CN111469131A - Unmanned ship water surface garbage cleaning control system and method with mechanical arm - Google Patents
Unmanned ship water surface garbage cleaning control system and method with mechanical arm Download PDFInfo
- Publication number
- CN111469131A CN111469131A CN202010389366.8A CN202010389366A CN111469131A CN 111469131 A CN111469131 A CN 111469131A CN 202010389366 A CN202010389366 A CN 202010389366A CN 111469131 A CN111469131 A CN 111469131A
- Authority
- CN
- China
- Prior art keywords
- garbage
- mechanical arm
- module
- information
- unmanned ship
- 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.)
- Withdrawn
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
-
- 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
-
- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02B—HYDRAULIC ENGINEERING
- E02B15/00—Cleaning or keeping clear the surface of open water; Apparatus therefor
- E02B15/04—Devices for cleaning or keeping clear the surface of open water from oil or like floating materials by separating or removing these materials
- E02B15/10—Devices for removing the material from the surface
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Robotics (AREA)
- Environmental & Geological Engineering (AREA)
- Civil Engineering (AREA)
- Structural Engineering (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a system and a method for controlling the cleaning of water surface garbage of an unmanned ship provided with mechanical arms. According to the invention, the continuous operation mode of traversing the task area is replaced by arranging the position point of the mechanical arm for picking up the garbage, so that the energy consumption is reduced; meanwhile, the garbage cleaning work of the position of the dead zone outside the calibration point is realized by utilizing the camera and the laser radar. The invention realizes the full-automatic water surface garbage cleaning and classifying work and solves the problems of low efficiency, high energy consumption and high labor cost in the traditional method.
Description
Technical Field
The invention belongs to the technical field of unmanned ship application, and particularly relates to an unmanned ship water surface garbage cleaning control system and method with an assembled mechanical arm.
Background
The problem of cleaning garbage on the water surface exists in river channels, ponds, lake surfaces, lakes and river surfaces, landscape river channels, urban river channels, reservoirs and hydropower stations. The water surface garbage comprises aquatic plants such as waterweeds, water hyacinth, reeds and the like and various domestic garbage, which not only obstruct a navigation channel but also damage the marine ecological environment. At present, common garbage cleaning modes are divided into two modes of manual fishing and semi-automatic processing, the manual fishing is low in efficiency, the operation time is long, and certain danger is achieved. Fully automated methods for surface waste cleaning have also been proposed, but require the vessel to traverse the mission area completely, which results in higher energy consumption and less efficient working time. Therefore, a set of efficient and low-power-consumption water surface garbage cleaning control system with classification capability needs to be researched.
Disclosure of Invention
The invention aims to provide a system and a method for controlling the cleaning of water surface garbage of an unmanned boat, which are provided with mechanical arms and are used for solving the problems of low working efficiency and high energy consumption of the cleaning of the water surface garbage. According to the invention, an unmanned ship is taken as a mobile carrier to carry a mechanical arm to perform tracking and target detection in a task area, and the mechanical arm based on reinforcement learning is utilized to realize the detection and grabbing of a target. The mechanical arm cleaning device can not only clean water surface garbage under a given area, but also optimize the grabbing precision of the mechanical arm in continuous learning.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the utility model provides an unmanned ship surface of water rubbish clearance control system of assembly arm, includes data acquisition module, data processing module, data storage module and the arm system that is connected with central controller respectively, and the power module who provides the power for above each module, wherein:
the data acquisition module is used for acquiring the position information of the unmanned ship, the course angle information of the unmanned ship, detecting whether garbage and garbage classification information exist in a task area or not, acquiring the distance information between the residual garbage and the unmanned ship in the whole task area and sending all the acquired information to the data processing module;
the data processing module is used for processing all the information acquired by the data acquisition module and transmitting the processed information to the central controller;
the data storage module is used for storing the image information of common water surface garbage and summarizing the image information into three types of dry garbage, recoverable garbage and other garbage so that the central controller controls the mechanical arm system to realize classified garbage throwing; the central controller is used for reading the image information acquired by the data acquisition module and comparing the image information with the image information in the data storage module to determine the garbage type, planning the cruise path of the unmanned ship and calibrating the picking point of the mechanical arm system.
