CN113467468A - Embedded robot intelligent obstacle avoidance system and method - Google Patents
Embedded robot intelligent obstacle avoidance system and method Download PDFInfo
- Publication number
- CN113467468A CN113467468A CN202110836637.4A CN202110836637A CN113467468A CN 113467468 A CN113467468 A CN 113467468A CN 202110836637 A CN202110836637 A CN 202110836637A CN 113467468 A CN113467468 A CN 113467468A
- Authority
- CN
- China
- Prior art keywords
- robot
- environment
- tag
- label
- obstacle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000000007 visual effect Effects 0.000 claims abstract description 19
- 238000013500 data storage Methods 0.000 claims abstract description 4
- 238000004458 analytical method Methods 0.000 claims description 37
- 238000011156 evaluation Methods 0.000 claims description 26
- 238000005516 engineering process Methods 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 7
- 238000003891 environmental analysis Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 abstract description 7
- 230000004888 barrier function Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
- G05D1/0251—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0285—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using signals transmitted via a public communication network, e.g. GSM network
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Aviation & Aerospace Engineering (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Electromagnetism (AREA)
- Acoustics & Sound (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses an embedded-based intelligent robot obstacle avoidance system and method, relates to the technical field of robot control, and solves the technical problem that the accuracy of a planned path is low because a robot cannot acquire obstacle information of a visual field blind area in the moving process in the existing scheme; the system comprises an embedded processor, a data storage module, a terminal control module, an environment acquisition module, an execution control module and a visual field acquisition module; the invention is provided with the environment acquisition module and the visual field acquisition module, so that the obstacles in the visual field of the robot can be identified, the obstacles in the visual field blind area of the robot can be identified, the obstacles in the moving process of the robot are considered in all directions, and the accuracy and timeliness of obstacle avoidance of the robot are improved; the execution control module is arranged, so that the speed of the robot can be adjusted in real time according to the movement control tag in the moving process of the robot, and the probability of collision between the robot and an obstacle is reduced.
Description
Technical Field
The invention belongs to the field of robot control, relates to an intelligent obstacle avoidance technology of a robot, and particularly relates to an intelligent obstacle avoidance system and method based on an embedded robot.
Background
With the continuous development of science and technology, the robot can realize the functions of voice interaction, visual detection, obstacle avoidance and the like. At present, the robot can avoid obstacles through a vibration sensor or an ultrasonic sensor, and can also realize obstacles through technologies such as visual detection, voice interaction and the like, but the two schemes cannot achieve the balance between cost and accuracy.
The invention patent with publication number CN112171667A discloses an intelligent obstacle avoidance system and an obstacle avoidance method for a picking robot based on an embedded type, wherein the system comprises an embedded processor, a terminal control system, a sensor, a camera and a robot driving device, and the sensor and an image recognition technology are combined to sense a working environment, so that the obstacle avoidance control of the robot is realized.
According to the scheme, monitoring can be implemented, the accuracy of robot path planning is improved, and the time cost is reduced, but when the path planning is carried out, only obstacle information which can be seen by a robot 'view field' is considered, the obstacle information in a robot view field blind area cannot be predicted, and accidents are easy to happen or the working efficiency is reduced; therefore, a system and a method for avoiding obstacles intelligently by a robot are needed.
Disclosure of Invention
The invention provides an embedded-based intelligent robot obstacle avoidance system and method, which are used for solving the technical problem that the accuracy of a planned path is low because the robot cannot acquire obstacle information of a visual field blind area in the moving process in the existing scheme.
