CN115628749A - Space monitoring system and method based on robot front-end information - Google Patents
Space monitoring system and method based on robot front-end information Download PDFInfo
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Abstract
The application provides a space monitoring system and a method thereof based on front-end information of a robot, wherein the method comprises the following steps: judging whether the peripheral objects are positioned on a task planning path of the robot or not according to the azimuth information of the peripheral objects, taking the objects positioned on the planning path as obstacles, and avoiding the obstacles by using an obstacle avoidance algorithm; according to the weather information of the place where the current task is executed, adaptively switching among a common mode, a rainproof mode and an antiskid mode; updating and adjusting the task planning path according to the real-time road condition information of the task planning path; and sending the motion trail and the motion time line to a cloud server so as to update the display of the real-time state of the logistics task of the robot. The method and the device can intelligently bypass the obstacles on the planned path; preventive measures can be taken according to the weather; when the conditions of severe congestion road sections, construction road sections, forbidden road sections and the like are met, the path can be re-planned, and the waybill can be delivered on time.
Description
Technical Field
The application relates to the technical field of robots, in particular to a space monitoring system and a method thereof based on front-end information of a robot.
Background
With the continuous popularization of robot technology, intelligent robots have appeared in application scenes such as industrial factories, restaurants and the like, and can transport goods and materials or food to destinations according to commands of people and built-in maps.
However, during the transportation process, the robot can encounter the situation that an obstacle blocks on the planned path; in addition, in rainy and snowy weather, the robot may be rainy and slip to cause financial loss, and no measures for preventing the robot according to the weather exist at present; finally, the severely congested road section, the construction road section and the forbidden road section are sporadically and randomly changed, and if the robot encounters the severely congested road section, the construction road section cannot pass through, the forbidden road section cannot pass through and the like in the operation process, the delivery time of the waybill can be delayed, and the user experience can be reduced.
Disclosure of Invention
In view of this, an object of the present application is to provide a space monitoring system based on robot front-end information, which can specifically solve the problems of real-time obstacle avoidance, weather response and road condition in the existing robot stream transportation.
Based on the above purpose, the present application provides a space monitoring method based on robot front-end information, including:
the method comprises the steps that azimuth information of surrounding objects is obtained in real time through a sensor on a robot, weather information of the place where a current task is executed is obtained through an internet module, and real-time road condition information of a task planning path is obtained through a built-in real-time navigation map;
judging whether the peripheral objects are positioned on a task planning path of the robot or not according to the azimuth information of the peripheral objects, taking the objects positioned on the planning path as obstacles, and avoiding the obstacles by using an obstacle avoidance algorithm;
performing adaptive switching among a common mode, a rainproof mode and an antiskid mode according to the weather information of the place where the current task is executed;
updating and adjusting the mission planning path according to the real-time road condition information of the mission planning path so as to avoid a severely congested road section, a construction road section and/or a forbidden road section;
and running the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to a cloud server so as to update the display of the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time.
Further, the acquiring of the orientation information of the surrounding objects in real time through a sensor on the robot, the acquiring of the weather information of the location where the current task is executed through an internet access module, and the acquiring of the real-time road condition information of the task planning path through the built-in real-time navigation map include:
the sensor is a laser sensor, the laser sensor is arranged in the robot to sense the dynamic state of the surrounding object, and the angle, the direction and the distance of the surrounding object from the position of the robot are calculated;
acquiring geographical positioning information of a place where a current task is executed through a GPS positioning module, inputting the geographical positioning information into built-in internet weather software, and outputting the weather information of the geographical positioning information through the internet weather software;
and inputting the starting point and end point positioning information of the mission planning path into a built-in real-time navigation map to obtain the mission planning path, connecting the mission planning path with the Internet through an Internet access module, and downloading and loading real-time road condition information in real time.
Further, the determining whether the peripheral object is located on a mission planning path of the robot according to the orientation information of the peripheral object and using the object located on the planning path as an obstacle includes:
inputting the angle, the direction and the distance of the peripheral object from the position of the robot into the built-in real-time navigation map so as to mark the position of the peripheral object in the map;
loading a mission planning path in the map;
and judging whether the positions of the surrounding objects in the map have an overlapping area with the mission planning path or not, determining the surrounding objects with the overlapping area as obstacles, and determining the surrounding objects without the overlapping area as non-obstacles.