Furthermore, the data acquisition module comprises a positioning module, an attitude sensor, a camera and a laser radar; the positioning module provides ship position information; the attitude sensor is used for measuring the course angle of the ship; the camera is used for detecting whether garbage exists in the task area and collecting garbage image information picked up by the mechanical arm to realize garbage classification; the laser radar is used for detecting distance information between residual garbage and the unmanned ship in the whole task area.
Further, the positioning module is a GPS or inertial navigation system.
Further, the data processing module is used for processing the information obtained by the data acquisition module, and the processing comprises analog-to-digital conversion, filtering, signal amplification and image transcoding.
Further, the mechanical arm system comprises a microprocessor, a mechanical arm, a driver and a binocular camera; the binocular camera is used for determining distance information between the mechanical arm gripper and a target and sending the measured distance information to the microprocessor; the microprocessor processes the distance information to obtain a control signal; the driver controls the mechanical arm according to a control signal of the microprocessor to realize the grabbing of the target garbage, and the interaction process of the mechanical arm and the task environment is learned through a reinforcement learning network model optimization algorithm in the microprocessor to improve the grabbing precision.
Further, the reinforcement learning network model optimization algorithm comprises the following steps:
1) modeling a task environment through an auxiliary network;
2) the evaluation network predicts an external strengthening signal according to the current state and the simulation environment and learns the evaluation network by using a time sequence difference prediction method;
3) and carrying out genetic operation on the mobile network, and using the internal enhanced signal as a fitness function of the mobile network to obtain the action enhanced signal which is currently applied to the task environment by the mobile network.
Further, the power supply module comprises a solar cell panel, a storage battery and a voltage stabilizing module; the solar panel is used for converting solar energy into electric energy stored in the storage battery; the storage battery is in a chargeable mode to store electric energy generated by the solar panel and is connected with the voltage stabilizing module; the voltage stabilizing module is used for providing stable voltage for the data acquisition module, the data processing module, the central controller and the mechanical arm system.
A method for controlling the cleaning of garbage on the water surface of an unmanned boat provided with a mechanical arm comprises the following steps:
s01: the central controller plans a cruising track of the unmanned ship in the task area according to the position information and the course information measured by the positioning module and the attitude sensor and calibrates a picking point according to the length of the mechanical arm;
s02: after the unmanned ship reaches a calibrated picking point, acquiring image information by using a camera to find whether garbage exists or not;
s03: the data processing module processes the image information detected by the camera and compares the processed image information with the information stored by the storage module to determine the garbage type; meanwhile, the positions of the garbage are positioned by a binocular camera, and accurate position information is sent to a microprocessor;
s04: the microprocessor obtains a control signal according to the accurate position information of the garbage, and then drives the mechanical arm to grab the garbage by using the control signal and classify and recycle the garbage;
s05: the binocular camera sends distance information between the mechanical arm and the garbage before and after the mechanical arm acts to the central controller, and the central controller sends the resolved data to a microprocessor of the mechanical arm system;
s06: the microprocessor utilizes a reinforcement learning network model optimization algorithm;
s07: the driver drives the mechanical arm to grab the garbage by using the obtained enhanced signal;
s08: repeating S05-S07 to obtain the maximum reinforcement signal of the reinforcement learning network model;
s09: repeating the steps S02-S07 to realize the picking tasks of all the calibration points on the cruise track;
s10: after the picking task of the calibration point is finished, the unmanned ship detects whether the left garbage is not cleaned in the task area through the camera;
s11: if the garbage is not cleaned, starting a laser radar, and carrying out path planning by the central controller according to the distance between the current position of the unmanned ship and the garbage measured by the laser radar;
s12: the unmanned ship sails to a target position according to a planned path, and S03-S04 processes are executed to realize classification and cleaning of garbage;
s13: and repeating the steps S11-S12 until the garbage cleaning of the whole region is completed.
Further, the reinforcement learning network model optimization algorithm comprises the following steps:
1) modeling a task environment through an auxiliary network;
2) the evaluation network predicts an external strengthening signal according to the current state and the simulation environment and learns the evaluation network by using a time sequence difference prediction method;
3) and carrying out genetic operation on the mobile network, and using the internal enhanced signal as a fitness function of the mobile network to obtain the action enhanced signal which is currently applied to the task environment by the mobile network.