The purpose of the invention can be realized by the following technical scheme: an intelligent robot obstacle avoidance system based on an embedded type comprises an embedded processor and a data storage module; the data storage module is used for storing data generated in the running process of the system;
the embedded processor is respectively in communication and/or electrical connection with the terminal control module, the environment acquisition module and the execution control module; the environment acquisition module is in communication and/or electrical connection with the visual field acquisition module;
the terminal control module is used for sending the target position to the embedded processor; the visual field acquisition module is in communication and/or electrical connection with a high-definition camera in a robot work area;
when the embedded processor receives the target position, planning a moving path of the robot by combining the current position of the robot, and marking the path as a planned path;
the environment acquisition module acquires environment data through an environment sensor and analyzes the environment data to acquire an environment analysis label; wherein the environmental analysis tags comprise a first type of tag and a second type of tag;
when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module; when the environment analysis label is a second type label, the field of view blind area analysis is carried out by combining a field of view acquisition module, and a mobile control label is set;
and controlling the robot to operate according to the mobile control label through the execution control module.
Preferably, the obtaining of the environmental analysis tag includes:
acquiring a planned path, acquiring a real-time image of the advancing direction of the robot through a high-definition camera, and acquiring an obstacle in the real-time image through an image recognition technology;
when no obstacle exists in the real-time image or the obstacle is not in the preset range of the robot, setting the environment analysis label as 2, namely a second type label;
when the obstacle of the real-time image is within the preset range of the robot, the environment analysis tag is set to be 1, namely the first type tag.
Preferably, the preset range is a safe space for the robot to move, and the shape of the preset range includes a circle and a sphere.
Preferably, when the environmental analysis tag is a second type tag, the view blind zone analysis includes:
dividing the planned path into a plurality of path segments;
respectively acquiring the vibration intensity and the sound intensity of the next path segment in the advancing direction through a vibration sensor and an ultrasonic sensor, and respectively marking the vibration intensity and the sound intensity as ZQ and SQ; obtaining an environment evaluation coefficient HPX through a formula HPX ═ alpha 1 × ZQ + alpha 2 × SQ; wherein both alpha 1 and alpha 2 are proportionality coefficients larger than 0;
when the environment evaluation coefficient HPX is larger than the evaluation coefficient threshold value, acquiring a view image through a high-definition camera in a robot working area; wherein the evaluation coefficient threshold is a real number greater than 0;
the method comprises the steps of obtaining obstacles of a visual field image through an image recognition technology, judging that the obstacles in a path segment can affect the movement of the robot when the distance between the obstacles and a preset range boundary is smaller than a distance threshold, and setting a movement control label corresponding to the path segment to be 1, otherwise, setting the movement control label corresponding to the path segment to be 2.
Preferably, the execution control module controls the robot to move, and includes:
the method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2;
when moving the control tag 1, the moving speed of the robot is adjusted through the corresponding path segment.
Preferably, the environment sensor comprises at least two high-definition cameras, a vibration sensor and an ultrasonic sensor; the robot comprises a robot body, a plurality of high-definition cameras in the environment sensor, a vibration sensor and an ultrasonic sensor, wherein the plurality of high-definition cameras in the environment sensor are arranged on the robot body, and the vibration sensor and the ultrasonic sensor are arranged on the robot body or a moving path of the robot body.
Preferably, the data processing module is respectively in communication and/or electrical connection with the embedded processor and the terminal control module; the environment acquisition module is respectively communicated and/or electrically connected with the terminal control module and the execution control module.
