CN112060106A - Inspection system of inspection robot for mine and inspection method of inspection robot group - Google Patents
Inspection system of inspection robot for mine and inspection method of inspection robot group Download PDFInfo
- Publication number
- CN112060106A CN112060106A CN202010954150.1A CN202010954150A CN112060106A CN 112060106 A CN112060106 A CN 112060106A CN 202010954150 A CN202010954150 A CN 202010954150A CN 112060106 A CN112060106 A CN 112060106A
- Authority
- CN
- China
- Prior art keywords
- inspection
- module
- agent
- robot
- inspection robot
- 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.)
- Pending
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 173
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 claims abstract description 53
- 238000003708 edge detection Methods 0.000 claims abstract description 43
- 238000004891 communication Methods 0.000 claims abstract description 17
- 238000005065 mining Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 17
- 230000003993 interaction Effects 0.000 claims abstract description 12
- 230000008859 change Effects 0.000 claims abstract description 5
- 238000005259 measurement Methods 0.000 claims abstract description 4
- 230000009466 transformation Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 4
- 230000004888 barrier function Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 239000003795 chemical substances by application Substances 0.000 description 46
- 230000036544 posture Effects 0.000 description 9
- 230000006870 function Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 239000003245 coal Substances 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000005538 encapsulation Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 241001347978 Major minor Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
Abstract
The invention belongs to the technical field of mine inspection robots, and particularly relates to an inspection system of a mine inspection robot and an inspection method of inspection robot groups. The main control unit comprises an acquisition module, a communication module, an infrared distance measurement module, an edge detection module, an image identification module and a multi-agent module. The multi-agent module comprises a position agent, a posture agent, a speed agent, an obstacle avoidance agent, a path planning agent and a coordination agent. The monitoring unit mainly comprises an image processing module, a storage module, a display module, a power supply module, a man-machine interaction module and a communication module. The main control unit helps the robot to complete the inspection task through data acquisition, image processing and recognition and path planning. The monitoring unit is used for remotely issuing a patrol command, an emergency notice and a change command to the robot. A plurality of mining patrol robots cooperatively avoid obstacles and patrol route planning, and patrol efficiency and safety degree can be effectively improved.
Description
Technical Field
The invention belongs to the technical field of mine inspection robots, and particularly relates to an inspection system of a mine inspection robot and an inspection method of inspection robot groups.
Background
The coal mining working face has complex environment (large noise, low visibility, dark and moist, easy fire, flood, gas explosion and the like), large operation difficulty and high labor intensity, so the coal mining field is one of the works with the highest danger index at present. Along with the continuous promotion of the mining concepts of digital mining, intelligent mining, "less humanization" and "no humanization", the complexity of mining equipment is also increased. Therefore, there is a higher demand for real-time monitoring of downhole safety.
At present, safety inspection in the underground is mainly carried out in a manual mode, and the mode has many defects, for example, the problems of labor intensity increase of workers, low efficiency, potential safety hazard increase, strong inspection subjectivity, high danger coefficient of special stations and the like are gradually obvious in manual inspection, so that the mining inspection robot is produced by operation.
Due to the complexity of the working environment of the mining inspection robot, how to reasonably avoid the obstacle becomes a current difficulty. Meanwhile, as the routing inspection task is heavy and the workload is large, a single routing inspection robot cannot meet the working requirement, and a plurality of routing inspection robots are required to work cooperatively, so that the scientific problems of how to reasonably distribute the working task and the optimal path routing inspection and the like need to be solved urgently.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the inspection system of the inspection robot for the mine and the inspection method of the inspection robot group.