Compared with the prior art, the invention has the beneficial effects that:
the unmanned ship water surface garbage cleaning control system and method provided with the mechanical arm can simultaneously realize garbage cleaning work near a calibration pick-up point and garbage cleaning work at a blind area position, and ensure that a water surface garbage cleaning task in a whole task area is completed with lower power consumption. Compared with the common water surface garbage cleaning method, the automatic classification and cleaning of garbage can be realized, and the control algorithm in an application system (the mechanical arm system) can be optimized on line, so that the unmanned boat water surface garbage cleaning control system provided with the mechanical arm has lower energy consumption and higher efficiency and safety, and the working time of the system is increased by setting the picking point and assembling the solar cell panel. The invention has the characteristics of high grabbing precision, classification and cleaning, low power consumption, long endurance time and the like.
Drawings
Fig. 1 is a block diagram of an unmanned boat water surface garbage cleaning control system equipped with a mechanical arm.
FIG. 2 illustrates a planned cruise trajectory and calibrated surface garbage pick-up points according to an exemplary embodiment of the present invention.
FIG. 3 is a diagram of a reinforcement learning algorithm network model in a robot microprocessor.
Fig. 4 is a flow chart of the work of the unmanned surface vehicle water surface garbage cleaning control system equipped with the mechanical arm.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification.
As shown in fig. 1, the unmanned surface vehicle water surface garbage cleaning control system provided with the mechanical arm is composed of a data acquisition module 1, a data processing module 2, a data storage module 3, a central controller 4, a mechanical arm system 6 and a power supply module 5;
as shown in fig. 1, the data acquisition module comprises a positioning module 1-1, an attitude sensor 1-2, a camera 1-3 and a laser radar 1-4; the positioning module 1-1 preferably selects a GPS or an inertial navigation system to provide real-time position information of the ship; the attitude sensor 1-2 is used for measuring the course angle of the ship; the camera 1-3 is used for detecting whether garbage exists in the task area and collecting garbage information picked up by the mechanical arm to realize garbage classification; the laser radar 1-4 is used for detecting distance information between residual garbage and the unmanned ship in the whole task area;
the data processing module 2 is used for processing the information obtained by the data acquisition module 1, such as analog-to-digital conversion, filtering, signal amplification, image transcoding and the like, and transmitting the processed information to the central controller 4;
the data storage module 3 is used for storing image information of common water surface garbage and summarizing the image information into three types of dry garbage, recoverable garbage and other garbage so that the central controller 4 controls the mechanical arm to realize classified garbage throwing;
the central controller 4 is used for reading the image information acquired by the camera and comparing the image information with the image information in the data storage module 3 to determine the garbage type, and can plan the cruise path of the unmanned ship and calibrate the mechanical arm picking point;
the mechanical arm system 6 comprises a microprocessor 2-1, a driver 6-2, a mechanical arm 6-3 and a binocular camera 6-4; the binocular camera 6-4 is used for determining distance information between the mechanical arm gripper and a target and sending the measured distance information to the microprocessor 2-1; the microprocessor 2-1 processes the signals to obtain control signals (such as action signals of a stepping motor); the driver 6-2 controls the mechanical arm 6-3 according to a control signal of the microprocessor 2-1 to realize the grabbing of target garbage, and the interaction process of the mechanical arm 6-3 and a task environment is learned through a reinforcement learning network model optimization algorithm in the microprocessor 2-1 to improve the grabbing precision;
the power module 5 comprises a solar cell panel 5-1, a storage battery 5-2 and a voltage stabilizing module 5-3; the solar panel 5-1 is used for converting solar energy into electric energy stored in the storage battery; the storage battery 5-2 is selected as a chargeable mode to store electric energy generated by the solar panel and is connected with the voltage stabilizing module 5-3; the voltage stabilizing module 5-3 is used for providing stable voltage for the data acquisition module 1, the data processing module 2, the central controller 4 and the mechanical arm system 6;
a method for controlling the cleaning of garbage on the water surface of an unmanned boat provided with a mechanical arm comprises the following steps:
s01: the central controller plans a cruising track of the unmanned ship in the task area according to the position information and the course information measured by the positioning module and the attitude sensor and calibrates a picking point according to the length of the mechanical arm, as shown in figure 2;
