Steel bar carrying robot and control method

Abstract

The invention provides a reinforcing steel bar carrying robot and a control method, and relates to the technical field of transportation. The reinforcing steel bar carrying robot comprises a robot chassis, a positioning module, a control module, a driving system, a sensor module and a power supply system, the robot chassis is used for bearing various devices of the robot and is responsible for movement and running of the robot; the sensor module is used for acquiring the surrounding environment information of the robot and the motion state of the robot; the power supply system is used for supplying power to each system of the robot; the positioning module is used for carrying out dense mapping on the surrounding environment and carrying out high-precision positioning on the robot; and the control module adopts an intelligent control algorithm to enable the robot to autonomously control and adjust. The method can improve the carrying efficiency and the carrying stability, realizes the accurate positioning of the robot in the map, and is suitable for various transportation environments.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a steel bar carrying robot and a control method thereof. Background Art

[0002] With the rapid development of robot technology and the continuous increase in labor costs, the transportation of steel bars urgently needs to be replaced by machines. The existing handling robots cannot cope with the uneven transportation road surface. During use, the construction ground may be uneven and there is a large amount of metal waste. The robot will produce a lot of bumps during walking. The metal waste will also wear the robot's wheels. The bumps of the robot will also cause the steel bars to fall, causing inconvenience in transportation, which is easy to cause transportation difficulties and poor transportation stability and comfort. The handling robot also has poor positioning ability and cannot achieve accurate positioning of the robot. The existing robots cannot achieve real-time updates of maps. When the robot enters a new area, the autonomous update iterative map has inaccurate problems. Generally, only a rough map is drawn, and relevant maps cannot be drawn in time. The intelligent control of the robot cannot achieve autonomous adjustment and automatic planning. This point is often ignored and the robot is only transported according to the set program. At the same time, the existing transportation robots often ignore the real-time analysis of the robot's transportation data and cannot analyze it according to the robot's real-time working status, resulting in a low level of intelligence of the robot. Summary of the invention

[0003] The object of the present invention is to provide a steel bar carrying robot and a control method, which can improve the handling efficiency, be suitable for various transportation environments, improve the handling stability and realize the precise positioning of the robot in the map.

[0004] The present invention is achieved in that:

[0005] In a first aspect, the present application provides a steel bar carrying robot, including a robot chassis, a positioning module, a control module, a sensor module and a power supply system;

[0006] The robot chassis is used to carry various equipment of the robot and is responsible for the movement and driving of the robot;

[0007] The control module is used to use intelligent control algorithms based on the robot's own motion state, dense mapping of the surrounding environment, and high-precision positioning of the robot, so that the robot can be autonomously controlled and adjusted;

[0008] The sensor module is used to realize the robot's autonomous navigation and obstacle avoidance, and obtain the robot's surrounding environment information and its own motion status;

[0009] A power system, used to supply power to various systems of the robot;

[0010] The positioning module is used to achieve dense mapping of the surrounding environment and high-precision positioning of the robot based on the surrounding environment information of the robot obtained by the sensor module.

[0011] Furthermore, a suspension system is included for stabilizing the transportation of the robot according to the load and travel speed of the robot.

[0012] Furthermore, the robot chassis is provided with a Mecanum wheel drive mode and a rear-wheel drive and front-wheel Ackerman steering drive mode according to the surrounding environment information; when the surrounding environment information shows that the environment is a flat ground, the Mecanum wheel drive mode is used; when the surrounding environment information shows that the environment is a rough ground, the rear-wheel drive and front-wheel Ackerman steering drive mode is used.

[0013] Furthermore, the sensor module includes a laser radar, a visual sensor, and an inertial navigation;

[0014] LiDAR, used to achieve 3D mapping and positioning of the robot;

[0015] Vision sensors to identify storage locations and the types and quantities of steel bars;

[0016] Inertial navigation is used to achieve robot posture control and motion control.

[0017] Further, the power system includes a battery management subsystem, a lithium battery, and a charging management subsystem;

[0018] Battery management subsystem, used to monitor the status and health of lithium batteries in real time;

[0019] Lithium batteries, used for charging, discharging and storing electricity;

[0020] The charging management subsystem is used to automatically adjust the charging speed and time according to the power of the lithium battery and the working conditions of the robot.

[0021] In a second aspect, the present application provides a control method for a steel bar carrying robot, comprising the following steps:

[0022] Use the sensor module to obtain the robot's surrounding environment information and its own motion status;

[0023] According to the robot's surrounding environment information, the Cartographer algorithm is used to build a three-dimensional map and position the robot, and build SLAM;

[0024] Based on SLAM, VSLAM is constructed using the ORB-SLAM3 algorithm to achieve dense mapping of the surrounding environment and high-precision positioning of the robot;

[0025] Intelligent control algorithms are used based on the robot's own motion state, dense mapping of the surrounding environment and high-precision positioning of the robot to enable the robot to control and adjust itself autonomously.