The invention is realized by the following technical scheme: a polling system of a mining polling robot comprises a main control unit for helping the polling robot to complete a polling task and a monitoring unit for monitoring relevant information of the polling robot and polling operation conditions in real time; the main control unit is arranged on the inspection robot, and the monitoring unit is positioned in the ground dispatching center;
the main control unit includes:
the collection module is used for collecting environmental information around the inspection robot;
the edge detection module is used for carrying out edge detection on the surrounding environment according to the information acquired by the acquisition module and determining the edge information of the barrier;
the image identification module is used for identifying according to the edge information of the obstacle after edge detection and determining the type of the obstacle;
the infrared distance measurement module determines the distance between the inspection robot and the obstacle according to the edge information and the type of the obstacle;
the multi-agent module comprises a position agent, a posture agent, a speed agent, an obstacle avoidance agent and a path planning agent, wherein the position agent, the posture agent and the speed agent in the multi-agent module are used for acquiring the position, the posture and the speed information of the inspection robot, the obstacle avoidance agent is used for avoiding obstacles of the inspection robot, and the path planning agent is used for planning the inspection path of the inspection robot;
the monitoring unit includes:
the system comprises an image processing module, a storage module, a display module and a power supply module, wherein the image processing module, the storage module, the display module and the power supply module are used for monitoring relevant information of the inspection robot and inspection operation conditions in real time;
the inspection robot comprises a man-machine interaction module, an inspection robot and a control module, wherein an inspection person remotely issues an inspection instruction, an emergency notice and a change instruction to the inspection robot through a man-machine interaction interface;
the main control unit and the monitoring unit are both provided with communication modules, and the communication modules of the main control unit are connected with the communication modules of the monitoring unit.
Furthermore, the encapsulation shell of the main control unit is of a mining explosion-proof and intrinsic safety type.
Further, the edge detection module is based on image edge detection of B-spline wavelets.
Further, the image edge detection step of the edge detection module based on B-spline wavelet transform is as follows:
step 1: converting the original image into a gray image, performing wavelet transformation on the gray image, and calculating high-frequency detail components;
step 2: calculating the modulus of the wavelet transform coefficient;
step 3: calculating the amplitude angle of the wavelet transformation coefficient:
step 4: obtaining a local modulus maximum value: dividing the argument into 4 directions, wherein the first direction is 0 degree or 180 degree direction, the second direction is 90 degree or 270 degree direction, the third direction is 45 degree or 225 degree direction, and the fourth direction is 135 degree or 315 degree direction;
step 5: sequentially checking each pixel point to see whether the pixel point is the maximum value in the direction closest to the corresponding argument; if so, recording the gradient value, otherwise, setting the gradient value to be 0;
step 6: carrying out threshold processing on the obtained preliminary edge image, selecting a threshold lambda, and setting the modulus values of all pixels with the modulus maximum value smaller than lambda as 0;
step 7: adjusting the scale parameters of wavelet transformation, and outputting edge detection images under various scales;
step 8: and running the program and outputting the image edge.
A method for polling the polling robot group by adopting the polling system of the mining polling robot comprises the following steps that a main control unit is installed on each polling robot, and a monitoring unit monitors relevant information and polling operation conditions of each polling robot in real time; the specific inspection method comprises the following steps:
A. the inspection personnel send task instructions to the inspection robots through the monitoring unit, and the inspection robots are independent from each other and have no primary and secondary points;
B. after the inspection robot receives the inspection instruction, the acquisition module acquires relevant data information according to the ambient environment condition;
C. the edge detection module carries out edge detection on surrounding obstacles according to the acquired image information;
D. the image identification module identifies the edge information of the obstacle after the edge detection to determine the type of the obstacle;
E. the infrared ranging module is used for ranging according to the edge information and the type of the obstacle so as to avoid the obstacle;
F. the position intelligent agent in the multi-agent module acquires the position information of each obstacle and the inspection robot and simultaneously sends the information to other inspection robots and the monitoring unit;
G. the gesture intelligent agent shares the gesture information of the inspection robot to each robot and each monitoring unit; meanwhile, the self posture is continuously optimized in the process of inspection operation;
H. the speed intelligent agent shares the walking speed of the inspection robot per se on each road section to each inspection robot and the monitoring unit; the inspection robot continuously adjusts the walking speed of the inspection robot under each road condition while acquiring experience;
I. the obstacle avoidance intelligent body integrates all the information to avoid the collision of the inspection robot with other obstacles;
J. the path planning agent comprehensively analyzes information obtained by processing other agents and images in the multi-agent module to determine the optimal routing inspection path of the routing inspection robot;
K. each inspection robot sends inspection data information to the monitoring unit through wireless communication;
l, monitoring the operation condition of each inspection robot in real time by inspection personnel according to the monitoring unit, and timely changing an inspection scheme by the inspection personnel through a human-computer interaction interface if necessary;
furthermore, the multi-agent module also comprises a coordination agent, and the coordination agent is used for coordinating the relationship among the inspection robots.