Further, avoiding the obstacle using an obstacle avoidance algorithm, comprising:
establishing a two-dimensional grid map by using the azimuth information of the known obstacles;
establishing a global coordinate system according to the position of the robot in the two-dimensional grid map, and setting a starting point and an end point of the robot;
determining the shortest path from the starting point to the end point by adopting a jumping point search algorithm;
the shortest path comprises a plurality of local target points which are sequentially connected;
in the process of controlling the robot to move to each local target point, a local obstacle avoidance algorithm is adopted to avoid dynamic obstacles, and the method comprises the following steps: detecting the dynamic barrier in real time, and acquiring the real-time distance and the azimuth angle of the dynamic barrier relative to the mobile robot; controlling the robot to rotate according to the real-time distance and the azimuth angle so as to avoid the dynamic obstacle, and the method comprises the following steps: presetting a plurality of distance range thresholds, wherein each distance range threshold corresponds to one obstacle avoidance rotating angle; determining a distance range threshold value and a corresponding obstacle avoidance rotation angle of the real-time distance according to the real-time distance; and controlling the mobile robot to rotate the obstacle avoidance rotating angle so as to avoid the dynamic obstacle.
Further, the adaptively switching among a normal mode, a rainproof mode and an antiskid mode according to the weather information of the location where the current task is executed comprises:
acquiring a current working mode, wherein the types of the current working mode comprise a common mode, a rainproof mode and an antiskid mode;
if the weather information is rainy days, switching the current working mode into a rainproof mode;
if the weather information is snow or the current air temperature is lower than zero, switching the current working mode into an anti-skidding mode;
and if the weather information is not rainy or snowy days and the current air temperature is higher than zero degree, switching the current working mode into a common mode.
Further, the updating and adjusting the mission planning path according to the real-time road condition information of the mission planning path to avoid a severely congested road section, a construction road section and/or a forbidden road section includes:
if the real-time road condition information of the mission planning path does not comprise a heavily congested road section, a construction road section and/or a forbidden road section, continuing to operate the robot according to the current mission planning path;
and if the real-time road condition information of the mission planning path comprises a severe congestion road section, a construction road section and/or a block road section, indicating the built-in real-time navigation map to be switched to a congestion avoiding, construction and block mode, and updating and adjusting the mission planning path to avoid the severe congestion road section, the construction road section and/or the block road section.
Further, the operating the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to the cloud server to update the display of the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time, and the method comprises the following steps:
after the obstacle avoidance algorithm is used for avoiding the obstacles, the current working mode is switched according to weather information, and the mission planning path is updated and adjusted according to real-time road condition information of the mission planning path, the robot is operated, and the historical motion trail and the motion time line of the robot are recorded in the built-in real-time navigation map;
sending the historical motion trail and the motion time line to a cloud server;
the cloud server loads the historical motion trail and the motion timeline to a current logistics task to update the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time;
and sending the real-time state of the logistics task to a mobile client for displaying at the mobile client.
Based on above-mentioned purpose, this application has still provided a space monitoring system based on robot front end information, includes:
the front-end information acquisition module is used for acquiring the azimuth information of surrounding objects in real time through a sensor on the robot, acquiring the weather information of the place where the current task is executed through the internet access module, and acquiring the real-time road condition information of a task planning path through a built-in real-time navigation map;
the obstacle avoidance module is used for judging whether the peripheral object is positioned on a task planning path of the robot or not according to the azimuth information of the peripheral object, taking the object positioned on the planning path as an obstacle, and avoiding the obstacle by using an obstacle avoidance algorithm;
the weather mode module is used for adaptively switching among a common mode, a rainproof mode and an antiskid mode according to weather information of the place where the current task is executed;
the path adjusting module is used for updating and adjusting the mission planning path according to the real-time road condition information of the mission planning path so as to avoid a severely congested road section, a construction road section and/or a forbidden road section;
and the real-time state updating module is used for operating the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to the cloud server so as to update the display of the logistics task real-time state of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time.
Generally, the advantages of the present application and the experience brought to the user are:
according to the method and the device, the obstacles on the planned path can be intelligently bypassed in the freight process of the robot; preventive measures can be taken according to the weather; finally, if the robot encounters a heavily congested road section, a construction road section cannot pass through, a forbidden road section cannot pass through and the like in the operation process, a path can be re-planned, and the fact that the waybill is delivered on time is guaranteed.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 shows a schematic diagram of the system architecture of the present application.
Fig. 2 shows a flowchart of a robot front-end information based spatial monitoring system according to an embodiment of the application.