s02: after the unmanned ship reaches a calibrated picking point, acquiring image information by using a camera to find whether garbage exists or not;
s03: the data processing module processes the image information detected by the camera and compares the processed image information with the information stored by the storage module to determine the garbage type; meanwhile, the positions of the garbage are positioned by a binocular camera, and accurate position information is sent to a microprocessor;
s04: the microprocessor obtains a control signal according to the accurate position information of the garbage, and then drives the mechanical arm to grab the garbage by using the control signal and classify and recycle the garbage;
s05: the binocular camera sends distance information between the mechanical arm and the garbage before and after the mechanical arm acts to the central controller, and the central controller sends the resolved data to a microprocessor of the mechanical arm system;
s06: the microprocessor utilizes the reinforcement learning network model optimization algorithm shown in fig. 3, and the working process is as follows:
1) modeling an environment through an auxiliary network
2) The evaluation network predicts the external reinforcing signal according to the current state and the simulation environment, and learns the evaluation network by using a time sequence difference prediction method,
3) carrying out genetic operation on the mobile network, and using the internal enhanced signal as a fitness function of the mobile network to obtain an action enhanced signal which is currently applied to the environment by the mobile network;
s07: the driver drives the mechanical arm to grab the garbage by using the obtained enhanced signal;
s08: repeating S05-S07 to maximize the reinforcement signal obtained by the reinforcement learning algorithm;
s09: repeating the steps S02-S07 to realize the picking tasks of all the calibration points on the cruise track;
s10: after the picking task of the calibration point is finished, the unmanned ship detects whether the left garbage is not cleaned in the task area through the camera;
s11: if the garbage is not cleaned, starting a laser radar, and carrying out path planning by the central controller according to the distance between the current position of the unmanned ship and the garbage measured by the laser radar;
s12: the unmanned ship sails to a target position according to a planned path, and S03-S04 is adopted to realize classification and cleaning of garbage;
s13: and repeating the steps S11-S12 until the garbage cleaning of the whole region is completed, wherein the complete work flow is shown in the figure 4.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. The utility model provides an unmanned ship surface of water rubbish clearance control system of assembly arm, its characterized in that, includes data acquisition module, data processing module, data storage module and the arm system of being connected with central controller respectively, and the power module who provides the power for above each module, wherein:
the data acquisition module is used for acquiring the position information of the unmanned ship, the course angle information of the unmanned ship, detecting whether garbage and garbage classification information exist in a task area or not, acquiring the distance information between the residual garbage and the unmanned ship in the whole task area and sending all the acquired information to the data processing module;
the data processing module is used for processing all the information acquired by the data acquisition module and transmitting the processed information to the central controller;
the data storage module is used for storing the image information of common water surface garbage and summarizing the image information into three types of dry garbage, recoverable garbage and other garbage so that the central controller controls the mechanical arm system to realize classified garbage throwing;
the central controller is used for reading the image information acquired by the data acquisition module and comparing the image information with the image information in the data storage module to determine the garbage type, planning the cruise path of the unmanned ship and calibrating the picking point of the mechanical arm system.
2. The unmanned ship surface garbage cleaning control system equipped with mechanical arms of claim 1, wherein the data acquisition module comprises a positioning module, an attitude sensor, a camera and a laser radar; the positioning module provides ship position information; the attitude sensor is used for measuring the course angle of the ship; the camera is used for detecting whether garbage exists in the task area and collecting garbage image information picked up by the mechanical arm to realize garbage classification; the laser radar is used for detecting distance information between residual garbage and the unmanned ship in the whole task area.
3. The unmanned surface vessel garbage disposal control system with mechanical arm assembly as claimed in claim 2, wherein said positioning module is a GPS or inertial navigation system.
4. The unmanned surface vessel garbage collection control system equipped with robotic arms as claimed in claim 1, wherein said data processing module is configured to process the information obtained by the data acquisition module, including analog-to-digital conversion, filtering, signal amplification and image transcoding.