An intelligent obstacle avoidance method based on an embedded robot comprises the following steps:
sending the target position to an embedded processor through a terminal control module, and acquiring a planned path by the embedded processor according to the target position and the current position of the robot;
the environment acquisition module acquires a real-time image of the advancing direction of the robot and identifies obstacles in the real-time image; setting an environment analysis tag according to the obstacle identification result; when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module;
when the environment analysis label is a second type label, dividing the planned path into a plurality of path segments; respectively acquiring the vibration intensity and the sound intensity of the next path segment in the advancing direction through a vibration sensor and an ultrasonic sensor, and respectively marking the vibration intensity and the sound intensity as ZQ and SQ; obtaining an environment evaluation coefficient HPX through a formula HPX ═ alpha 1 × ZQ + alpha 2 × SQ;
when the environment evaluation coefficient HPX is larger than the evaluation coefficient threshold value, acquiring a view image through a high-definition camera in a robot working area; acquiring obstacles of a visual field image through an image recognition technology, judging that the obstacles in the path segment can influence the movement of the robot when the distance between the obstacles and the boundary of the preset range is smaller than a distance threshold value, and setting the movement control label corresponding to the path segment to be 1, otherwise, setting the movement control label corresponding to the path segment to be 2; wherein the evaluation coefficient threshold is a real number greater than 0;
the method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2; when moving the control tag 1, the moving speed of the robot is adjusted through the corresponding path segment.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with an environment acquisition module and a visual field acquisition module; the environment acquisition module acquires a real-time image of the advancing direction of the robot and identifies obstacles in the real-time image; setting an environment analysis tag according to the obstacle identification result; when the environment analysis tag is a first type tag, performing obstacle alarm, when the environment analysis tag is a second type tag, acquiring an environment evaluation coefficient of the path segment, when the environment evaluation coefficient is greater than an evaluation coefficient threshold value, acquiring a view image through a high-definition camera of a working area of the robot, when the distance between an obstacle and a preset range boundary is smaller than a distance threshold value, judging that the obstacle in the path segment can affect the movement of the robot, setting the movement control tag corresponding to the path segment to be 1, and otherwise, setting the movement control tag corresponding to the path segment to be 2; the setting of environment collection module and field of vision collection module can discern the barrier in the robot field of vision, can discern the barrier of the field of vision blind area of robot again, and the barrier of all-round robot removal in-process of considering has improved the accuracy and the promptness that the barrier was kept away to the robot.
2. The invention is provided with an execution control module; the method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2; when the control tag 1 is moved, adjusting the moving speed of the robot to pass through the corresponding path segment; the execution control module can adjust the speed of the robot in real time according to the mobile control label in the moving process of the robot, and the probability of collision between the robot and an obstacle is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1, an intelligent obstacle avoidance system based on an embedded robot includes:
sending the target position to an embedded processor through a terminal control module, and acquiring a planned path by the embedded processor according to the target position and the current position of the robot; the terminal control module comprises a smart phone, a tablet personal computer and a common controller.
The path planning in the embodiment is planned according to a drawing or three-dimensional modeling of a working area, so that accidents caused by the fact that the robot touches objects such as a storage rack in the moving process are avoided, and the path planning algorithm comprises but is not limited to a simulated annealing algorithm, an artificial potential field method, a fuzzy logic algorithm, a tabu search algorithm, a C space method, a grid method, a free space method, a voronoi diagram method, an ant colony algorithm, a neural network algorithm, a particle swarm algorithm and a genetic algorithm; in other preferred embodiments, when no object such as a storage rack exists in the working area, the planned path can be directly obtained according to the path planning algorithm without referring to the working area drawing or model.
The environment acquisition module acquires a real-time image of the advancing direction of the robot and identifies obstacles in the real-time image; setting an environment analysis tag according to the obstacle identification result; when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module; and when the environment analysis label is a second type label, the field of view blind area analysis is carried out by combining the field of view acquisition module, and a mobile control label is set. In this embodiment, a real-time image of a forward direction in a planned path of the robot is first obtained, when an obstacle exists in the real-time image, the environment analysis tag is set as a first type tag, and obstacle early warning is performed through the terminal control module, where the obstacle may be a static obstacle such as a scaffold and stacked goods, or a dynamic obstacle such as a moving cart and a worker. The real-time image in this embodiment is obtained through the high definition digtal camera that sets up on the robot body.
And when the environment analysis tag is a second type tag, dividing the planned path into a plurality of path segments, acquiring environment evaluation coefficients of the path segments, and setting a mobile control tag by combining the view image. In this embodiment, it is mainly considered that when the robot moves forward along the planned path, the obstacle is moved out from between the two storage racks, so that the obstacle is found by the high-definition camera in the working area, and the movement control tag is set according to the distance between the obstacle and the preset range boundary. The preset range in the embodiment is specifically a regular range including a robot, and may be a circle or a sphere; when the preset range is a circle or a sphere, the center of gravity of the robot can be used as the center of a circle or the center of a sphere, and a fixed value, such as 1 meter, is used as the radius to obtain a corresponding circle or sphere; but may also be rectangular or cubic.