Compared with the traditional inspection robot obstacle avoidance scheme and path planning control, the method disclosed by the invention has the advantages that inspection personnel issue inspection instructions remotely, and then a multi-agent control concept and an image processing deep learning method are utilized to cooperatively control a plurality of inspection robots. The process is that the polling personnel sends instructions to the polling robots through the man-machine interaction module on the monitoring unit and transmits the instructions to each polling robot through the communication module of the monitoring unit; after each inspection robot obtains the instruction, acquiring path information through an acquisition module and image edge detection and identification; then measuring the position of the obstacle through an infrared distance measuring module; next, information sharing such as obstacle avoidance, routing inspection paths, postures and positions among the inspection robots is achieved through the multi-agent module; and finally, each independent inspection robot sends inspection data to the monitoring unit through the communication module of the main control unit, so that inspection personnel can further judge the data according to the output display module of the monitoring unit. The multiple inspection robots work cooperatively, so that inspection efficiency can be improved, labor intensity can be reduced, potential safety hazards can be reduced, and great significance is provided for digital mines, intelligent, 'less-humanized' and 'unmanned' working faces.
Drawings
FIG. 1 is a schematic diagram of the present invention;
FIG. 2 is a schematic diagram of the B-spline wavelet-based edge detection algorithm of the present invention;
FIG. 3 is a graph illustrating the determination of an edge of an obstacle according to the edge detection principle of the present invention;
FIG. 4 is a diagram of the multi-agent control scheme of the present invention;
fig. 5 is a horn pattern of the present invention.
Detailed Description
The invention is further illustrated below with reference to the figures and examples.
As shown in fig. 1 to 4, the inspection system for the mining inspection robot comprises a main control unit for helping the inspection robot to complete an inspection task and a monitoring unit for monitoring relevant information of the inspection robot and inspection operation conditions in real time; the main control unit is arranged on the inspection robot, and the monitoring unit is positioned in the ground dispatching center;
the main control unit includes:
the collection module is used for collecting environmental information around the inspection robot;
the edge detection module is used for carrying out edge detection on the surrounding environment according to the information acquired by the acquisition module and determining the edge information of the barrier; the edge detection module is based on image edge detection of B-spline wavelets.
The principle of image edge detection of the B-spline wavelet is as follows:
let θ (x, y) be a two-dimensional smoothing function, where ≈ θ (x, y) dxy ≠ 0. Then there are:
function psi(1)(x, y) and ψ(2)(x, y) are two basic two-dimensional wavelets, which are the first partial derivatives of θ (x, y) along the x, y direction. Then setting:
wherein,
for any function f (x, y) e L2(R2) Then it isThe two components of the wavelet transform are:
in the formula,
representing a convolution. Thus:
from equations (5), (6) and (7), a vector equation such as the following can be obtained:
in the formula fs(x, y) is f (x, y) is θa(x, y) smoothed image. Equation (8) reflects the gradient of the image gray along the x, y direction. For wavelet transform, it is common to take a-2j(j ∈ Z), then there are:
the modulus value is:
the amplitude (angle to x direction) is:
the edge is in fact Mod [ WTf]Where extreme values are taken, in other words
Mutation point of (3), Arg [ WTf]The gradient direction is indicated. According to wavelet analysis, the maximum value point of the wavelet coefficient is a point for depicting the abrupt change position of the image, namely the position of the edge point of the image, and then the multi-scale edge detection of the wavelet modulus maximum value can be realized by adjusting the scale. But noise is also a gray-level discontinuity and also a maximum point. Because the wavelet has the property of energy concentration, the wavelet can concentrate signal energy on a few wavelet coefficients, the wavelet coefficient amplitude of the edge is larger, the noise energy is more dispersed, and the wavelet coefficient amplitude is smaller. Therefore, the first derivative of the smoothing function is used as the wavelet function to perform wavelet transform on the image, and the modulus maximum value point of the wavelet coefficient larger than a certain threshold value is the edge point of the corresponding image, which is the principle of the wavelet transform for edge detection.
