CN113804208B - Unmanned vehicle path optimization method and related equipment - Google Patents
Unmanned vehicle path optimization method and related equipment Download PDFInfo
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The embodiment of the disclosure provides an unmanned vehicle path optimization method and device, a computer-readable storage medium and electronic equipment, and belongs to the technical field of computers and communication. The method comprises the following steps: acquiring an initial path generated by the unmanned vehicle; acquiring obstacles around the initial path; if the initial path intersects the obstacle, making a decision on the obstacle not to detour; if the initial path does not intersect the obstacle, the decision on the obstacle is left-hand or right-hand; and optimizing the initial path of the drone based on the decision of the obstacle. The technical scheme of the embodiment of the disclosure provides an unmanned vehicle path optimization method, which can avoid the problem that the detour behavior of the smooth path and the path before the smooth path on the obstacle is not uniform.
Description
Technical Field
The disclosure relates to the technical field of computers and communications, in particular to a method and a device for optimizing a path of an unmanned vehicle, a computer readable storage medium and electronic equipment.
Background
The current mobile robot technology is developed rapidly, and various mobile robots are layered endlessly along with the continuous expansion of the application scenes and modes of robots in recent years, so that an unmanned vehicle is one member. Currently, unmanned vehicle path planning generally obtains an optimal path in an evaluation system based on the evaluation system, and then directly uses the path for speed planning, and does not give specific lateral decisions (i.e. left-winding, right-winding or no-winding) to obstacles around the path. If the optimal path obtained based on the set evaluation system is not smooth enough, a subsequent smoothing process is required. Since the obstacles around the path have no transverse decision, the path after smoothing is possibly not uniform with the detour behavior of the path before smoothing on the obstacle, and the unmanned vehicle cannot detour the obstacle.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the disclosure provides an unmanned vehicle path optimization method and device, a computer-readable storage medium and electronic equipment, which can avoid the problem that the detour behavior of a smooth path and a path before the smooth path on an obstacle is not uniform.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided an unmanned vehicle path optimization method, including:
acquiring an initial path generated by the unmanned vehicle, wherein the initial path comprises a plurality of discrete path points;
Acquiring obstacles around the initial path;
If the initial path intersects the obstacle, making a decision on the obstacle not to detour;
if the initial path does not intersect the obstacle, then:
among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring;
Among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the right side of a connecting line between the first path points and the center point of the obstacle, the decision of the obstacle is to bypass right; and
Optimizing the initial path of the drone based on the decision of the obstacle.
In one embodiment, further comprising:
determining a first vector connecting the first waypoint and the obstacle center point and a second vector connecting the first waypoint and the second waypoint according to the Cartesian coordinates of the first waypoint, the second waypoint and the obstacle center point;
Among the discrete path points in the initial path, if there are two adjacent first path points and second path points close to the obstacle, so that the second path point is located at the left side of the connection line between the first path point and the center point of the obstacle, the decision on the obstacle is to bypass left, including:
When the value of the cross multiplication of the first vector and the second vector is larger than zero, the second path point is positioned at the left side of the connecting line of the first path point and the center point of the obstacle, and the decision of the obstacle is left-hand detouring;
Wherein, in the discrete path points in the initial path, if there are two adjacent first path points and second path points close to the obstacle, so that the second path point is located on the right side of the connection line between the first path point and the center point of the obstacle, the decision on the obstacle is to detour to the right includes:
When the value of the cross multiplication of the first vector and the second vector is smaller than zero, the second path point is positioned on the right side of the connecting line of the first path point and the center point of the obstacle, and the decision of the obstacle is to bypass right.
In one embodiment, the obstacle comprises an obstacle boundary polygon comprising a start flena S coordinate and an end flena S coordinate, the start flena S coordinate of the obstacle boundary polygon being a minimum of the flena S coordinates in the end coordinates of the obstacle boundary polygon, the end flena S coordinate of the obstacle boundary polygon being a maximum of the flena S coordinates in the end coordinates of the obstacle boundary polygon, wherein if there are two adjacent first and second path points near the obstacle comprises:
if the flena S coordinates of the second waypoint are greater than the start flena S coordinates of the obstacle boundary polygon and the flena S coordinates of the first waypoint are less than the end flena S coordinates of the obstacle boundary polygon, then there are two adjacent first waypoints and second waypoints that are proximate to the obstacle.