And acquiring a movement control label of a next path segment in the advancing direction of the robot in real time, and controlling the moving speed of the robot according to the movement control label. In the embodiment, the moving speed is controlled to be accelerated, decelerated and stopped, so that the robot can be timely and effectively prevented from colliding with the obstacle.
Next, the symbols of the technical solutions in the present application are embodied to further explain the workflow:
sending the target position to an embedded processor through a terminal control module, acquiring a planned path according to the target position and the current position of the robot by the embedded processor, and sending the planned path to an environment acquisition module;
acquiring a real-time image of the advancing direction of the robot when the robot advances according to a planned path through a high-definition camera, identifying obstacles in the real-time image through an image identification technology, setting an environment analysis label as a second type label when the real-time image has no obstacles or the obstacles are not in a preset range of the robot, and setting the environment analysis label as a first type label when the obstacles in the real-time image are in the preset range of the robot; when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module;
when the environment analysis label is a second type label, dividing the planned path into a plurality of path segments; respectively acquiring the vibration intensity and the sound intensity of the next path segment in the advancing direction through a vibration sensor and an ultrasonic sensor, wherein if the vibration intensity is 1, the sound intensity is 1; then the environment assessment coefficient HPX ═ 2 is obtained by the formula HPX ═ 1 × 1+ 1 × 1;
when the evaluation coefficient threshold is 1, the environment evaluation coefficient HPX is larger than the evaluation coefficient threshold, and a high-definition camera in a robot working area is used for acquiring a view image; the high-definition cameras in this embodiment are disposed on the top of the working area or on each moving track.
Acquiring an obstacle of a visual field image through an image recognition technology, and when the distance between the obstacle and a preset range boundary is smaller than a distance threshold, judging that the obstacle in a path segment can influence the movement of the robot, wherein the distance between the obstacle and the preset range boundary in the embodiment is the shortest distance between the obstacle and the preset range boundary, in other optimal embodiments, the distance between the obstacle and the center of the robot can also be the distance between the obstacle and the center of the robot, and if the distance between the obstacle and the preset range boundary is 1 and the distance threshold is 2, judging that the obstacle in the path segment can influence the movement of the robot, and setting a movement control label corresponding to the path segment to be 1; when the distance threshold is 0.5, the movement control flag of the corresponding path segment is set to 2.
The method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2; when moving the control tag 1, the moving speed of the robot is adjusted through the corresponding path segment. The set speed in this embodiment is an optimal speed for the robot to operate, and the adjustment of the moving speed of the robot includes:
marking the distance from the obstacle to the boundary of the preset range of the robot as JL;
the moving speed SD can be formulated
Obtaining, wherein beta 1 is a proportionality coefficient greater than 0; when the distance JL is smaller, the moving speed of the robot is slower.
