CN113138597A - Obstacle avoidance method of intelligent trolley and intelligent trolley - Google Patents
Obstacle avoidance method of intelligent trolley and intelligent trolley Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
Abstract
The application is suitable for the technical field of robots, and provides an obstacle avoiding method of an intelligent trolley and the intelligent trolley, wherein the obstacle avoiding method comprises the following steps: acquiring vehicle information, wherein the vehicle information comprises a current vehicle speed, a vehicle size and a preset safe distance; determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size and the preset safe distance; if an obstacle exists in the current obstacle avoidance area, determining an obstacle avoidance strategy according to the distribution condition of the obstacle; and executing the obstacle avoidance strategy, dynamically planning an obstacle avoidance area of the intelligent vehicle by combining the vehicle speed of the intelligent vehicle, and selecting a corresponding obstacle avoidance strategy according to the distribution condition of the obstacles, so that the driving safety of the vehicle can be effectively ensured.
Description
Technical Field
The application belongs to the technical field of robots, and particularly relates to an obstacle avoidance method of an intelligent trolley and the intelligent trolley.
Background
An intelligent Vehicle (AVG) is a transport Vehicle equipped with an electromagnetic or optical automatic navigation device, capable of traveling along a predetermined navigation route, and having safety protection and various transfer functions. The obstacle avoidance function of the intelligent trolley is the guarantee of safe operation of the intelligent trolley, the intelligent trolley can detect whether an obstacle exists in an obstacle avoidance area or not at present, the intelligent trolley can be controlled to decelerate and stop under the condition that the obstacle exists in the obstacle avoidance area, and the intelligent trolley is controlled to continue to operate until the obstacle disappears, however, a certain potential safety hazard still exists in the obstacle avoidance mode.
Disclosure of Invention
The embodiment of the application provides an obstacle avoidance method of an intelligent trolley and the intelligent trolley, which can improve the safety of the intelligent trolley in the running process and ensure the safe running of the intelligent trolley.
In a first aspect, an embodiment of the present application provides an obstacle avoidance method for an intelligent vehicle, including:
acquiring vehicle information, wherein the vehicle information comprises a current vehicle speed, a vehicle size and a preset safe distance;
determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size and the preset safe distance;
if an obstacle exists in the current obstacle avoidance area, determining an obstacle avoidance strategy according to the distribution condition of the obstacle;
and executing the obstacle avoidance strategy.
In a possible implementation manner of the first aspect, the determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size, and the preset safe distance further includes:
calculating a deceleration distance according to the current vehicle speed and the deceleration speed;
calculating the length of the current obstacle avoidance area according to the deceleration distance, the length of the vehicle and the preset safety distance;
calculating the width of the current obstacle avoidance area according to the width of the vehicle;
and determining the current obstacle avoidance area according to the length of the current obstacle avoidance area and the width of the current obstacle avoidance area.
4. In a possible implementation manner of the first aspect, the determining an obstacle avoidance policy according to a distribution condition of the obstacles includes:
if the obstacles are detected to be distributed in the emergency stop area, determining that the obstacle avoidance strategy is emergency stop;
and if the obstacles are detected to be distributed in the deceleration area, determining that the obstacle avoidance strategy is deceleration.
In a possible implementation manner of the first aspect, if an obstacle exists in the current obstacle avoidance area, before determining an obstacle avoidance policy according to a distribution situation of the obstacle, the method further includes:
and detecting the distribution condition of the obstacles.
In a possible implementation manner of the first aspect, if an obstacle exists in the current obstacle avoidance area, determining an obstacle avoidance policy according to a distribution situation of the obstacle, where the method further includes:
if no obstacle exists in the current obstacle avoidance area, detecting whether an obstacle exists in a rescheduled area;
and if the obstacle exists in the re-planning area, setting a re-planning route according to the current position information of the intelligent vehicle and the position information of the obstacle.
In a possible implementation manner of the first aspect, if an obstacle exists in the rescheduled area, after the rescheduled route is set according to the current location information of the smart car and the location information of the obstacle, the method further includes:
and uploading the re-planned route to a dispatching center.
In a possible implementation manner of the first aspect, after uploading the re-planned route to a scheduling center, the method further includes:
and after receiving a confirmation instruction issued by the dispatching center, controlling the intelligent trolley to run according to the re-planned route.