In one embodiment, the initial path includes a start flena S coordinate and a stop flena S coordinate, wherein acquiring the obstacle around the initial path includes:
Comparing the start flena S coordinates and the end flena S coordinates of the obstacle boundary polygon with the start flena S coordinates and the end flena S coordinates of the initial path;
And acquiring the obstacle boundary polygon of which the end flena S coordinate of the obstacle boundary polygon is larger than the initial flena S coordinate of the initial path and the initial flena S coordinate of the obstacle boundary polygon is smaller than the end flena S coordinate of the initial path.
In one embodiment, the obstacle comprises an obstacle boundary polygon, wherein if the initial path intersects the obstacle comprises:
if the initial path intersects the obstacle or the obstacle-boundary polygon.
In one embodiment, if the initial path intersects the obstacle or the obstacle-boundary polygon comprises:
If there are two adjacent first and second path points near the obstacle, among the discrete path points in the initial path, a line connecting the first and second path points intersects the obstacle or the obstacle boundary polygon.
In one embodiment, the obstacle center point is the center point of the obstacle or the center point of the obstacle boundary polygon,
Wherein locating the second waypoint to the left of the first waypoint to the obstacle center point line comprises:
such that the second path point is to the left of a line connecting the first path point with the obstacle center point or the center point of the obstacle boundary polygon;
wherein locating the second waypoint to the right of the first waypoint to the obstacle center point line comprises:
such that the second path point is located to the right of a line connecting the first path point with the obstacle center point or the center point of the obstacle boundary polygon.
According to one aspect of the present disclosure, there is provided an unmanned vehicle path optimizing apparatus including:
an acquisition module configured to acquire an initial path that the unmanned vehicle has generated and an obstacle around the initial path, wherein the initial path includes a plurality of discrete path points; and
A decision module configured to decide not to detour on the obstacle if the initial path intersects the obstacle;
if the initial path does not intersect the obstacle, then:
among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring;
Among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the right side of a connecting line between the first path points and the center point of the obstacle, the decision of the obstacle is to bypass right; and
An optimization module configured to optimize the initial path of the drone based on the decision of the obstacle.
According to one aspect of the present disclosure, there is provided an electronic device including:
one or more processors;
a storage device configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of any of the above embodiments.
According to one aspect of the present disclosure, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements a method according to any one of the above embodiments.
In the technical solutions provided in some embodiments of the present disclosure, according to a decision method for acquiring an obstacle around an initial path generated by an unmanned vehicle according to the initial path, a problem that a path after smoothing the initial path may not be uniform with a path before smoothing in bypassing the obstacle, so that the unmanned vehicle cannot bypass the obstacle may be avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The following figures depict certain illustrative embodiments of the invention, in which like reference numerals refer to like elements. These described embodiments are to be considered in all respects as illustrative and not restrictive.
FIG. 1 illustrates a schematic diagram of an exemplary system architecture to which an unmanned vehicle path optimization method or an unmanned vehicle path optimization apparatus of embodiments of the present disclosure may be applied;
FIG. 2 illustrates a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure;
FIG. 3 schematically illustrates a Frenet coordinate system and a Cartesian coordinate system according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a schematic view of an obstacle and obstacle boundary polygons in Frenet and Cartesian coordinate systems according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of a method of unmanned vehicle path optimization in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flow chart of an unmanned vehicle obstacle decision method according to an embodiment of the disclosure;
FIG. 7 schematically illustrates a block diagram of an unmanned vehicle path optimization apparatus according to an embodiment of the present disclosure;
FIG. 8 schematically illustrates a block diagram of an unmanned vehicle path optimization apparatus according to another embodiment of the present invention;
fig. 9 schematically shows a block diagram of an unmanned vehicle path optimizing apparatus according to another embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 shows a schematic diagram of an exemplary system architecture 100 to which an unmanned vehicle path optimization method or an unmanned vehicle path optimization apparatus of embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 105 may be a server cluster formed by a plurality of servers.