The moving speed is obtained in real time and is adjustable, the obstacle avoidance of the robot can be guaranteed to the maximum extent, and the moving safety of the robot is improved.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
sending the target position to an embedded processor through a terminal control module, and acquiring a planned path by the embedded processor according to the target position and the current position of the robot;
the environment acquisition module acquires a real-time image of the advancing direction of the robot and identifies obstacles in the real-time image; setting an environment analysis tag according to the obstacle identification result; when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module;
when the environment analysis label is a second type label, dividing the planned path into a plurality of path segments; respectively acquiring the vibration intensity and the sound intensity of the next path segment in the advancing direction through a vibration sensor and an ultrasonic sensor, and acquiring an environment evaluation coefficient through a formula;
when the environment evaluation coefficient is larger than the evaluation coefficient threshold value, acquiring a view image through a high-definition camera in a robot working area; acquiring obstacles of a visual field image through an image recognition technology, judging that the obstacles in the path segment can influence the movement of the robot when the distance between the obstacles and the boundary of the preset range is smaller than a distance threshold value, and setting the movement control label corresponding to the path segment to be 1, otherwise, setting the movement control label corresponding to the path segment to be 2;
the method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2; when moving the control tag 1, the moving speed of the robot is adjusted through the corresponding path segment.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. An embedded-based intelligent robot obstacle avoidance system comprises an embedded processor and a data storage module, and is characterized in that the embedded processor is respectively in communication and/or electrical connection with a terminal control module, an environment acquisition module and an execution control module; the environment acquisition module is in communication and/or electrical connection with the visual field acquisition module;
the terminal control module is used for sending the target position to the embedded processor; the visual field acquisition module is in communication and/or electrical connection with a high-definition camera in a robot work area;
when the embedded processor receives the target position, planning a moving path of the robot by combining the current position of the robot, and marking the path as a planned path;
the environment acquisition module acquires environment data through an environment sensor and analyzes the environment data to acquire an environment analysis label; wherein the environmental analysis tags comprise a first type of tag and a second type of tag;
when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module; when the environment analysis label is a second type label, the field of view blind area analysis is carried out by combining a field of view acquisition module, and a mobile control label is set;
and controlling the robot to operate according to the mobile control label through the execution control module.
2. The embedded robot-based intelligent obstacle avoidance system of claim 1, wherein the obtaining of the environmental analysis tag comprises:
acquiring a planned path, acquiring a real-time image of the advancing direction of the robot through a high-definition camera, and acquiring an obstacle in the real-time image through an image recognition technology;
when no obstacle exists in the real-time image or the obstacle is not in the preset range of the robot, setting the environment analysis label as 2, namely a second type label;
when the obstacle of the real-time image is within the preset range of the robot, the environment analysis tag is set to be 1, namely the first type tag.
3. The embedded robot-based intelligent obstacle avoidance system of claim 2, wherein the preset range is a safe space for robot movement, and the shape of the preset range comprises a circle and a sphere.
4. The embedded robot-based intelligent obstacle avoidance system of claim 2, wherein when the environment analysis tag is a second type tag, the view blind area analysis comprises:
dividing the planned path into a plurality of path segments;
respectively acquiring the vibration intensity and the sound intensity of the next path segment in the advancing direction through a vibration sensor and an ultrasonic sensor, and respectively marking the vibration intensity and the sound intensity as ZQ and SQ; obtaining an environment evaluation coefficient HPX through a formula HPX ═ alpha 1 × ZQ + alpha 2 × SQ; wherein both alpha 1 and alpha 2 are proportionality coefficients larger than 0;
when the environment evaluation coefficient HPX is larger than the evaluation coefficient threshold value, acquiring a view image through a high-definition camera in a robot working area; wherein the evaluation coefficient threshold is a real number greater than 0;
acquiring obstacles of a visual field image through an image recognition technology, judging that the obstacles in the path segment can influence the movement of the robot when the distance between the obstacles and the boundary of the preset range is smaller than a distance threshold value, and setting the movement control label corresponding to the path segment to be 1, otherwise, setting the movement control label corresponding to the path segment to be 2; wherein the distance threshold is a real number greater than 0.
5. The embedded robot intelligent obstacle avoidance system according to claim 4, wherein the execution control module controls the robot to move, and the system comprises:
the method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2;
when moving the control tag 1, the moving speed of the robot is adjusted through the corresponding path segment.