The image edge detection method based on B-spline wavelet transform of the edge detection module comprises the following steps:
step 1: converting the original image into a gray image, performing wavelet transformation on the gray image, and calculating high-frequency detail components;
step 2: calculating the modulus of the wavelet transform coefficient;
step 3: calculating the amplitude angle of the wavelet transformation coefficient:
step 4: obtaining a local modulus maximum value: dividing the argument into 4 directions, as shown in fig. 5, a first direction is 0 ° or 180 °, a second direction is 90 ° or 270 °, a third direction is 45 ° or 225 °, and a fourth direction is 135 ° or 315 °;
step 5: and sequentially checking each pixel point to see whether the pixel point is the maximum value or not in the direction closest to the corresponding argument. If so, recording the gradient value, otherwise, setting the gradient value to be 0;
step 6: carrying out threshold processing on the obtained preliminary edge image, selecting a threshold lambda, and setting the modulus values of all pixels with the modulus maximum value smaller than lambda as 0;
step 7: adjusting the scale parameters of wavelet transformation, and outputting edge detection images under various scales;
step 8: and running the program and outputting the image edge.
The image identification module is used for identifying according to the edge information of the obstacle after edge detection and determining the type of the obstacle;
the infrared distance measurement module determines the distance between the inspection robot and the obstacle according to the edge information and the type of the obstacle;
the multi-agent module comprises a position agent, a posture agent, a speed agent, an obstacle avoidance agent and a path planning agent, wherein the position agent, the posture agent and the speed agent in the multi-agent module are used for acquiring the position, the posture and the speed information of the inspection robot, the obstacle avoidance agent is used for avoiding obstacles of the inspection robot, and the path planning agent is used for planning the inspection path of the inspection robot;
the monitoring unit includes:
the system comprises an image processing module, a storage module, a display module and a power supply module, wherein the image processing module, the storage module, the display module and the power supply module are used for monitoring relevant information of the inspection robot and inspection operation conditions in real time;
the inspection robot comprises a man-machine interaction module, an inspection robot and a control module, wherein an inspection person remotely issues an inspection instruction, an emergency notice and a change instruction to the inspection robot through a man-machine interaction interface;
the main control unit and the monitoring unit are both provided with communication modules, and the communication modules of the main control unit are connected with the communication modules of the monitoring unit.
In order to patrol the safety of the robot in the underground, the encapsulation shell of the main control unit adopts a mining explosion-proof and intrinsic safety type.
A method for polling the polling robot group by adopting the polling system of the mining polling robot comprises the following steps that a main control unit is installed on each polling robot, and a monitoring unit monitors relevant information and polling operation conditions of each polling robot in real time; the specific inspection method comprises the following steps:
A. the monitoring unit is installed on ground dispatch center, and the personnel of patrolling and examining issue the instruction of patrolling and examining to each robot through the human-computer interaction interface, and communication module on the monitoring unit sends the instruction, and each robot of patrolling and examining begins to patrol and examine work after receiving the instruction afterwards, and each robot of patrolling and examining is independent each other, does not have the major-minor to divide.
B. After the inspection robot receives the inspection instruction, the acquisition module acquires relevant data information according to the ambient environment condition and provides basic data for the walking path.
C. The edge detection module carries out edge detection on surrounding obstacles according to the acquired image information; as shown in fig. 2, the edge detection module is based on the edge detection principle of B-spline wavelet, and as shown in fig. 3, determines the edge of the obstacle according to the edge detection principle.
D. And the image identification module identifies the edge information of the obstacle after the edge detection to determine the type of the obstacle.
E. The infrared ranging module measures the distance according to the edge information and the type of the obstacle, and determines the distance between the inspection robot and the obstacle so as to avoid the obstacle.