Fig. 3 is a configuration diagram showing a robot front end information-based space monitoring system according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 5 is a schematic diagram of a storage medium provided in an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows a schematic diagram of the system architecture of the present application. In an embodiment of the application, the application provides a space monitoring system based on robot front-end information and a method thereof. The method comprises the following steps: judging whether the peripheral object is positioned on a task planning path of the robot or not according to the azimuth information of the peripheral object, taking the object positioned on the planning path as an obstacle, and avoiding the obstacle by using an obstacle avoidance algorithm; performing adaptive switching among a common mode, a rainproof mode and an antiskid mode according to the weather information of the place where the current task is executed; updating and adjusting the task planning path according to the real-time road condition information of the task planning path; and sending the motion trail and the motion time line to a cloud server so as to update the display of the real-time state of the logistics task of the robot.
The dispatching of the whole robot system can receive the waybill through the server, and the waybill is analyzed and split, so that the reasonable robot completes the waybill. The control commands of the server to the robot can be sent by various existing wireless communications, such as Wifi, bluetooth, zigbee, GPRS, GSM, 4G, 5G, etc.
Fig. 2 shows a flowchart of a robot front-end information based spatial monitoring system according to an embodiment of the application. As shown in fig. 2, the space monitoring system based on the robot front-end information includes:
step 101: the method includes the steps that the azimuth information of surrounding objects is acquired in real time through a sensor on a robot, the weather information of the place where the current task is executed is acquired through an internet module, and the real-time road condition information of a task planning path is acquired through a built-in real-time navigation map, and the method includes the following steps:
the sensor is a laser sensor, the laser sensor is arranged in the robot to sense the dynamic state of the surrounding object, and the angle, the direction and the distance of the surrounding object from the position of the robot are calculated;
acquiring geographical positioning information of a place where a current task is executed through a GPS positioning module, inputting the geographical positioning information into built-in internet weather software, and outputting the weather information of the geographical positioning information through the internet weather software;
and inputting the starting point and end point positioning information of the mission planning path into a built-in real-time navigation map to obtain the mission planning path, connecting the mission planning path with the Internet through an Internet access module, and downloading and loading real-time road condition information in real time.
The purpose of the step is to obtain the weather of the place where the obstacle and the robot are located through the built-in configuration of the robot such as a sensor, a positioning module, an internet module and the like, respectively mark on a map according to a warehouse and a receiver address in an waybill when the robot is started at the beginning, and automatically obtain an initial mission planning path through a navigation map.
Step 102: judging whether the peripheral object is positioned on a task planning path of the robot or not according to the azimuth information of the peripheral object, taking the object positioned on the planning path as an obstacle, and avoiding the obstacle by using an obstacle avoidance algorithm, wherein the method comprises the following steps:
inputting the angle, the direction and the distance of the peripheral object from the position of the robot into the built-in real-time navigation map so as to mark the position of the peripheral object in the map;
loading a mission planning path in the map;
judging whether the positions of the surrounding objects in the map have an overlapping area with the mission planning path or not, determining the surrounding objects with the overlapping area as obstacles, and determining the surrounding objects without the overlapping area as non-obstacles;
establishing a two-dimensional grid map by using the azimuth information of the known obstacles;
establishing a global coordinate system according to the position of the robot in the two-dimensional grid map, and setting a starting point and an end point of the robot;
determining the shortest path from the starting point to the end point by adopting a jumping point search algorithm;
the shortest path comprises a plurality of local target points which are sequentially connected;
in the process of controlling the robot to move to each local target point, a local obstacle avoidance algorithm is adopted to avoid dynamic obstacles, and the method comprises the following steps: detecting the dynamic barrier in real time, and acquiring the real-time distance and the azimuth angle of the dynamic barrier relative to the mobile robot; controlling the robot to rotate according to the real-time distance and the azimuth angle so as to avoid the dynamic obstacle, and the method comprises the following steps: presetting a plurality of distance range thresholds, wherein each distance range threshold corresponds to one obstacle avoidance rotating angle; determining a distance range threshold value and a corresponding obstacle avoidance rotation angle of the real-time distance according to the real-time distance; and controlling the mobile robot to rotate the obstacle avoidance rotating angle so as to avoid the dynamic obstacle.
In step 102, the obstacle information in the front-end information is used, and an obstacle avoidance algorithm is used to intelligently avoid obstacles on the planned path and prevent the obstacles from colliding with the robot, so that property loss is caused.