5. The robotic arm equipped unmanned surface vessel garbage disposal control system of claim 1 wherein said robotic arm system comprises a microprocessor, a robotic arm, a drive, and a binocular camera; the binocular camera is used for determining distance information between the mechanical arm gripper and a target and sending the measured distance information to the microprocessor; the microprocessor processes the distance information to obtain a control signal; the driver controls the mechanical arm according to a control signal of the microprocessor to realize the grabbing of the target garbage, and the interaction process of the mechanical arm and the task environment is learned through a reinforcement learning network model optimization algorithm in the microprocessor to improve the grabbing precision.
6. The unmanned surface vehicle robotic arm based garbage collection control system of claim 5, wherein said reinforcement learning network model optimization algorithm comprises the steps of:
1) modeling a task environment through an auxiliary network;
2) the evaluation network predicts an external strengthening signal according to the current state and the simulation environment and learns the evaluation network by using a time sequence difference prediction method;
3) and carrying out genetic operation on the mobile network, and using the internal enhanced signal as a fitness function of the mobile network to obtain the action enhanced signal which is currently applied to the task environment by the mobile network.
7. The robotic arm equipped unmanned surface vessel debris removal control system of claim 1, wherein the power module comprises a solar panel, a battery, and a voltage regulation module; the solar panel is used for converting solar energy into electric energy stored in the storage battery; the storage battery is in a chargeable mode to store electric energy generated by the solar panel and is connected with the voltage stabilizing module; the voltage stabilizing module is used for providing stable voltage for the data acquisition module, the data processing module, the central controller and the mechanical arm system.
8. A method for controlling the cleaning of garbage on the water surface of an unmanned boat provided with a mechanical arm is characterized by comprising the following steps:
s01: the central controller plans a cruising track of the unmanned ship in the task area according to the position information and the course information measured by the positioning module and the attitude sensor and calibrates a picking point according to the length of the mechanical arm;
s02: after the unmanned ship reaches a calibrated picking point, acquiring image information by using a camera to find whether garbage exists or not;
s03: the data processing module processes the image information detected by the camera and compares the processed image information with the information stored by the data storage module to determine the garbage type; meanwhile, the positions of the garbage are positioned by a binocular camera, and accurate position information is sent to a microprocessor;
s04: the microprocessor obtains a control signal according to the accurate position information of the garbage, and then drives the mechanical arm to grab the garbage by using the control signal and classify and recycle the garbage;
s05: the binocular camera sends distance information between the mechanical arm and the garbage before and after the mechanical arm acts to the central controller, and the central controller sends the resolved data to a microprocessor of the mechanical arm system;
s06: the microprocessor utilizes a reinforcement learning network model optimization algorithm;
s07: the driver drives the mechanical arm to grab the garbage by using the obtained enhanced signal;
s08: repeating S05-S07 to enable the reinforcement learning network model optimization algorithm to obtain the maximum reinforcement signal;
s09: repeating the steps S02-S07 to realize the picking tasks of all the calibration points on the cruise track;
s10: after the picking task of the calibration point is finished, the unmanned ship detects whether the left garbage is not cleaned in the task area through the camera;
s11: if the garbage is not cleaned, starting a laser radar, and carrying out path planning by the central controller according to the distance between the current position of the unmanned ship and the garbage measured by the laser radar;
s12: the unmanned ship sails to a target position according to a planned path, and S03-S04 processes are executed to realize classification and cleaning of garbage;
s13: and repeating the steps S11-S12 until the garbage cleaning of the whole region is completed.