6. An intelligent robot obstacle avoiding method based on an embedded type is characterized by comprising the following steps:
sending the target position to an embedded processor through a terminal control module, and acquiring a planned path by the embedded processor according to the target position and the current position of the robot;
the environment acquisition module acquires a real-time image of the advancing direction of the robot and identifies obstacles in the real-time image; setting an environment analysis tag according to the obstacle identification result; when the environment analysis tag is the first type tag, performing obstacle alarm through the terminal control module;
when the environment analysis label is a second type label, dividing the planned path into a plurality of path segments; respectively acquiring the vibration intensity and the sound intensity of the next path segment in the advancing direction through a vibration sensor and an ultrasonic sensor, and respectively marking the vibration intensity and the sound intensity as ZQ and SQ; obtaining an environment evaluation coefficient HPX through a formula HPX ═ alpha 1 × ZQ + alpha 2 × SQ;
when the environment evaluation coefficient HPX is larger than the evaluation coefficient threshold value, acquiring a view image through a high-definition camera in a robot working area; acquiring obstacles of a visual field image through an image recognition technology, judging that the obstacles in the path segment can influence the movement of the robot when the distance between the obstacles and the boundary of the preset range is smaller than a distance threshold value, and setting the movement control label corresponding to the path segment to be 1, otherwise, setting the movement control label corresponding to the path segment to be 2; wherein the evaluation coefficient threshold is a real number greater than 0;
the method comprises the steps of acquiring a movement control label of a path segment in real time, and controlling the robot to move according to a planned path according to the set speed of the robot when the movement control label is 2; when moving the control tag 1, the moving speed of the robot is adjusted through the corresponding path segment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110836637.4A CN113467468B (en) | 2021-07-23 | 2021-07-23 | Intelligent robot obstacle avoidance system and method based on embedded robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110836637.4A CN113467468B (en) | 2021-07-23 | 2021-07-23 | Intelligent robot obstacle avoidance system and method based on embedded robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113467468A true CN113467468A (en) | 2021-10-01 |
| CN113467468B CN113467468B (en) | 2024-03-29 |
Family
ID=77882057
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110836637.4A Active CN113467468B (en) | 2021-07-23 | 2021-07-23 | Intelligent robot obstacle avoidance system and method based on embedded robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113467468B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114217620A (en) * | 2021-12-15 | 2022-03-22 | 常州信息职业技术学院 | Intelligent obstacle avoidance control system and method for wheeled robot |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080040040A1 (en) * | 2006-08-08 | 2008-02-14 | Takanori Goto | Obstacle avoidance method and obstacle-avoidable mobile apparatus |
| JP2008234400A (en) * | 2007-03-22 | 2008-10-02 | Toyota Motor Corp | Mobile robot and robot movement control method |
| WO2015024407A1 (en) * | 2013-08-19 | 2015-02-26 | 国家电网公司 | Power robot based binocular vision navigation system and method based on |
| CN104865965A (en) * | 2015-05-20 | 2015-08-26 | 深圳市锐曼智能装备有限公司 | Barrier-avoidance control method combining depth camera and supersonic wave for robot and system thereof |
| CN106003043A (en) * | 2016-06-20 | 2016-10-12 | 先驱智能机械（深圳）有限公司 | Obstacle avoidance method and obstacle avoidance system of mechanical arm |
| CN207488823U (en) * | 2017-06-30 | 2018-06-12 | 炬大科技有限公司 | A kind of mobile electronic device |
| CN110502019A (en) * | 2019-09-06 | 2019-11-26 | 北京云迹科技有限公司 | A kind of barrier-avoiding method and device of Indoor Robot |
| US20200331148A1 (en) * | 2018-01-24 | 2020-10-22 | Qfeeltech (Beijing) Co., Ltd. | Cleaning robot |
| JP6884312B1 (en) * | 2020-08-20 | 2021-06-09 | 上海姜歌机器人有限公司Ｄｊａｎｇｏ Ｒｏｂｏｔｉｃｓ Ｓｈａｎｇｈａｉ Ｃｏ．，Ｌｔｄ． | Robot obstacle avoidance processing method, device, robot |