Step 103: according to the weather information of the place where the current task is executed, the adaptive switching is carried out among a common mode, a rainproof mode and an antiskid mode, and the adaptive switching method comprises the following steps:
acquiring a current working mode, wherein the types of the current working mode comprise a common mode, a rainproof mode and an antiskid mode;
if the weather information is rainy days, switching the current working mode into a rainproof mode;
if the weather information is snow or the current air temperature is lower than zero, switching the current working mode into an anti-skidding mode;
and if the weather information is not rainy or snowy days and the current air temperature is higher than zero degree, switching the current working mode into a common mode.
In the commodity circulation transportation of reality, the robot is difficult to avoid working at outdoor environment, and outdoor environment often has comparatively abominable weather, for example the condition such as rainy, snowing, low temperature, then leads to the robot to skid or electronic components, goods etc. are drenched, then leads to the robot to topple, short circuit damage etc. heavily. Therefore, the working mode of the robot is adjusted in time according to the real-time weather information, and the situation can be avoided. For example, when the local weather is detected to be rainy, the robot is switched to the rainproof mode, and the robot automatically opens the self-contained rainproof umbrella, or closes the water leakage component, opens the protective cover and other functions, so that the robot can work in a certain degree of rainy days without being affected. If it is snowing or when freezing to find local weather, switch to under the antiskid mode, the robot opens snowfield track or antiskid brake function or anti-skidding support, can effectively avoid slipping.
Step 104: updating and adjusting the mission planning path according to the real-time road condition information of the mission planning path so as to avoid a severely congested road section, a construction road section and/or a forbidden road section, and the method comprises the following steps:
if the real-time road condition information of the mission planning path does not comprise a heavily congested road section, a construction road section and/or a forbidden road section, continuing to operate the robot according to the current mission planning path;
and if the real-time road condition information of the mission planning path comprises a severe congestion road section, a construction road section and/or a forbidden road section, indicating the built-in real-time navigation map to be switched to a congestion avoiding, construction and forbidden modes, and updating and adjusting the mission planning path to avoid the severe congestion road section, the construction road section and/or the forbidden road section.
In real life, a temporary construction road section or a road section which is forbidden due to the condition of organizing sports activities and the like can be frequently encountered, and if the temporary construction road section or the road section which is forbidden due to the condition of organizing sports activities and the like cannot be known in time, the robot cannot pass through the road section or delay time around a long distance is caused. Because the current internet navigation map has the function of reminding the user of closing the road sections and the construction road sections, the conditions can be obtained by obtaining the real-time road condition information, so that the path is re-planned and the road sections are avoided. And for avoiding the severely congested road section, timeliness is mainly considered, and in order to prevent the conditions of traffic accidents, slow advancing and the like which are possibly caused by the operation of the robot on the severely congested road section, the traffic flow condition on the planned path can be checked by acquiring the road condition information in real time. If a certain section in front is seriously congested, the path can be re-planned when the congested road section is about to be reached, and the congested road section is avoided.
Step 105: the method comprises the following steps of operating the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to the cloud server to update the display of the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time, and comprises the following steps:
after the obstacle avoidance algorithm is used for avoiding the obstacles, the current working mode is switched according to weather information, and the mission planning path is updated and adjusted according to real-time road condition information of the mission planning path, the robot is operated, and the historical motion trail and the motion time line of the robot are recorded in the built-in real-time navigation map;
sending the historical motion trail and the motion timeline to a cloud server;
the cloud server loads the historical motion trail and the motion time line to a current logistics task to update the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time;
and sending the real-time state of the logistics task to a mobile client for displaying at the mobile client.
In step 105, the robot performs optimized operation by using the results of the previous three steps, records the motion track of the robot and sends the motion track to the cloud server, and the server sends the current positioning and the estimated task completion time of the robot to the mobile phone of the receiver or the sender, so that the user can conveniently master logistics information in real time.
The method and the device can intelligently bypass the obstacles on the planned path in the process of transporting goods by the robot; preventive measures can be taken according to the weather; finally, if the robot encounters a heavily congested road section, a construction road section cannot pass through, a forbidden road section cannot pass through and the like in the operation process, a path can be re-planned, and the fact that the waybill is delivered on time is guaranteed.