F. And the position intelligent agent in the multi-agent module acquires the position information of each obstacle and the inspection robot according to the image processing technology, and simultaneously sends the information to other inspection robots and the monitoring unit to provide basic information for the optimal search path.
G. The gesture intelligent agent shares the gesture information of the inspection robot to each robot and each monitoring unit; meanwhile, the self posture is continuously optimized in the process of inspection operation;
H. the speed intelligent agent shares the walking speed of the inspection robot per se on each road section to each inspection robot and the monitoring unit; the inspection robot continuously adjusts the walking speed of the inspection robot under each road condition while acquiring experience;
I. and the obstacle avoidance intelligent body firstly avoids colliding with other obstacles according to the image processing information, secondly avoids colliding with each inspection robot according to data provided by the position intelligent body, the attitude intelligent body and the speed intelligent body, and determines the optimal position, attitude and speed of each inspection robot according to information sharing.
J. The path planning agent comprehensively analyzes information obtained by processing other agents and images in the multi-agent module to determine the optimal routing inspection path of the routing inspection robot; the multi-agent module also comprises a coordination agent which is used for coordinating the relationship among the inspection robots.
K. And each inspection robot sends inspection data information to the monitoring unit through wireless communication.
And L, monitoring the operation condition of each inspection robot in real time by inspection personnel according to the monitoring unit, and timely changing the inspection scheme by the inspection personnel through a human-computer interaction interface if necessary.
Claims (6)
1. A polling system of a mining polling robot is characterized by comprising a main control unit for helping the polling robot to complete a polling task and a monitoring unit for monitoring relevant information of the polling robot and polling operation conditions in real time; the main control unit is arranged on the inspection robot, and the monitoring unit is positioned in the ground dispatching center;
the main control unit includes:
the collection module is used for collecting environmental information around the inspection robot;
the edge detection module is used for carrying out edge detection on the surrounding environment according to the information acquired by the acquisition module and determining the edge information of the barrier;
the image identification module is used for identifying according to the edge information of the obstacle after edge detection and determining the type of the obstacle;
the infrared distance measurement module determines the distance between the inspection robot and the obstacle according to the edge information and the type of the obstacle;
the multi-agent module comprises a position agent, a posture agent, a speed agent, an obstacle avoidance agent and a path planning agent, wherein the position agent, the posture agent and the speed agent in the multi-agent module are used for acquiring the position, the posture and the speed information of the inspection robot, the obstacle avoidance agent is used for avoiding obstacles of the inspection robot, and the path planning agent is used for planning the inspection path of the inspection robot;
the monitoring unit includes:
the system comprises an image processing module, a storage module, a display module and a power supply module, wherein the image processing module, the storage module, the display module and the power supply module are used for monitoring relevant information of the inspection robot and inspection operation conditions in real time;
the inspection robot comprises a man-machine interaction module, an inspection robot and a control module, wherein an inspection person remotely issues an inspection instruction, an emergency notice and a change instruction to the inspection robot through a man-machine interaction interface;
the main control unit and the monitoring unit are both provided with communication modules, and the communication modules of the main control unit are connected with the communication modules of the monitoring unit.
2. The inspection system according to claim 1, wherein the enclosure of the master control unit is of a mining flameproof and intrinsically safe type.
3. The inspection system according to claim 1, wherein the edge detection module is based on image edge detection of B-spline wavelets.
4. The inspection system according to claim 3, wherein the edge detection module performs the following image edge detection steps based on B-spline wavelet transform:
step 1: converting the original image into a gray image, performing wavelet transformation on the gray image, and calculating high-frequency detail components;
step 2: calculating the modulus of the wavelet transform coefficient;
Mod[WTf(2j,x,y)]＝[|WT(1)f(2j,x,y)|2+|WT(2)f(2j,x,y)|2]1/2 (12)
step 3: calculating the amplitude angle of the wavelet transformation coefficient:
step 4: obtaining a local modulus maximum value: dividing the argument into 4 directions, wherein the first direction is 0 degree or 180 degree direction, the second direction is 90 degree or 270 degree direction, the third direction is 45 degree or 225 degree direction, and the fourth direction is 135 degree or 315 degree direction;
step 5: sequentially checking each pixel point to see whether the pixel point is the maximum value in the direction closest to the corresponding argument; if so, recording the gradient value, otherwise, setting the gradient value to be 0;
step 6: carrying out threshold processing on the obtained preliminary edge image, selecting a threshold lambda, and setting the modulus values of all pixels with the modulus maximum value smaller than lambda as 0;
step 7: adjusting the scale parameters of wavelet transformation, and outputting edge detection images under various scales;
step 8: and running the program and outputting the image edge.
5. A method for inspecting the inspection robot group by adopting the inspection robot inspection system for the mine according to the claims 1 to 4, wherein a main control unit is arranged on each inspection robot, and a monitoring unit monitors the relevant information and the inspection operation condition of each inspection robot in real time; the method is characterized in that the specific inspection method comprises the following steps:
A. the inspection personnel send task instructions to the inspection robots through the monitoring unit, and the inspection robots are independent from each other and have no primary and secondary points;
B. after the inspection robot receives the inspection instruction, the acquisition module acquires relevant data information according to the ambient environment condition;
C. the edge detection module carries out edge detection on surrounding obstacles according to the acquired image information;
D. the image identification module identifies the edge information of the obstacle after the edge detection to determine the type of the obstacle;
E. the infrared ranging module is used for ranging according to the edge information and the type of the obstacle so as to avoid the obstacle;
F. the position intelligent agent in the multi-agent module acquires the position information of each obstacle and the inspection robot and simultaneously sends the information to other inspection robots and the monitoring unit;
G. the gesture intelligent agent shares the gesture information of the inspection robot to each robot and each monitoring unit; meanwhile, the self posture is continuously optimized in the process of inspection operation;
H. the speed intelligent agent shares the walking speed of the inspection robot per se on each road section to each inspection robot and the monitoring unit; the inspection robot continuously adjusts the walking speed of the inspection robot under each road condition while acquiring experience;
I. the obstacle avoidance intelligent body integrates all the information to avoid the collision of the inspection robot with other obstacles;
J. the path planning agent comprehensively analyzes information obtained by processing other agents and images in the multi-agent module to determine the optimal routing inspection path of the routing inspection robot;
K. each inspection robot sends inspection data information to the monitoring unit through wireless communication;
l, monitoring the operation condition of each inspection robot in real time by inspection personnel according to the monitoring unit, and timely changing the inspection personnel through a human-computer interaction interface if necessary;
6. the inspection method according to claim 5, wherein the inspection method comprises the following steps: the multi-agent module also comprises a coordination agent which is used for coordinating the relationship among the inspection robots.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010954150.1A CN112060106A (en) | 2020-09-11 | 2020-09-11 | Inspection system of inspection robot for mine and inspection method of inspection robot group |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010954150.1A CN112060106A (en) | 2020-09-11 | 2020-09-11 | Inspection system of inspection robot for mine and inspection method of inspection robot group |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112060106A true CN112060106A (en) | 2020-12-11 |
Family
ID=73696349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010954150.1A Pending CN112060106A (en) | 2020-09-11 | 2020-09-11 | Inspection system of inspection robot for mine and inspection method of inspection robot group |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112060106A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112787264A (en) * | 2020-12-30 | 2021-05-11 | 珠海华网科技有限责任公司 | Electric power overhead line inspection device and method based on ultrasonic technology |
| CN112863001A (en) * | 2021-01-19 | 2021-05-28 | 中国工商银行股份有限公司 | Power distribution room patrol inspection method, device and system |
| CN113034718A (en) * | 2021-03-01 | 2021-06-25 | 启若人工智能研究院(南京)有限公司 | Subway pipeline inspection system based on multiple agents |
| CN113232025A (en) * | 2021-06-07 | 2021-08-10 | 上海大学 | Mechanical arm obstacle avoidance method based on proximity perception |
| CN113489001A (en) * | 2021-07-16 | 2021-10-08 | 南京邮电大学 | Multi-agent task area planning method in combined inspection scene |
| CN114489086A (en) * | 2022-04-14 | 2022-05-13 | 武汉跨克信息技术有限公司 | Bionic robot cooperative operation method and device |
| CN114879655A (en) * | 2022-03-24 | 2022-08-09 | 慧之安信息技术股份有限公司 | Intelligent robot inspection method for underground coal mine conveyor belt |
| CN114995417A (en) * | 2022-05-27 | 2022-09-02 | 北京京东乾石科技有限公司 | Robot and robot inspection method |
| CN115328155A (en) * | 2022-09-05 | 2022-11-11 | 中煤科工集团重庆研究院有限公司 | Obstacle avoidance method for underground coal mine tracked vehicle |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101450843B1 (en) * | 2013-05-20 | 2014-10-15 | 한국생산기술연구원 | Group Robot System and Control Method thereof |
| CN106808482A (en) * | 2015-12-02 | 2017-06-09 | 中国科学院沈阳自动化研究所 | A kind of crusing robot multisensor syste and method for inspecting |
| CN107671831A (en) * | 2017-08-02 | 2018-02-09 | 国网浙江省电力公司紧水滩水力发电厂 | A kind of power station subregion intelligent inspection system and method |
| CN109212377A (en) * | 2018-09-27 | 2019-01-15 | 国网山东省电力公司电力科学研究院 | A kind of high-tension line obstacle recognition method, device, crusing robot |
| CN109613931A (en) * | 2019-01-07 | 2019-04-12 | 北京航空航天大学 | Isomery unmanned plane cluster object tracking system and method based on biological social force |
| CN109664301A (en) * | 2019-01-17 | 2019-04-23 | 中国石油大学(北京) | Method for inspecting, device, equipment and computer readable storage medium |
| CN109732591A (en) * | 2018-12-24 | 2019-05-10 | 济南大学 | One kind having multirobot cluster control method under obstacle environment |
| CN110065061A (en) * | 2018-01-24 | 2019-07-30 | 南京机器人研究院有限公司 | A kind of robot ambulation control method |
| CN110362090A (en) * | 2019-08-05 | 2019-10-22 | 北京深醒科技有限公司 | A kind of crusing robot control system |
-
2020
- 2020-09-11 CN CN202010954150.1A patent/CN112060106A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101450843B1 (en) * | 2013-05-20 | 2014-10-15 | 한국생산기술연구원 | Group Robot System and Control Method thereof |
| CN106808482A (en) * | 2015-12-02 | 2017-06-09 | 中国科学院沈阳自动化研究所 | A kind of crusing robot multisensor syste and method for inspecting |
| CN107671831A (en) * | 2017-08-02 | 2018-02-09 | 国网浙江省电力公司紧水滩水力发电厂 | A kind of power station subregion intelligent inspection system and method |
| CN110065061A (en) * | 2018-01-24 | 2019-07-30 | 南京机器人研究院有限公司 | A kind of robot ambulation control method |
| CN109212377A (en) * | 2018-09-27 | 2019-01-15 | 国网山东省电力公司电力科学研究院 | A kind of high-tension line obstacle recognition method, device, crusing robot |
| CN109732591A (en) * | 2018-12-24 | 2019-05-10 | 济南大学 | One kind having multirobot cluster control method under obstacle environment |
| CN109613931A (en) * | 2019-01-07 | 2019-04-12 | 北京航空航天大学 | Isomery unmanned plane cluster object tracking system and method based on biological social force |
| CN109664301A (en) * | 2019-01-17 | 2019-04-23 | 中国石油大学(北京) | Method for inspecting, device, equipment and computer readable storage medium |
| CN110362090A (en) * | 2019-08-05 | 2019-10-22 | 北京深醒科技有限公司 | A kind of crusing robot control system |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112787264A (en) * | 2020-12-30 | 2021-05-11 | 珠海华网科技有限责任公司 | Electric power overhead line inspection device and method based on ultrasonic technology |
| CN112863001A (en) * | 2021-01-19 | 2021-05-28 | 中国工商银行股份有限公司 | Power distribution room patrol inspection method, device and system |
| CN113034718A (en) * | 2021-03-01 | 2021-06-25 | 启若人工智能研究院(南京)有限公司 | Subway pipeline inspection system based on multiple agents |
| CN113232025A (en) * | 2021-06-07 | 2021-08-10 | 上海大学 | Mechanical arm obstacle avoidance method based on proximity perception |
| CN113232025B (en) * | 2021-06-07 | 2022-04-22 | 上海大学 | Mechanical arm obstacle avoidance method based on proximity perception |
| CN113489001A (en) * | 2021-07-16 | 2021-10-08 | 南京邮电大学 | Multi-agent task area planning method in combined inspection scene |
| CN113489001B (en) * | 2021-07-16 | 2023-09-22 | 南京邮电大学 | Multi-agent task area planning method in joint inspection scene |
| CN114879655A (en) * | 2022-03-24 | 2022-08-09 | 慧之安信息技术股份有限公司 | Intelligent robot inspection method for underground coal mine conveyor belt |
| CN114879655B (en) * | 2022-03-24 | 2022-12-02 | 慧之安信息技术股份有限公司 | Intelligent robot inspection method for underground coal mine conveyor belt |
| CN114489086A (en) * | 2022-04-14 | 2022-05-13 | 武汉跨克信息技术有限公司 | Bionic robot cooperative operation method and device |
| CN114995417A (en) * | 2022-05-27 | 2022-09-02 | 北京京东乾石科技有限公司 | Robot and robot inspection method |
| CN115328155A (en) * | 2022-09-05 | 2022-11-11 | 中煤科工集团重庆研究院有限公司 | Obstacle avoidance method for underground coal mine tracked vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112060106A (en) | Inspection system of inspection robot for mine and inspection method of inspection robot group | |
| Wang et al. | Vision-based robotic system for on-site construction and demolition waste sorting and recycling | |
| CN109753081B (en) | Roadway inspection unmanned aerial vehicle system based on machine vision and navigation method | |
| Zhang et al. | Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering | |
| Song et al. | A vision-based broken strand detection method for a power-line maintenance robot | |
| Zormpas et al. | Power transmission lines inspection using properly equipped unmanned aerial vehicle (UAV) | |
| Liu | Robot systems for rail transit applications | |
| CN107380163A (en) | Automobile intelligent alarm forecasting system and its method based on magnetic navigation | |
| CN111813130A (en) | Autonomous navigation obstacle avoidance system of intelligent patrol robot of power transmission and transformation station | |
| CN113075686B (en) | Cable trench intelligent inspection robot graph building method based on multi-sensor fusion | |
| CN115256414A (en) | Mining drilling robot and coupling operation method of mining drilling robot and geological and roadway models | |
| CN112983417B (en) | Data analysis and early warning method for coal mining equipment | |
| CN114803860A (en) | Underground monorail crane unmanned driving system and method based on machine vision | |
| Baba | A new design of a flying robot, with advanced computer vision techniques to perform self-maintenance of smart grids | |
| CN115937213A (en) | Visual defect identification system of automatic inspection robot for mining monorail crane track | |
| CN212515475U (en) | Autonomous navigation obstacle avoidance system of intelligent patrol robot of power transmission and transformation station | |
| CN112281972B (en) | Remote monitoring structure based on unmanned excavator and excavator | |
| CN102521653B (en) | Biostimulation neural network device and method for jointly rescuing by multiple underground robots | |
| Güney et al. | Autonomous control of shore robotic charging systems based on computer vision | |
| Du et al. | Applications of machine vision in coal mine fully mechanized tunneling faces: a review | |
| CN113625773A (en) | Unmanned aerial vehicle emergency fire-fighting forest patrol command system | |
| CN116214532B (en) | Autonomous obstacle avoidance grabbing system and grabbing method for submarine cable mechanical arm | |
| Jia et al. | Condition assessment of the cable trench based on an intelligent inspection robot | |
| CN117733845A (en) | XR-based multi-element collaborative operation method for underground robot and operator unmanned aerial vehicle | |
| CN116798117A (en) | Video understanding-based method for identifying abnormal actions under mine |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201211 |