9. The unmanned surface vehicle garbage disposal control method equipped with a robot arm according to claim 7, wherein the reinforcement learning network model optimization algorithm comprises the following steps:
1) modeling a task environment through an auxiliary network;
2) the evaluation network predicts an external strengthening signal according to the current state and the simulation environment and learns the evaluation network by using a time sequence difference prediction method;
3) and carrying out genetic operation on the mobile network, and using the internal enhanced signal as a fitness function of the mobile network to obtain the action enhanced signal which is currently applied to the task environment by the mobile network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010389366.8A CN111469131A (en) | 2020-05-10 | 2020-05-10 | Unmanned ship water surface garbage cleaning control system and method with mechanical arm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010389366.8A CN111469131A (en) | 2020-05-10 | 2020-05-10 | Unmanned ship water surface garbage cleaning control system and method with mechanical arm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111469131A true CN111469131A (en) | 2020-07-31 |
Family
ID=71762290
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010389366.8A Withdrawn CN111469131A (en) | 2020-05-10 | 2020-05-10 | Unmanned ship water surface garbage cleaning control system and method with mechanical arm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111469131A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112526870A (en) * | 2020-12-02 | 2021-03-19 | 电子科技大学 | Road cleaning formation planning method based on inverse clustering algorithm |
| CN113239747A (en) * | 2021-04-26 | 2021-08-10 | 西安建筑科技大学 | Intelligent blowdown system of removing obstacles on water based on computer vision |
| CN113351631A (en) * | 2021-07-05 | 2021-09-07 | 北京理工大学 | Photoelectric intelligent garbage sorting trolley system |
| CN113985876A (en) * | 2021-10-27 | 2022-01-28 | 广州大学 | Marine garbage recycling path planning method and system based on foraging of bionic fishes |
| CN116310597A (en) * | 2023-05-09 | 2023-06-23 | 广东工业大学 | Garbage classification and positioning method, unmanned cleaning boat control method and system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109375633A (en) * | 2018-12-18 | 2019-02-22 | 河海大学常州校区 | River course clear up path planning system and method based on global state information |
| CN208914092U (en) * | 2018-11-09 | 2019-05-31 | 吉林大学 | A kind of intelligent garbage sorting machine people |
| CN110187714A (en) * | 2019-04-22 | 2019-08-30 | 西安电子科技大学 | A kind of rubbish waterborne salvaging control method and system, unmanned plane based on unmanned plane |
| CN110208478A (en) * | 2019-06-20 | 2019-09-06 | 南京芊玥机器人科技有限公司 | A kind of solar energy unmanned boat carrying water environment monitoring system |
| CN110282120A (en) * | 2019-05-10 | 2019-09-27 | 河海大学文天学院 | It is a kind of for picking up the UAV system and its working method of rubbish |
| CN110442140A (en) * | 2019-08-19 | 2019-11-12 | 大连海事大学 | One kind can the learning-oriented unmanned vehicle control of garbage classification |
| CN110485345A (en) * | 2019-09-16 | 2019-11-22 | 福建工程学院 | A kind of garbage cleaning vehicle and rubbish method for cleaning |
| CN110722556A (en) * | 2019-10-17 | 2020-01-24 | 苏州恒辉科技有限公司 | Movable mechanical arm control system and method based on reinforcement learning |
| CN110737271A (en) * | 2019-10-23 | 2020-01-31 | 西南科技大学 | Autonomous cruise system and method for water surface robots |
| CN110924340A (en) * | 2019-11-25 | 2020-03-27 | 武汉思睿博特自动化系统有限公司 | Mobile robot system for intelligently picking up garbage and implementation method |
| CN110963209A (en) * | 2019-12-27 | 2020-04-07 | 中电海康集团有限公司 | Garbage sorting device and method based on deep reinforcement learning |
-
2020
- 2020-05-10 CN CN202010389366.8A patent/CN111469131A/en not_active Withdrawn
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN208914092U (en) * | 2018-11-09 | 2019-05-31 | 吉林大学 | A kind of intelligent garbage sorting machine people |
| CN109375633A (en) * | 2018-12-18 | 2019-02-22 | 河海大学常州校区 | River course clear up path planning system and method based on global state information |
| CN110187714A (en) * | 2019-04-22 | 2019-08-30 | 西安电子科技大学 | A kind of rubbish waterborne salvaging control method and system, unmanned plane based on unmanned plane |
| CN110282120A (en) * | 2019-05-10 | 2019-09-27 | 河海大学文天学院 | It is a kind of for picking up the UAV system and its working method of rubbish |
| CN110208478A (en) * | 2019-06-20 | 2019-09-06 | 南京芊玥机器人科技有限公司 | A kind of solar energy unmanned boat carrying water environment monitoring system |