-
2021
- 2021-07-23 CN CN202110836637.4A patent/CN113467468B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080040040A1 (en) * | 2006-08-08 | 2008-02-14 | Takanori Goto | Obstacle avoidance method and obstacle-avoidable mobile apparatus |
| JP2008234400A (en) * | 2007-03-22 | 2008-10-02 | Toyota Motor Corp | Mobile robot and robot movement control method |
| WO2015024407A1 (en) * | 2013-08-19 | 2015-02-26 | 国家电网公司 | Power robot based binocular vision navigation system and method based on |
| CN104865965A (en) * | 2015-05-20 | 2015-08-26 | 深圳市锐曼智能装备有限公司 | Barrier-avoidance control method combining depth camera and supersonic wave for robot and system thereof |
| CN106003043A (en) * | 2016-06-20 | 2016-10-12 | 先驱智能机械（深圳）有限公司 | Obstacle avoidance method and obstacle avoidance system of mechanical arm |
| CN207488823U (en) * | 2017-06-30 | 2018-06-12 | 炬大科技有限公司 | A kind of mobile electronic device |
| US20200331148A1 (en) * | 2018-01-24 | 2020-10-22 | Qfeeltech (Beijing) Co., Ltd. | Cleaning robot |
| CN110502019A (en) * | 2019-09-06 | 2019-11-26 | 北京云迹科技有限公司 | A kind of barrier-avoiding method and device of Indoor Robot |
| JP6884312B1 (en) * | 2020-08-20 | 2021-06-09 | 上海姜歌机器人有限公司Ｄｊａｎｇｏ Ｒｏｂｏｔｉｃｓ Ｓｈａｎｇｈａｉ Ｃｏ．，Ｌｔｄ． | Robot obstacle avoidance processing method, device, robot |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114217620A (en) * | 2021-12-15 | 2022-03-22 | 常州信息职业技术学院 | Intelligent obstacle avoidance control system and method for wheeled robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113467468B (en) | 2024-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mohammed et al. | Active collision avoidance for human–robot collaboration driven by vision sensors | |
| CN106054208B (en) | The anticollision device, collision-prevention device of the recognition methods of multi-line laser radar vehicle target and automobile | |
| US10335964B2 (en) | Method and system for controlling robot | |
| AU2021266203B2 (en) | Semantic laser-based multilevel obstacle avoidance system and method for mobile robot | |
| CN114885133B (en) | Depth image-based equipment safety real-time monitoring method and system and related equipment | |
| EP4159643A1 (en) | Cargo box extraction and device, system, robot, and storage medium | |
| CN114140503A (en) | Power distribution network dangerous area identification device and method based on deep learning | |
| CN113467468B (en) | Intelligent robot obstacle avoidance system and method based on embedded robot | |
| Fang et al. | Computer vision based obstacle detection and target tracking for autonomous vehicles | |
| Kenk et al. | Human-aware Robot Navigation in Logistics Warehouses. | |
| CN115509231A (en) | Robot following obstacle avoidance method and device and storage medium | |
| CN111026121A (en) | Multi-level three-dimensional obstacle avoidance control method and device for intelligent sweeper | |
| Abu-Jassar et al. | The Optical Flow Method and Graham’s Algorithm Implementation Features for Searching for the Object Contour in the Mobile Robot’s Workspace | |
| CN114550138A (en) | Fork truck collision avoidance system based on binocular vision | |
| Nasti et al. | Obstacle avoidance during robot navigation in dynamic environment using fuzzy controller | |
| CN113467504B (en) | Method, system, equipment and storage medium for controlling flight stability of aircraft | |
| CN116038684A (en) | Robot collision early warning method based on vision | |
| CN116330344A (en) | Cooperative robot collision detection method based on supervised learning support vector machine | |
| Celinski et al. | Achieving efficient data fusion through integration of sensory perception control and sensor fusion | |
| CN114200938B (en) | Voice reminding method and device for leading surrounding obstacle of robot and robot | |
| CN117226810B (en) | Rope load parallel robot and obstacle avoidance method, device and storage medium thereof | |
| CN115091463A (en) | Wiring robot speed self-adaptive control method oriented to human-computer cooperation safety | |
| WO2023173330A1 (en) | Flight control method and apparatus for unmanned aerial vehicle, unmanned aerial vehicle, and storage medium | |
| CN114037952A (en) | Image detection system based on intelligent logistics and use method thereof | |
| Li et al. | Anti-collision Motion Control of the Industrial Manipulator Based on 3D Point Cloud |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |