threshold Thr verifies the new node X new And whether or not to reach the target point X goal And if so, executing the ninth step to complete the route planning, otherwise, continuing to randomly sample, and avoiding the problem that the route planning cannot be quickly completed due to repeated shoulder rubbing of a new node and a target point.
6. The ornithopter route planning method based on the improved RRT algorithm as claimed in claim 5, wherein: the method comprises the following specific steps: from the target point X goal Starting to search route nodes X forward in sequence goal-k Where k =1,2, … …, n and n represents the number of nodes of the airway; and sequentially connecting the route nodes X goal-k And the starting point X init Connecting until one of the route nodes X is obtained i And the starting point X init If the connecting line has not collided, X is recorded i Is a valid node; subsequently from the target point X goal Sequentially searching route nodes X forwards goal-k And X i Whether collision occurs or not until X is obtained j And X i The connecting line is not collided, and the yaw angle gamma is measured at the moment; if the angle satisfies the maximum yaw angle theta constraint, X is added j And recording the shortest route as an effective node, otherwise, abandoning the node, and continuing to search forwards to finally obtain the shortest route meeting the maximum yaw angle constraint.
7. The ornithopter routing method based on the improved RRT algorithm as claimed in claim 6, wherein: the eleventh step specifically comprises: the route planned by the RRT algorithm is a multi-section broken line, and in order to meet the requirement of smooth flight of the flapping wing aircraft, smooth transition processing is carried out on the broken line by cubic B spline curve fitting in the step, so that the final route meets the requirements of continuity and smoothness of flight of the flapping wing aircraft.
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| CN116295443A (en) * | 2023-05-16 | 2023-06-23 | 北京科技大学 | Path planning method, device, equipment and medium of hinge type unmanned mining equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116295443A (en) * | 2023-05-16 | 2023-06-23 | 北京科技大学 | Path planning method, device, equipment and medium of hinge type unmanned mining equipment |
| CN116295443B (en) * | 2023-05-16 | 2023-08-04 | 北京科技大学 | Path planning method, device, equipment and medium of hinge type unmanned mining equipment |
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