The drone may use the terminal devices 101, 102, 103 to interact with the server 105 over the network 104 to receive or send messages, etc. The terminal devices 101, 102, 103 may be a variety of electronic devices with display screens including, but not limited to, smartphones, tablet computers, laptop and desktop computers, digital cinema projectors, and the like.
The server 105 may be a server providing various services. For example, the drone transmits a request for optimizing the path of the drone to the server 105 using the terminal device 103 (may be the terminal device 101 or 102). The server 105 may obtain an initial path that the drone has generated, wherein the initial path includes a plurality of discrete path points; acquiring obstacles around the initial path; if the initial path intersects the obstacle, making a decision on the obstacle not to detour; if the initial path does not intersect the obstacle, then: among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring; among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the right side of a connecting line between the first path points and the center point of the obstacle, the decision of the obstacle is to bypass right; and optimizing the initial path of the drone based on the decision of the obstacle. The server 105 may send the optimized path information to the terminal device 103 to display the optimized path information on the terminal device 103, and the drone may view the optimized path of the corresponding current drone based on the content displayed on the terminal device 103.
As another example, the terminal device 103 (may also be the terminal device 101 or 102) may be a smart tv, a VR (Virtual Reality)/AR (Augmented Reality ) head-mounted display, or a mobile terminal such as a smart phone, a tablet computer, etc. on which a navigation, a network about car, an instant messaging, a video Application (APP), etc. are installed, and the unmanned vehicle may send a request for route optimization of the unmanned vehicle to the server 105 through the smart tv, the VR/AR head-mounted display, or the navigation, the network about car, the instant messaging, the video APP. The server 105 may obtain the result of the unmanned vehicle path optimization based on the unmanned vehicle path optimization request, and return the result of the unmanned vehicle path optimization to the smart television, the VR/AR head-mounted display or the navigation, the network vehicle, the instant messaging, the video APP, and further display the returned result of the unmanned vehicle path optimization through the smart television, the VR/AR head-mounted display or the navigation, the network vehicle, the instant messaging, the video APP.
Fig. 2 shows a schematic diagram of a computer system suitable for use in implementing embodiments of the present disclosure.
It should be noted that, the computer system 200 of the electronic device shown in fig. 2 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present disclosure.
As shown in fig. 2, the computer system 200 includes a central processing unit (CPU, central Processing Unit) 201 that can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 202 or a program loaded from a storage portion 208 into a random access Memory (RAM, random Access Memory) 203. In the RAM 203, various programs and data required for the system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other through a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
The following components are connected to the I/O interface 205: an input section 206 including a keyboard, a mouse, and the like; an output section 207 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 208 including a hard disk or the like; and a communication section 209 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 209 performs communication processing via a network such as the internet. The drive 210 is also connected to the I/O interface 205 as needed. A removable medium 211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 210 as needed, so that a computer program read therefrom is installed into the storage section 208 as needed.
In particular, according to embodiments of the present disclosure, the processes described below with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 209, and/or installed from the removable medium 211. The computer program, when executed by a Central Processing Unit (CPU) 201, performs the various functions defined in the method and/or apparatus of the present application.
It should be noted that the computer readable storage medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM (Erasable Programmable Read Only Memory, erasable programmable read-only memory) or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF (Radio Frequency), and the like, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules and/or units and/or sub-units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware, and the described modules and/or units and/or sub-units may also be provided in a processor. Wherein the names of the modules and/or units and/or sub-units do not in some cases constitute a limitation of the modules and/or units and/or sub-units themselves.
As another aspect, the present application also provides a computer-readable storage medium that may be included in the electronic device described in the above embodiment modes; or may exist alone without being incorporated into the electronic device. The computer-readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 5 or fig. 6.
In the related art, for example, a machine learning method, a deep learning method and the like can be adopted to optimize the unmanned vehicle path, and the application ranges of different methods are different.
Fig. 3 schematically illustrates a Frenet coordinate system and a cartesian coordinate system according to an embodiment of the present disclosure.
Referring to fig. 3, the cartesian coordinate system is the yMx coordinate system. The Frenet coordinate system takes the center line of the road as an S axis and takes the vertical S axis to the left as an L axis, and the center line of the road consists of a series of discrete points. Assuming that there is a point p (x p,yp) in the cartesian coordinate system, two discrete points s (x s,ys) and e (x e,ye) closest to p are found in the road centerline, and assuming that s is (s s, 0) in the Frenet coordinate system and e is (s e, 0) in the Frenet coordinate system, the relationship between the point p (x p,yp) in the cartesian coordinate system and its coordinate (s p,lp) in the Frenet coordinate system is determined by the formula (1):
Fig. 4 schematically illustrates a schematic view of an obstacle (obstacle) 401 and obstacle boundary polygon 402 in a Frenet (flener) coordinate system and a cartesian coordinate system according to an embodiment of the disclosure.
Referring to fig. 4, a solid line polygon 401 is a schematic view of an obstacle, and a broken line polygon 402 is a schematic view of an obstacle boundary polygon. In fig. 4, the obstacle 401 and the obstacle boundary polygon 402 are both quadrangles, but the present disclosure is not limited thereto, and the obstacle boundary polygon may be other shapes than quadrangles.
The unmanned vehicle path is a set formed by a series of discrete points, whether based on a Frenet coordinate system or a Cartesian coordinate system, and the unmanned vehicle planned path can be represented by a point set N= { p i(xi,yi,si,li) |=i1, 2,..m, wherein m represents the number of the discrete points in the path, (x i,yi) represents the Cartesian coordinate of the ith point, and (s i,li) represents the coordinate under the ith point Frenet.
Referring to fig. 4, the obstacle polygon 402 is formed by connecting a series of vertices in a certain order, and in combination with the conversion relation between the Frenet coordinate system and the cartesian coordinate system, assuming that the obstacle boundary polygon 402 is represented by (s start,send,lstart,lend), the boundary value of the obstacle boundary polygon may be determined according to the formula (2):
wherein (s i,li) in the formula (2) represents the coordinates of the i-th point in the obstacle vertex in the Frenet coordinate system.
Fig. 5 schematically illustrates a flow chart of a method of unmanned vehicle path optimization in accordance with an embodiment of the present disclosure. The method steps of the embodiments of the present disclosure may be performed by the terminal device, by the server, or by both the terminal device and the server, for example, by the server 105 in fig. 1, but the present disclosure is not limited thereto.
In step S510, an initial path that has been generated by the drone is obtained, wherein the initial path includes a plurality of discrete path points.
In this step, the terminal device or the server may obtain an initial path that has been generated by the unmanned vehicle, where the initial path includes a plurality of discrete path points, i.e., the initial path is composed of a plurality of discrete path points, and may be represented by, for example, a point set n= { p i(xi,yi,si,li) |i=1, 2.
In the embodiments of the present disclosure, the terminal device may be implemented in various forms. For example, the terminals described in the present disclosure may include mobile terminals such as a mobile phone, a tablet computer, a notebook computer, a palm computer, a Personal Digital Assistant (PDA), a portable media player (portable MEDIA PLAYER, PMP), a drone path optimization device, a wearable device, a smart bracelet, a pedometer, a robot, a drone, and fixed terminals such as a digital TV (television), a desktop computer, and the like.
In step S520, an obstacle around the initial path is acquired.
In this step, the terminal device or the server may acquire an obstacle around the initial path. In one embodiment, the obstacle comprises an obstacle boundary polygon comprising a start flena S coordinate and an end flena S coordinate, the start flena S coordinate of the obstacle may be the start flena S coordinate of the obstacle boundary polygon, the end flena S coordinate of the obstacle may be the end flena S coordinate of the obstacle boundary polygon, the initial path comprises the start flena S coordinate and the end flena S coordinate, wherein acquiring the obstacle around the initial path comprises: comparing the start flena S coordinates and the end flena S coordinates of the obstacle boundary polygon with the start flena S coordinates and the end flena S coordinates of the initial path; and acquiring the obstacle or the obstacle boundary polygon of which the final flena S coordinate of the obstacle boundary polygon is larger than the initial flena S coordinate of the initial path and the initial flena S coordinate of the obstacle boundary polygon is smaller than the final flena S coordinate of the initial path, namely acquiring the obstacle or the obstacle boundary polygon positioned between the starting point and the end point of the initial path.
In step S530, if the initial path intersects the obstacle, the decision on the obstacle is not to detour.
In this step, the terminal device or the server judges that: if the initial path intersects the obstacle, then the decision for the obstacle is not to detour. In one embodiment, the obstacle comprises an obstacle boundary polygon, wherein if the initial path intersects the obstacle comprises: if the initial path intersects the obstacle or the obstacle-boundary polygon. In one embodiment, if the initial path intersects the obstacle or the obstacle-boundary polygon comprises: if there are two adjacent first and second path points near the obstacle, among the discrete path points in the initial path, a line connecting the first and second path points intersects the obstacle or the obstacle boundary polygon. In one embodiment, the obstacle comprises an obstacle boundary polygon comprising a start flena S coordinate and an end flena S coordinate, the start flena S coordinate of the obstacle boundary polygon being a minimum of the flena S coordinates in the end coordinates of the obstacle boundary polygon, the end flena S coordinate of the obstacle boundary polygon being a maximum of the flena S coordinates in the end coordinates of the obstacle boundary polygon, wherein if there are two adjacent first and second path points near the obstacle comprises: if the flena S coordinates of the second waypoint are greater than the start flena S coordinates of the obstacle boundary polygon and the flena S coordinates of the first waypoint are less than the end flena S coordinates of the obstacle boundary polygon, then there are two adjacent first waypoints and second waypoints that are proximate to the obstacle.
In step S540, if the initial path does not intersect the obstacle, then: among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring; among the discrete path points in the initial path, if there are two adjacent first and second path points near the obstacle such that the second path point is located on the right side of the line connecting the first path point and the obstacle center point, then the decision on the obstacle is to detour to the right.
In this step, if the initial path does not intersect with the obstacle, the terminal device or the server determines a decision of the obstacle according to discrete path points in the initial path. In one embodiment, the obstacle center point may be a center point of the obstacle or may be a center point of the obstacle boundary polygon, wherein positioning the second path point on the left side of the line connecting the first path point and the obstacle center point includes: such that the second path point is to the left of a line connecting the first path point with the obstacle center point or the center point of the obstacle boundary polygon; wherein locating the second waypoint to the right of the first waypoint to the obstacle center point line comprises: such that the second path point is located to the right of a line connecting the first path point with the obstacle center point or the center point of the obstacle boundary polygon. In one embodiment, the obstacle center point may be the obstacle or the obstacle boundary polygon geometric center of gravity point.
In one embodiment, a first vector connecting the first waypoint and the obstacle center point and a second vector connecting the first waypoint and the second waypoint are determined from the cartesian coordinates of the first waypoint, the second waypoint and the obstacle center point; among the discrete path points in the initial path, if there are two adjacent first path points and second path points close to the obstacle, so that the second path point is located at the left side of the connection line between the first path point and the center point of the obstacle, the decision on the obstacle is to bypass left, including: when the value of the cross multiplication of the first vector and the second vector is larger than zero, the second path point is positioned at the left side of the connecting line of the first path point and the center point of the obstacle, and the decision of the obstacle is left-hand detouring; wherein, in the discrete path points in the initial path, if there are two adjacent first path points and second path points close to the obstacle, so that the second path point is located on the right side of the connection line between the first path point and the center point of the obstacle, the decision on the obstacle is to detour to the right includes: when the value of the cross multiplication of the first vector and the second vector is smaller than zero, the second path point is positioned on the right side of the connecting line of the first path point and the center point of the obstacle, and the decision of the obstacle is to bypass right.
In step S550, the initial path of the drone is optimized based on the decision of the obstacle.
In this step, the terminal device or the server may be used for further smoothing of the initial path or the like optimization, or for speed planning of the initial path or the like, according to the decision (not detouring, left detouring or right detouring) of the obstacle obtained in the previous step. In the further smooth optimization of the initial path, firstly, a solution space required by an optimizer is required to be solved finely according to decision information of existing obstacles around the unmanned vehicle, vehicle driving direction and road boundary information, then the initial path is taken as an initial value in the solution space, and the path is optimized through the optimizers such as IPOPT, OSQP and the like, so that a smooth, comfortable, safe and feasible new path is obtained.
According to the method for optimizing the path of the unmanned vehicle, which is disclosed by the invention, the decision method for acquiring the obstacles around the initial path according to the generated initial path of the unmanned vehicle can be used for avoiding the problem that the unmanned vehicle cannot bypass the obstacles due to the fact that the path after the initial path is smoothed is possibly inconsistent with the bypass behavior of the path before the path is smoothed.
Fig. 6 schematically illustrates a flow chart of an unmanned vehicle obstacle decision method according to an embodiment of the disclosure.
In one embodiment, a first vector connecting the first waypoint and the obstacle center point and a second vector connecting the first waypoint and the second waypoint are determined from the cartesian coordinates of the first waypoint, the second waypoint and the obstacle center point.
In step S541, when the value of the cross multiplication of the first vector and the second vector is greater than zero, the second path point is located at the left side of the connection line between the first path point and the center point of the obstacle, and the decision on the obstacle is to bypass to the left.
In this step, if there are two adjacent first and second path points near the obstacle among the discrete path points in the initial path such that the value of the first vector and the second vector cross-product is greater than zero, the second path point is located on the left side of the line connecting the first path point and the obstacle center point, and the decision on the obstacle is to detour to the left.
In step S542, when the value of the cross multiplication of the first vector and the second vector is less than zero, the second path point is located on the right side of the line connecting the first path point and the center point of the obstacle, and the decision on the obstacle is to bypass to the right.
In this step, if there are two adjacent first and second path points near the obstacle in the discrete path points in the initial path, such that the value of the first vector and the second vector cross-product is less than zero, the second path point is located on the right side of the line connecting the first path point and the center point of the obstacle, and the decision on the obstacle is to detour to the right.
In one embodiment, the decision algorithm flow based on the generated nearby obstructions of the initial path is as follows:
traversing all obstacles b j from the sensing module, storing all obstacles that do not meet the b j.sstart＞pm.s||bj.send＜p1 s condition in a container v= { b j |j=1, 2., k; wherein b j.sstart＞pm.s||bj.send＜p1 S represents the obstacle with a final flena S coordinate less than the initial flena S coordinate of the initial path or a final flena S coordinate greater than the initial path.
Traversing all obstacles b j in container V:
Initializing a bool flag=false, and setting a b j default detour state flag direction=left, namely left detour;
traversing all path points p i:
If p i+1.s＜bj.sstart, continuous; if p i.s＞bj.send, break; wherein, p i+1.s＜bj.sstart represents that when the flena S coordinate of the path point of the initial path is smaller than the flena S coordinate of the obstacle start; p i.s＞bj.send denotes that when the flena S coordinate of the path point of the initial path is greater than the obstacle-terminated flena S coordinate;
Let p ixy＝(pi.x,pi.y),pi+1xy＝(pi+1.x,pi+1. Y);
If the line segment p ixypi+1xy (the line segment where point i connects with point i+1) collides with a polygonal obstacle (i.e., the line segment p ixpy+1i intersects with the obstacle or the boundary polygon of the obstacle), then let direction=cross, i.e., not wind, break; otherwise, if the flag is false, taking the center point a (x a,ya) of the obstacle or obstacle boundary polygon, constructing a vector (First vector) and/>(Second vector) if/>If yes, enabling direction=left and flag=true, otherwise enabling direction=right and flag=true;
If direction is left, the obstacle b j is left-hand, if direction is right, the obstacle b j is right-hand, otherwise, the obstacle b j is left-hand.
Fig. 7 schematically illustrates a block diagram of an unmanned vehicle path optimization apparatus according to an embodiment of the present disclosure. The unmanned vehicle path optimizing apparatus 700 provided in the embodiment of the present disclosure may be provided on a terminal device, or may be provided on a server side, for example, may be provided on the server 105 in fig. 1, but the present disclosure is not limited thereto.
The unmanned vehicle path optimizing apparatus 700 provided in the embodiment of the present disclosure may include an obtaining module 710, a determining module 720, and an optimizing module 730.
Wherein the obtaining module 710 is configured to obtain an initial path that the drone has generated and an obstacle around the initial path, wherein the initial path includes a plurality of discrete path points; and the decision module 720 is configured to decide not to detour on the obstacle if the initial path intersects the obstacle; if the initial path does not intersect the obstacle, then: among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring; among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the right side of a connecting line between the first path points and the center point of the obstacle, the decision of the obstacle is to bypass right; and an optimization module 730 configured to optimize the initial path of the drone based on the decision of the obstacle.
The unmanned vehicle path optimizing device 700 can obtain the decision method of the obstacles around the initial path according to the initial path generated by the unmanned vehicle, and can avoid the problem that the unmanned vehicle cannot bypass the obstacles due to the fact that the path after smoothing the initial path is possibly not uniform with the bypassing behavior of the path before smoothing on the obstacles.
According to embodiments of the present disclosure, the above-described unmanned vehicle path optimization apparatus 700 may be used to implement the unmanned vehicle path optimization method and the unmanned vehicle obstacle decision method described in the embodiments of fig. 5 and 6.
Fig. 8 schematically shows a block diagram of an unmanned vehicle path optimizing apparatus 800 according to another embodiment of the invention.
As shown in fig. 8, the unmanned vehicle path optimizing apparatus 800 further includes a display module 810 in addition to the acquisition module 710, the judgment module 720, and the optimization module 730 described in the embodiment of fig. 7.
Specifically, the display module 810 displays the obstacle decision result and the path optimization result on the terminal after the decision module 720 makes the obstacle decision and the optimization module 730 optimizes the initial path.
In the unmanned vehicle path optimizing device 800, visual display of the obstacle decision result and the path optimizing result can be completed through the display module 810.
Fig. 9 schematically shows a block diagram of an unmanned vehicle path optimizing apparatus 900 according to another embodiment of the present invention.
As shown in fig. 9, the unmanned vehicle path optimizing apparatus 900 further includes a storage module 910 in addition to the acquisition module 710, the judgment module 720, and the optimization module 730 described in the embodiment of fig. 7.
Specifically, the storage module 910 is configured to store the obstacle decision result and the path optimization result, so as to facilitate subsequent calling and referencing.
It is understood that the acquisition module 710, the judgment module 720, the optimization module 730, the display module 810, and the storage module 910 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. At least one of the acquisition module 710, the determination module 720, the optimization module 730, the display module 810, and the storage module 910 may be implemented, at least in part, as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or any other reasonable manner of integrating or packaging circuitry, or in hardware or firmware, or in a suitable combination of software, hardware, and firmware implementations, in accordance with embodiments of the present invention. Or at least one of the acquisition module 710, the judgment module 720, the optimization module 730, the display module 810, and the storage module 910 may be at least partially implemented as computer program modules, which when executed by a computer, may perform the functions of the respective modules.
Since each module of the unmanned vehicle path optimizing apparatus according to the exemplary embodiment of the present invention may be used to implement the steps of the exemplary embodiment of the unmanned vehicle path optimizing method described in fig. 5 and 6, for details not disclosed in the embodiment of the apparatus of the present invention, please refer to the embodiment of the unmanned vehicle path optimizing method described in the present invention.
The specific implementation of each module, unit and subunit in the unmanned vehicle path optimization device provided in the embodiment of the present disclosure may refer to the content in the above unmanned vehicle path optimization method, which is not described herein again.
It should be noted that although in the above detailed description several modules, units and sub-units of the apparatus for action execution are mentioned, this division is not mandatory. Indeed, the features and functions of two or more modules, units, and sub-units described above may be embodied in one module, unit, and sub-unit, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module, unit, and sub-unit described above may be further divided into ones that are embodied by a plurality of modules, units, and sub-units.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for optimizing a path of an unmanned vehicle, comprising:
acquiring an initial path generated by the unmanned vehicle, wherein the initial path comprises a plurality of discrete path points;
Acquiring obstacles around the initial path;
If the initial path intersects the obstacle, making a decision on the obstacle not to detour;
if the initial path does not intersect the obstacle, then:
among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring;
Among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the right side of a connecting line between the first path points and the center point of the obstacle, the decision of the obstacle is to bypass right; and
Optimizing the initial path of the drone based on the decision of the obstacle;
Wherein the second path point is located behind the first path point in the extending direction of the initial path.
2. The method as recited in claim 1, further comprising:
determining a first vector connecting the first waypoint and the obstacle center point and a second vector connecting the first waypoint and the second waypoint according to the Cartesian coordinates of the first waypoint, the second waypoint and the obstacle center point;
Among the discrete path points in the initial path, if there are two adjacent first path points and second path points close to the obstacle, so that the second path point is located at the left side of the connection line between the first path point and the center point of the obstacle, the decision on the obstacle is to bypass left, including:
When the value of the cross multiplication of the first vector and the second vector is larger than zero, the second path point is positioned at the left side of the connecting line of the first path point and the center point of the obstacle, and the decision of the obstacle is left-hand detouring;
Wherein, in the discrete path points in the initial path, if there are two adjacent first path points and second path points close to the obstacle, so that the second path point is located on the right side of the connection line between the first path point and the center point of the obstacle, the decision on the obstacle is to detour to the right includes:
When the value of the cross multiplication of the first vector and the second vector is smaller than zero, the second path point is positioned on the right side of the connecting line of the first path point and the center point of the obstacle, and the decision of the obstacle is to bypass right.
3. The method of claim 1, wherein the obstacle comprises an obstacle boundary polygon including a start flena S coordinate and an end flena S coordinate, the start flena S coordinate of the obstacle boundary polygon being a minimum of the flena S coordinates in the end coordinates of the obstacle boundary polygon, the end flena S coordinate of the obstacle boundary polygon being a maximum of the flena S coordinates in the end coordinates of the obstacle boundary polygon, wherein if there are two adjacent first and second path points near the obstacle comprises:
if the flena S coordinates of the second waypoint are greater than the start flena S coordinates of the obstacle boundary polygon and the flena S coordinates of the first waypoint are less than the end flena S coordinates of the obstacle boundary polygon, then there are two adjacent first waypoints and second waypoints that are proximate to the obstacle.
4. The method of claim 3, wherein the initial path comprises a start flena S coordinate and a stop flena S coordinate, wherein acquiring the obstacle around the initial path comprises:
Comparing the start flena S coordinates and the end flena S coordinates of the obstacle boundary polygon with the start flena S coordinates and the end flena S coordinates of the initial path;
And acquiring the obstacle boundary polygon of which the end flena S coordinate of the obstacle boundary polygon is larger than the initial flena S coordinate of the initial path and the initial flena S coordinate of the obstacle boundary polygon is smaller than the end flena S coordinate of the initial path.
5. The method of claim 1, wherein the obstacle comprises an obstacle boundary polygon, wherein if the initial path intersects the obstacle comprises:
if the initial path intersects the obstacle or the obstacle-boundary polygon.
6. The method of claim 5, wherein if the initial path intersects the obstacle or the obstacle-boundary polygon comprises:
If there are two adjacent first and second path points near the obstacle, among the discrete path points in the initial path, a line connecting the first and second path points intersects the obstacle or the obstacle boundary polygon.
7. The method of claim 1, wherein the obstacle center point is a center point of the obstacle or a center point of the obstacle boundary polygon,
Wherein locating the second waypoint to the left of the first waypoint to the obstacle center point line comprises:
such that the second path point is to the left of a line connecting the first path point with the obstacle center point or the center point of the obstacle boundary polygon;
wherein locating the second waypoint to the right of the first waypoint to the obstacle center point line comprises:
such that the second path point is located to the right of a line connecting the first path point with the obstacle center point or the center point of the obstacle boundary polygon.
8. An unmanned vehicle path optimizing apparatus, comprising:
an acquisition module configured to acquire an initial path that the unmanned vehicle has generated and an obstacle around the initial path, wherein the initial path includes a plurality of discrete path points; and
A decision module configured to decide not to detour on the obstacle if the initial path intersects the obstacle;
if the initial path does not intersect the obstacle, then:
among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the left side of a connecting line of the first path points and the center point of the obstacle, the decision of the obstacle is left-hand detouring;
Among the discrete path points in the initial path, if two adjacent first path points and second path points close to the obstacle exist, so that the second path points are positioned on the right side of a connecting line between the first path points and the center point of the obstacle, the decision of the obstacle is to bypass right; and
An optimization module configured to optimize the initial path of the drone based on the decision of the obstacle;
Wherein the second path point is located behind the first path point in the extending direction of the initial path.
9. An electronic device, comprising:
one or more processors;
A storage device configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.
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