| CN110442140A (en) * | 2019-08-19 | 2019-11-12 | 大连海事大学 | One kind can the learning-oriented unmanned vehicle control of garbage classification |
| CN110485345A (en) * | 2019-09-16 | 2019-11-22 | 福建工程学院 | A kind of garbage cleaning vehicle and rubbish method for cleaning |
| CN110722556A (en) * | 2019-10-17 | 2020-01-24 | 苏州恒辉科技有限公司 | Movable mechanical arm control system and method based on reinforcement learning |
| CN110737271A (en) * | 2019-10-23 | 2020-01-31 | 西南科技大学 | Autonomous cruise system and method for water surface robots |
| CN110924340A (en) * | 2019-11-25 | 2020-03-27 | 武汉思睿博特自动化系统有限公司 | Mobile robot system for intelligently picking up garbage and implementation method |
| CN110963209A (en) * | 2019-12-27 | 2020-04-07 | 中电海康集团有限公司 | Garbage sorting device and method based on deep reinforcement learning |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112526870A (en) * | 2020-12-02 | 2021-03-19 | 电子科技大学 | Road cleaning formation planning method based on inverse clustering algorithm |
| CN112526870B (en) * | 2020-12-02 | 2022-02-01 | 电子科技大学 | Road cleaning formation planning method based on inverse clustering algorithm |
| CN113239747A (en) * | 2021-04-26 | 2021-08-10 | 西安建筑科技大学 | Intelligent blowdown system of removing obstacles on water based on computer vision |
| CN113351631A (en) * | 2021-07-05 | 2021-09-07 | 北京理工大学 | Photoelectric intelligent garbage sorting trolley system |
| CN113985876A (en) * | 2021-10-27 | 2022-01-28 | 广州大学 | Marine garbage recycling path planning method and system based on foraging of bionic fishes |
| CN113985876B (en) * | 2021-10-27 | 2023-09-26 | 广州大学 | Ocean garbage recycling path planning method and system based on bionic fish foraging |
| CN116310597A (en) * | 2023-05-09 | 2023-06-23 | 广东工业大学 | Garbage classification and positioning method, unmanned cleaning boat control method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111469131A (en) | Unmanned ship water surface garbage cleaning control system and method with mechanical arm | |
| CN110371112B (en) | Intelligent obstacle avoidance system and method for automatic driving vehicle | |
| CN108303508B (en) | Ecological early warning system and method based on laser radar and deep learning path optimization | |
| CN108275246A (en) | Automatically it searches for and handles the unmanned boat of garbage on water and unmanned boat system | |
| KR102169701B1 (en) | System for colleting marine waste | |
| CN105929821A (en) | Robot accurate positioning and automatic return charging system | |
| CN110187714A (en) | A kind of rubbish waterborne salvaging control method and system, unmanned plane based on unmanned plane | |
| CN111898285B (en) | Underwater unmanned autonomous vehicle cluster operation efficiency evaluation method | |
| CN105371896A (en) | Cruising water quality multi-parameter remote monitoring system and method capable of self-learning locus navigation | |
| CN112960078A (en) | Unmanned sailing boat automatic driving system and method thereof | |
| CN104908897A (en) | Autonomous cruise and water-surface spilled oil recovery robot | |
| CN112595551A (en) | Unmanned full-automatic intelligent sampling ship | |
| CN111746728A (en) | Novel overwater cleaning robot based on reinforcement learning and control method | |
| CN107878669A (en) | The wisdom water surface monitors trimaran | |
| CN114162322A (en) | Unmanned aerial vehicle-boat cooperative three-dimensional water ecological environment monitoring system and using method | |
| CN116494247A (en) | Mechanical arm path planning method and system based on depth deterministic strategy gradient | |
| CN114549826A (en) | Energy-saving marine garbage collection return algorithm and system based on machine vision | |
| Pessoa et al. | Design of an underwater sensor network perpetually powered from AUVs | |
| CN116466714A (en) | Control system and method for AGV (automatic guided vehicle) | |
| CN113359739B (en) | Intelligent movement device based on energy substitution technology and movement control method thereof | |
| CN110722556A (en) | Movable mechanical arm control system and method based on reinforcement learning | |
| CN113311837A (en) | Unmanned ship obstacle avoidance system based on overwater and underwater environment recognition | |
| CN212137934U (en) | Multi-unmanned ship cooperative control system based on 5G network | |
| CN111645813A (en) | Multifunctional intelligent control unmanned ship for water surface cleaning | |
| CN117439534B (en) | Cleaning robot based on visual navigation movement and navigation system thereof |
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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20200731 |