An application embodiment provides a space monitoring system based on robot front-end information, which is configured to execute the space monitoring system based on robot front-end information described in the foregoing embodiment, and as shown in fig. 3, the system includes:
the front-end information acquisition module 501 is used for acquiring the azimuth information of surrounding objects in real time through a sensor on the robot, acquiring the weather information of the place where the current task is executed through an internet module, and acquiring the real-time road condition information of a task planning path through a built-in real-time navigation map;
the obstacle avoidance module 502 is configured to determine whether a peripheral object is located on a task planning path of the robot according to the orientation information of the peripheral object, use the object located on the planning path as an obstacle, and use an obstacle avoidance algorithm to avoid the obstacle;
a weather mode module 503, configured to perform adaptive switching among a normal mode, a rainproof mode, and an antiskid mode according to weather information of a location where the current task is executed;
a path adjusting module 504, configured to update and adjust the mission planning path according to real-time road condition information of the mission planning path, so as to avoid a heavily congested road section, a construction road section, and/or a forbidden road section;
and the real-time state updating module 505 is configured to run the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, record a motion track and a motion timeline in the built-in real-time navigation map, and send the motion track and the motion timeline to the cloud server to update the display of the logistics task real-time state of the robot, where the real-time state includes location positioning of the robot and predicted task completion time.
The robot front-end information-based space monitoring system provided by the embodiment of the application and the robot front-end information-based space monitoring system provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as methods adopted, operated or realized by application programs stored in the space monitoring system.
The embodiment of the present application further provides an electronic device corresponding to the space monitoring system based on the robot front-end information provided in the foregoing embodiment, so as to implement the above space monitoring system based on the robot front-end information. The embodiments of the present application are not limited.
Referring to fig. 4, a schematic diagram of an electronic device provided in some embodiments of the present application is shown. As shown in fig. 4, the electronic device 2 includes: a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the space monitoring system based on the robot front-end information provided by any one of the foregoing embodiments when executing the computer program.
The Memory 201 may include a Random Access Memory (RAM) and a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be 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. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic device provided by the embodiment of the application and the space monitoring system based on the front-end information of the robot provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Referring to fig. 5, the computer readable storage medium is an optical disc 30, on which a computer program (i.e., a program product) is stored, and when the computer program is executed by a processor, the computer readable storage medium executes the robot front-end information-based space monitoring system provided in any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the robot front-end information-based space monitoring system provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system is apparent from the description above. Moreover, this application is not intended to refer to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore, may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application. The present application may also be embodied as apparatus or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A space monitoring method based on robot front-end information is characterized by comprising the following steps:
the method comprises the steps that azimuth information of surrounding objects is obtained in real time through a sensor on a robot, weather information of the place where a current task is executed is obtained through an internet module, and real-time road condition information of a task planning path is obtained through a built-in real-time navigation map;
judging whether the peripheral object is positioned on a task planning path of the robot or not according to the azimuth information of the peripheral object, taking the object positioned on the planning path as an obstacle, and avoiding the obstacle by using an obstacle avoidance algorithm;
according to the weather information of the place where the current task is executed, adaptively switching among a common mode, a rainproof mode and an antiskid mode;
updating and adjusting the mission planning path according to the real-time road condition information of the mission planning path so as to avoid a severely congested road section, a construction road section and/or a forbidden road section;
and running the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to a cloud server so as to update the display of the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time.
2. The method of claim 1,
the real-time orientation information of surrounding objects is obtained through a sensor on the robot in real time, the weather information of the place where the current task is executed is obtained through an internet module, and the real-time road condition information of a task planning path is obtained through a built-in real-time navigation map, and the method comprises the following steps:
the sensor is a laser sensor, the laser sensor is arranged in the robot to sense the dynamic state of the surrounding object, and the angle, the direction and the distance of the surrounding object from the position of the robot are calculated;
acquiring geographical positioning information of a place where a current task is executed through a GPS positioning module, inputting the geographical positioning information into built-in internet weather software, and outputting the weather information of the geographical positioning information through the internet weather software;
and inputting the starting point and end point positioning information of the mission planning path into a built-in real-time navigation map to obtain the mission planning path, connecting the mission planning path with the Internet through an Internet access module, and downloading and loading real-time road condition information in real time.
3. The method according to claim 1 or 2,
the method for judging whether the peripheral object is positioned on a task planning path of the robot or not according to the azimuth information of the peripheral object and taking the object positioned on the task planning path as an obstacle comprises the following steps:
inputting the angle, the direction and the distance of the peripheral object from the position of the robot into the built-in real-time navigation map so as to mark the position of the peripheral object in the map;
loading a mission planning path in the map;
and judging whether the positions of the surrounding objects in the map have an overlapping area with the mission planning path or not, determining the surrounding objects with the overlapping area as obstacles, and determining the surrounding objects without the overlapping area as non-obstacles.
4. The method of claim 3,
avoiding the obstacle using an obstacle avoidance algorithm, comprising:
establishing a two-dimensional grid map by using the azimuth information of the known obstacles;
establishing a global coordinate system according to the position of the robot in the two-dimensional grid map, and setting a starting point and an end point of the robot;
determining the shortest path between the starting point and the end point by adopting a jumping point search algorithm;
the shortest path comprises a plurality of local target points which are sequentially connected;
in the process of controlling the robot to move to each local target point, a local obstacle avoidance algorithm is adopted to avoid dynamic obstacles, and the method comprises the following steps: detecting the dynamic obstacle in real time, and acquiring the real-time distance and the azimuth angle of the dynamic obstacle relative to the mobile robot; controlling the robot to rotate according to the real-time distance and the azimuth angle so as to avoid the dynamic obstacle, and the method comprises the following steps: presetting a plurality of distance range thresholds, wherein each distance range threshold corresponds to one obstacle avoidance rotating angle; determining a distance range threshold value and a corresponding obstacle avoidance rotation angle of the real-time distance according to the real-time distance; and controlling the mobile robot to rotate the obstacle avoidance rotating angle so as to avoid the dynamic obstacle.
5. The method of claim 4,
the method for adaptively switching among a common mode, a rainproof mode and an antiskid mode according to the weather information of the place where the current task is executed comprises the following steps:
acquiring a current working mode, wherein the types of the current working mode comprise a common mode, a rainproof mode and an antiskid mode;
if the weather information is rainy days, switching the current working mode into a rainproof mode;
if the weather information is snow or the current air temperature is lower than zero, switching the current working mode into an anti-skidding mode;
and if the weather information is not rainy or snowy days and the current air temperature is higher than zero degree, switching the current working mode into a common mode.
6. The method of claim 5,
the updating and adjusting of the mission planning path according to the real-time road condition information of the mission planning path to avoid a severely congested road section, a construction road section and/or a sealed road section comprises the following steps:
if the real-time road condition information of the mission planning path does not comprise a heavily congested road section, a construction road section and/or a forbidden road section, continuing to operate the robot according to the current mission planning path;
and if the real-time road condition information of the mission planning path comprises a severe congestion road section, a construction road section and/or a block road section, indicating the built-in real-time navigation map to be switched to a congestion avoiding, construction and block mode, and updating and adjusting the mission planning path to avoid the severe congestion road section, the construction road section and/or the block road section.
7. The method of claim 6,
the method comprises the following steps of operating the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to the cloud server so as to update the display of the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time, and the method comprises the following steps:
after the obstacle avoidance algorithm is used for avoiding the obstacles, the current working mode is switched according to weather information, the mission planning path is updated and adjusted according to real-time road condition information of the mission planning path, the robot is operated, and the historical movement track and the movement time line of the robot are recorded in the built-in real-time navigation map;
sending the historical motion trail and the motion timeline to a cloud server;
the cloud server loads the historical motion trail and the motion time line to a current logistics task to update the real-time state of the logistics task of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time;
and sending the real-time state of the logistics task to a mobile client for displaying at the mobile client.
8. A space monitoring system based on robot front-end information, comprising:
the front-end information acquisition module is used for acquiring the azimuth information of surrounding objects in real time through a sensor on the robot, acquiring the weather information of the place where the current task is executed through the internet access module, and acquiring the real-time road condition information of a task planning path through a built-in real-time navigation map;
the obstacle avoidance module is used for judging whether the peripheral object is positioned on a task planning path of the robot or not according to the azimuth information of the peripheral object, taking the object positioned on the planning path as an obstacle, and avoiding the obstacle by using an obstacle avoidance algorithm;
the weather mode module is used for adaptively switching among a common mode, a rainproof mode and an antiskid mode according to weather information of the place where the current task is executed;
the path adjusting module is used for updating and adjusting the mission planning path according to the real-time road condition information of the mission planning path so as to avoid a severely congested road section, a construction road section and/or a forbidden road section;
and the real-time state updating module is used for operating the robot according to the obstacle avoidance algorithm, the current working mode and the task planning path, recording a motion track and a motion time line in the built-in real-time navigation map, and sending the motion track and the motion time line to the cloud server so as to update the display of the logistics task real-time state of the robot, wherein the real-time state comprises the location positioning of the robot and the predicted task completion time.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the method according to any of claims 1-7.
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