In a second aspect, an embodiment of the present application provides an intelligent vehicle, including:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring vehicle information, and the vehicle information comprises a current vehicle speed, a vehicle size and a preset safe distance;
the planning module is used for determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size and the preset safety distance;
the selection module is used for determining an obstacle avoidance strategy according to the distribution condition of the obstacles if the obstacles exist in the current obstacle avoidance area;
and the execution module is used for executing the obstacle avoidance strategy.
In a third aspect, an embodiment of the present application provides an intelligent vehicle, including: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method according to the first aspect.
In a fifth aspect, the present application provides a computer program product, which when running on a smart cart, causes the smart cart to perform the method of the first aspect.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Compared with the prior art, the embodiment of the application has the advantages that: by dynamically planning the obstacle avoidance area of the intelligent trolley by combining the speed of the intelligent trolley and selecting a corresponding obstacle avoidance strategy according to the distribution condition of obstacles, the driving safety of the vehicle can be effectively ensured.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic view of a scene to which an obstacle avoidance method of an intelligent vehicle according to an embodiment of the present application is applied;
fig. 2 is a schematic flow chart illustrating an implementation of an obstacle avoidance method for an intelligent vehicle according to an embodiment of the present application;
fig. 3 is a schematic view of a distribution of current obstacle avoidance areas according to an embodiment of the present application;
fig. 4 is a schematic flow chart illustrating an implementation of an obstacle avoidance method for an intelligent vehicle according to another embodiment of the present application;
fig. 5 is a schematic diagram illustrating a distribution of current obstacle avoidance areas and a rescheduled area according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of an intelligent vehicle provided in the embodiment of the present application;
fig. 7 is a schematic structural diagram of an intelligent vehicle according to another embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The obstacle avoidance function of the intelligent vehicle is a safety guarantee in the operation process of the whole intelligent vehicle dispatching system, the obstacle avoidance function of the intelligent vehicle is usually realized by an obstacle avoidance module, the obstacle avoidance module usually comprises a detection mechanism of an obstacle, and the obstacle avoidance strategy is determined. The driving track issued by the dispatching system to the intelligent trolley is usually a fixed track, so a fixed obstacle avoidance area is usually set, when the obstacle is detected to fall into the fixed obstacle avoidance area, the intelligent trolley is controlled to decelerate until the intelligent trolley stops, and then the intelligent trolley is continuously controlled to run after the obstacle disappears. However, the speed of the vehicle is changed during the running process of the vehicle, and a fixed obstacle avoidance area is arranged, so that the vehicle can not stop in front of an obstacle, and further safety accidents can be caused.
In order to improve the safety of the intelligent trolley in the running process, the embodiment of the application provides an obstacle avoidance method of the intelligent trolley. By dynamically planning the obstacle avoidance area of the intelligent trolley by combining the speed of the intelligent trolley and selecting a corresponding obstacle avoidance strategy according to the distribution condition of obstacles, the driving safety of the vehicle can be effectively ensured.
Fig. 1 shows a schematic structural diagram of an intelligent vehicle dispatching system to which the obstacle avoidance method for an intelligent vehicle provided in the embodiment of the present application is applied, and as shown in fig. 1, the intelligent vehicle dispatching system may include a dispatching center 100 and an intelligent vehicle 200. It should be noted that the above-mentioned intelligent vehicle dispatching system can also dispatch multiple intelligent vehicles 200 at the same time, and fig. 1 is only an example and not a limitation.
The dispatching center 100 can be in communication connection with the intelligent vehicle 200, so as to realize information interaction, such as instruction issuing and information uploading.
Specifically, the dispatching center 100 may issue a dispatching command to the smart car 200, and the smart car 200 may determine a driving path according to the dispatching command and then control the smart car 200 to drive according to the driving path. In addition, the intelligent vehicle 200 provided by the embodiment of the application can dynamically plan the obstacle avoidance area, and select the corresponding obstacle avoidance strategy according to the distribution condition of the obstacles, so that the safe driving of the vehicle is ensured, and the safety of the vehicle driving process is improved.
Fig. 2 shows a schematic implementation flow diagram of an obstacle avoidance method for an intelligent vehicle according to an embodiment of the present application, where an execution subject is the intelligent vehicle, as shown in fig. 2, the obstacle avoidance method for the intelligent vehicle may include S201 to S204, which are detailed as follows:
s201: vehicle information is acquired.
In the embodiment of the present application, the vehicle information includes a current vehicle speed, a vehicle size, and a preset safe distance.
In the embodiment of the application, the intelligent trolley can acquire the vehicle information of the intelligent trolley under the condition that the preset condition is triggered. The preset condition may be that a preset period is reached, that is, the smart car may periodically acquire the vehicle information of the smart car according to the preset period (5 seconds or 10 minutes).
In an embodiment of the present application, the preset condition may also be speed change of the intelligent vehicle, that is, the intelligent vehicle may detect the vehicle speed in real time, and when the intelligent vehicle detects that the vehicle speed changes, the intelligent vehicle may obtain the vehicle information of the intelligent vehicle again.
In an embodiment of the present application, the preset condition may also be that the intelligent vehicle receives a reset instruction issued by the scheduling center, that is, the intelligent vehicle reacquires current vehicle information when the intelligent vehicle receives the reset instruction issued by the scheduling center.
In specific application, the intelligent vehicle can acquire the current vehicle speed of the intelligent vehicle in real time through the vehicle speed detection device. The vehicle speed detection device includes, but is not limited to, a vehicle speed detection sensor and the like. In addition, the intelligent car can also store the current speed acquired in real time into a memory of the intelligent car, and the intelligent car reads the current speed from the memory under the condition that the preset condition is triggered.
The vehicle size refers to information such as vehicle length and vehicle width of the intelligent vehicle, the vehicle size can be determined according to the size of an actual vehicle, the vehicle size of each intelligent vehicle can be stored in a memory of the intelligent vehicle in advance, and the intelligent vehicle reads out related information from the memory under the condition that a preset condition is triggered.
The preset safety distance refers to a minimum distance between the intelligent vehicle and the obstacle under the condition that the vehicle is guaranteed to run safely (namely, the intelligent vehicle does not collide with the obstacle), and the preset safety distance can be determined according to an actual application scene, and the method is not limited in the application.
S202: and determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size and the preset safe distance.
In the embodiment of the application, the intelligent vehicle can dynamically determine the current obstacle avoidance area of the intelligent vehicle based on the current vehicle speed.
In specific application, the intelligent trolley can calculate the deceleration distance according to the current speed and the deceleration and acceleration. The deceleration speed refers to the acceleration of the intelligent vehicle in the deceleration process, and the deceleration distance refers to the distance traveled by the intelligent vehicle in the process of decelerating from the current vehicle speed to 0 (namely parking). It should be noted that the deceleration rate may be preset according to an actual application scenario, and the present application is not limited to this.
The above formula for calculating the deceleration distance according to the current vehicle speed and the deceleration rate may be:
dec_dis＝v*v/2*dec；
where dec _ dis is a deceleration distance, v is a current vehicle speed, and dec is a deceleration rate.
After the deceleration distance is determined, the intelligent trolley can calculate the length of the current obstacle avoidance area based on the deceleration distance, the length of the vehicle and the preset safety distance.
The calculation formula for calculating the length of the current obstacle avoidance area based on the deceleration distance, the vehicle length and the preset safety distance may be as follows:
Larea＝0.5Lcar+safe_dis+dec_dis；
wherein L isareaIs the length, L, of the current obstacle avoidance areacarThe vehicle length is referred to, safe _ dis is a preset safe distance, and dec _ dis is a deceleration distance.
The intelligent trolley can also calculate the width of the current obstacle avoidance area according to the width of the vehicle.
The above calculation formula for calculating the width of the current obstacle avoidance area according to the vehicle width may be:
Warea＝0.5Wcar+0.2；
wherein, WareaIs the width, W, of the current obstacle avoidance areacarRefers to the width of the vehicle.
After the length of the current obstacle avoidance area and the width of the current obstacle avoidance area are determined, the coverage area of the current obstacle avoidance area can be determined.
S203: and if the obstacles exist in the current obstacle avoidance area, determining an obstacle avoidance strategy according to the distribution condition of the obstacles.
After the current obstacle avoidance area is obtained, the intelligent vehicle can judge the distribution condition of each obstacle, and judge whether the obstacle falls into the current obstacle avoidance area.
In specific application, the detection of the barrier is divided into static barrier detection and dynamic barrier detection, and the classification of the barrier can be chosen or rejected according to the performance of the laser radar, if the barrier is a single-line laser radar and can only be swept to a plane, the classification of the barrier can only be classified according to line characteristics and spot beams, if the barrier is a multi-line laser radar, the visual angle range of the horizontal direction and the vertical direction is wider, the outline of the barrier is more clear, and the classification can be more accurate. The intelligent vehicle can detect the distribution situation of the obstacles based on the laser radar, further obtain the position information of the obstacles, and then judge whether the obstacles exist in the current obstacle avoidance area according to the position information of each obstacle. If the obstacle exists in the current obstacle avoidance area, the situation that the intelligent vehicle needs to be controlled to avoid the obstacle is shown, and if the obstacle does not exist in the current obstacle avoidance area, the situation that the intelligent vehicle does not need to be controlled to avoid the obstacle is shown, and the intelligent vehicle can continue to run along the running route.
In the embodiment of the present application, as shown in fig. 3, the current obstacle avoidance area may be divided into an emergency stop area and a deceleration area, and accordingly, the obstacle avoidance strategy may be decelerated and emergency stopped.
In specific application, if the obstacles are detected to be distributed in the sudden stop area, determining that the obstacle avoidance strategy is sudden stop; and if the obstacles are detected to be distributed in the deceleration area, determining that the obstacle avoidance strategy is deceleration.
It should be noted that the division of the scram area and the deceleration area may be determined according to an actual application scenario, and the application is not limited thereto.
S204: and executing the obstacle avoidance strategy.
After the obstacle avoidance strategy is determined, the intelligent vehicle generates a corresponding control command according to the obstacle avoidance strategy and sends the control command to an execution mechanism (such as a power mechanism, a transmission mechanism and the like) of the intelligent vehicle, so that the execution mechanism executes a corresponding action, and the intelligent vehicle executes the obstacle avoidance strategy.
In specific application, if the obstacle avoidance strategy is sudden stop, a control instruction for setting the current vehicle speed to 0 is generated and sent to the execution mechanism, and the execution mechanism sets the current vehicle speed of the intelligent vehicle to 0 so that the intelligent vehicle stops running immediately; if the obstacle avoidance strategy is deceleration, a control instruction for setting the current deceleration and acceleration as a preset value is generated and sent to the executing mechanism, and the executing mechanism decelerates according to the preset deceleration and acceleration, so that the intelligent trolley decelerates until the intelligent trolley stops.
In summary, the obstacle avoidance method for the intelligent vehicle provided by the embodiment of the application can dynamically plan the obstacle avoidance area based on the current vehicle speed of the intelligent vehicle, and can select the corresponding obstacle avoidance strategy according to the distribution condition of the obstacles, thereby effectively ensuring the driving safety of the vehicle.
Referring to fig. 4, in another embodiment of the present application, different from the previous embodiment, as shown in fig. 4, the obstacle avoidance method for an intelligent vehicle further includes S401 to S403, which are detailed as follows:
s401: and if the obstacle exists in the re-planning area, setting a re-planning route according to the current position information of the intelligent vehicle and the position information of the obstacle.
Referring to fig. 5, when the intelligent vehicle replans the current obstacle avoidance area, a replanning area may be further set, a distance between the replanning area and the intelligent vehicle is greater than a distance between the current obstacle avoidance area and the intelligent vehicle, and a length and a width of the replanning area may be set according to an actual application scenario, which is not limited in the present application.
Under the condition that no obstacle exists in the current obstacle avoidance area and the obstacle exists in the rescheduled area is detected, the intelligent trolley can be triggered to reschedule the route, so that the obstacle is avoided, the flexibility of the intelligent trolley is improved, and the driving safety of the intelligent trolley is guaranteed.
The intelligent car can plan a brand-new route, namely a re-planned route, according to the current position information of the intelligent car and the position information of the obstacle.
S402: and uploading the re-planned route to a dispatching center.
Because the dispatching center can simultaneously dispatch the operation of a plurality of intelligent trolleys, in order to ensure that the re-planned route does not conflict with the driving routes of other intelligent trolleys, the re-planned route set by the intelligent trolleys needs to be uploaded to the dispatching center, so that the dispatching center judges whether the re-planned route is feasible or not based on the re-planned route and the driving routes of other intelligent trolleys, namely whether the intelligent trolleys can drive according to the re-planned route or not is judged, if the intelligent trolleys do not conflict with other intelligent trolleys when driving according to the re-planned route, the dispatching center returns a confirmation instruction, otherwise, the dispatching center does not return a confirmation instruction or a refund instruction.
S403: and after receiving a confirmation instruction issued by the dispatching center, controlling the intelligent trolley to run according to the re-planned route.
The intelligent vehicle can monitor whether a confirmation instruction issued by the dispatching center is received or not within a preset time period, and if the confirmation instruction issued by the dispatching center is received within the preset time period, the re-planned route is not conflicted with the running routes of other intelligent vehicles, so that the intelligent vehicle can run according to the re-planned route. At the moment, a control instruction for driving according to the re-planned route can be issued to the executing mechanism, so that the intelligent trolley drives according to the re-planned route to avoid the obstacle.
In another embodiment of the application, if the intelligent vehicle does not receive the confirmation instruction issued by the scheduling center or the intelligent vehicle receives the refund instruction issued by the scheduling center, the intelligent vehicle is controlled to continue to run along the original route until the obstacle falls into the current obstacle avoidance area, and then the intelligent vehicle is controlled to execute a corresponding obstacle avoidance strategy.
Corresponding to the obstacle avoidance method for the intelligent vehicle described in the above embodiment, fig. 6 shows a structural block diagram of the intelligent vehicle provided in the embodiment of the present application, and for convenience of description, only the parts related to the embodiment of the present application are shown.
Referring to fig. 6, the smart cart 60 includes: an acquisition module 601, a planning module 602, a selection module 603, and an execution module 604.
The obtaining module 601 is configured to obtain vehicle information, where the vehicle information includes a current vehicle speed, a vehicle size, and a preset safety distance;
the planning module 602 is configured to determine a current obstacle avoidance area according to the current vehicle speed, the vehicle size, and the preset safe distance;
the selection module 603 is configured to determine an obstacle avoidance policy according to a distribution condition of obstacles if the obstacles exist in the current obstacle avoidance area;
the execution module 604 is configured to execute the obstacle avoidance policy.
In one possible implementation, the vehicle information further includes a deceleration rate, the vehicle size includes a vehicle length and a vehicle width, and the planning module 602 may include a first calculating unit, a second calculating unit, a third calculating unit, and a determining unit.
The first calculation unit is used for calculating a deceleration distance according to the current vehicle speed and the deceleration speed;
the second calculation unit is used for calculating the length of the current obstacle avoidance area according to the deceleration distance, the length of the vehicle and the preset safety distance;
the third calculating unit is used for calculating the width of the current obstacle avoidance area according to the width of the vehicle;
the determining unit is used for determining the current obstacle avoidance area according to the length of the current obstacle avoidance area and the width of the current obstacle avoidance area.
In a possible implementation manner, the current obstacle avoidance area includes an emergency stop area and a deceleration area, the obstacle avoidance policy includes emergency stop and deceleration, and the selection module 603 may include a first selection unit and a second selection unit.
The first selection unit is used for determining that the obstacle avoidance strategy is the emergency stop if the obstacles are detected to be distributed in the emergency stop area;
the second selecting unit is used for determining that the obstacle avoidance strategy is deceleration if the obstacles are detected to be distributed in the deceleration area.
In a possible implementation manner, the intelligent vehicle further comprises a detection module, and the detection module is used for detecting the distribution situation of the obstacles.
In a possible implementation manner, the intelligent vehicle further includes a re-planning module, and the re-planning module is configured to detect whether an obstacle exists in a re-planning area if the obstacle does not exist in the current obstacle avoidance area; and if the obstacle exists in the re-planning area, setting a re-planning route according to the current position information of the intelligent vehicle and the position information of the obstacle.
In a possible implementation manner, the intelligent trolley further comprises an uploading module, and the uploading module is used for uploading the re-planned route to a dispatching center.
In a possible implementation manner, the execution module 604 is further configured to control the intelligent vehicle to travel according to the re-planned route after receiving a confirmation instruction issued by the scheduling center.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 7 is a schematic structural diagram of an intelligent vehicle according to another embodiment of the present application. As shown in fig. 7, the intelligent vehicle 7 of this embodiment includes: at least one processor 70 (only one is shown in fig. 7), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, wherein the processor 70 executes the computer program 72 to implement the steps in any of the above-mentioned embodiments of the obstacle avoidance method for the intelligent vehicle.
The smart cart may include, but is not limited to, a processor 70, a memory 71. Those skilled in the art will appreciate that fig. 7 is merely an example of the smart cart 7, and does not constitute a limitation on the smart cart 7, and may include more or less components than those shown, or combine some components, or different components, such as input and output devices, network access devices, etc.
The Processor 70 may be a Central Processing Unit (CPU), and the Processor 70 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may in some embodiments be an internal storage unit of the smart cart 7, such as a hard disk or a memory of the smart cart 7. The memory 71 may also be an external storage device of the Smart cart 7 in other embodiments, such as a plug-in hard disk provided on the Smart cart 7, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 71 may also include both an internal storage unit and an external storage device of the smart cart 7. The memory 71 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 71 may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiment of the present application provides a computer program product, which when running on an intelligent vehicle, enables the intelligent vehicle to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a smart cart, recording medium, computer Memory, Read-Only Memory (ROM), Random-Access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An obstacle avoidance method of an intelligent trolley is characterized by comprising the following steps:
acquiring vehicle information, wherein the vehicle information comprises a current vehicle speed, a vehicle size and a preset safe distance;
determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size and the preset safe distance;
if an obstacle exists in the current obstacle avoidance area, determining an obstacle avoidance strategy according to the distribution condition of the obstacle;
and executing the obstacle avoidance strategy.
2. The obstacle avoidance method for the intelligent vehicle as claimed in claim 1, wherein the vehicle information further includes deceleration, the vehicle size includes vehicle length and vehicle width, and the determining the current obstacle avoidance area according to the current vehicle speed, the vehicle size, and the preset safety distance includes:
calculating a deceleration distance according to the current vehicle speed and the deceleration speed;
calculating the length of the current obstacle avoidance area according to the deceleration distance, the length of the vehicle and the preset safety distance;
calculating the width of the current obstacle avoidance area according to the width of the vehicle;
and determining the current obstacle avoidance area according to the length of the current obstacle avoidance area and the width of the current obstacle avoidance area.
3. An obstacle avoidance method for an intelligent vehicle as claimed in claim 1, wherein the current obstacle avoidance area includes an emergency stop area and a deceleration area, the obstacle avoidance strategy includes emergency stop and deceleration, and determining the obstacle avoidance strategy according to the distribution of the obstacles includes:
if the obstacles are detected to be distributed in the emergency stop area, determining that the obstacle avoidance strategy is emergency stop;
and if the obstacles are detected to be distributed in the deceleration area, determining that the obstacle avoidance strategy is deceleration.
4. The obstacle avoidance method for the intelligent vehicle according to claim 1, wherein if an obstacle exists in the current obstacle avoidance area, before determining an obstacle avoidance strategy according to the distribution of the obstacle, the method further comprises:
and detecting the distribution condition of the obstacles.
5. An obstacle avoidance method for an intelligent vehicle according to any one of claims 1 to 4, wherein if an obstacle exists in the current obstacle avoidance area, an obstacle avoidance strategy is determined according to the distribution of the obstacle, and the method further comprises:
if no obstacle exists in the current obstacle avoidance area, detecting whether an obstacle exists in a rescheduled area;
and if the obstacle exists in the re-planning area, setting a re-planning route according to the current position information of the intelligent vehicle and the position information of the obstacle.
6. An obstacle avoidance method for an intelligent vehicle as claimed in claim 5, wherein if an obstacle exists in the rescheduled area, after setting the rescheduled route according to the current position information of the intelligent vehicle and the position information of the obstacle, further comprising:
and uploading the re-planned route to a dispatching center.
7. An obstacle avoidance method for an intelligent vehicle as recited in claim 6, wherein after uploading the re-planned route to a dispatch center, the method further comprises:
and after receiving a confirmation instruction issued by the dispatching center, controlling the intelligent trolley to run according to the re-planned route.
8. The utility model provides an intelligent vehicle which characterized in that includes:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring vehicle information, and the vehicle information comprises a current vehicle speed, a vehicle size and a preset safe distance;
the planning module is used for determining a current obstacle avoidance area according to the current vehicle speed, the vehicle size and the preset safety distance;
the selection module is used for determining an obstacle avoidance strategy according to the distribution condition of the obstacles if the obstacles exist in the current obstacle avoidance area;
and the execution module is used for executing the obstacle avoidance strategy.
9. A smart cart comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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| CN202110350222.6A CN113138597A (en) | 2021-03-31 | 2021-03-31 | Obstacle avoidance method of intelligent trolley and intelligent trolley |
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| CN202110350222.6A CN113138597A (en) | 2021-03-31 | 2021-03-31 | Obstacle avoidance method of intelligent trolley and intelligent trolley |
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