[0026] Furthermore, the steps include adopting an intelligent control algorithm based on dense mapping of the surrounding environment and high-precision positioning of the robot to enable the robot to control and adjust autonomously, and further include:

[0027] Use cloud computing and big data technologies to analyze and process robot data in real time.

[0028] Furthermore, the Cartographer algorithm is used to perform three-dimensional mapping and positioning of the robot based on the robot's surrounding environment information. The construction of SLAM includes the following steps:

[0029] Extracting first map information from the robot's surrounding environment information and feature points in the map, and matching the feature points with the map information;

[0030] Converting the first map information into a first robot motion diagram, wherein nodes represent positions of robots and edges represent motions between robots;

[0031] The motion diagram of the first robot is optimized using a nonlinear optimization algorithm, and the map update and loop detection algorithms are used to update the map information and construct SLAM when the robot passes through an unexplored area.

[0032] Furthermore, VSLAM is constructed based on SLAM using the ORB-SLAM3 algorithm to achieve dense mapping of the surrounding environment and high-precision positioning of the robot, including the following steps:

[0033] Using a camera of a robot sensor module to obtain environmental information, and extracting second map information according to the environmental information, wherein the map information includes image feature points, depth information, and camera pose;

[0034] Matching the image feature points with the map information in SLAM to obtain a first intermediate map;

[0035] The first intermediate map is converted into a second robot motion diagram, and the diagram is optimized using a nonlinear optimization algorithm; wherein the nodes in the second robot motion diagram represent the positions of the robots, and the edges represent the motions between the robots;

[0036] The bag-of-words model algorithm is used to convert image feature points into a bag of words, and the bag of words is matched with the first intermediate map. When it is detected that the robot passes through an unexplored area, the map information is updated.

[0037] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0038] The present invention proposes a steel bar carrying robot and a control method, designs multiple driving modes of multiple chassis, and can adapt to different handling environments; adopts multiple sensors to realize information recognition and autonomous navigation of the robot; constructs SLAM and VSLAM to obtain a map with high accuracy, and realizes real-time updating of the map; adopts an intelligent control algorithm to realize autonomous control and adjustment of the robot; improves work efficiency, improves the adaptability and safety of the robot, and improves the navigation accuracy and speed of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 This is a structural block diagram of a steel bar carrying robot of the present invention;

[0041] Figure 2 Schematic diagram of various motion modes of a Mecanum wheel according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the Ackerman motion mode according to an embodiment of the present invention;

[0043] Figure 4 Indoor maps constructed by the Cartographer algorithm of the present invention;

[0044] Figure 5 An indoor map constructed by the ORB-SLAM3 algorithm of the present invention;

[0045] Figure 6 The present invention is a flow chart of a control method of a steel bar carrying robot. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. Some embodiments of the present application are described in detail below in conjunction with the drawings. In the absence of conflict, the following embodiments and the various features in the embodiments can be combined with each other.

[0047] Example

[0048] See also Figure 1 The steel bar carrying robot comprises a robot chassis, a positioning module, a control module, a sensor module and a power supply system;

[0049] The robot chassis is used to carry various equipment of the robot and is responsible for the movement and driving of the robot;

[0050] A suspension system is also included to stabilize the robot for transport depending on its load and travel speed.

[0051] Demonstrationally, the size of the robot is set to be no larger than 1.2m*1.0m*0.35m, and the load capacity is no less than 2 tons. The material selection of the chassis needs to take into account the load and working environment of the robot. Generally speaking, the material of the chassis should have high strength and rigidity, and be able to withstand large loads and impacts. Commonly used materials include high-strength steel, aluminum alloy, etc. Design the chassis structure: The design of the chassis structure needs to take into account factors such as the load and driving speed of the robot. The chassis structure should have good stability and safety, and be able to protect the robot's equipment and improve the robot's work efficiency. In practical applications, based on actual usage and user feedback, the chassis needs to be continuously optimized and improved to improve the performance and efficiency of the robot.

[0052] The robot chassis is set with a Mecanum wheel drive mode and a rear-wheel drive and front-wheel Ackerman steering drive mode according to the surrounding environment information; when the surrounding environment information shows that the environment is a flat ground, the Mecanum wheel drive mode is used; when the surrounding environment information shows that the environment is a rough ground, the rear-wheel drive and front-wheel Ackerman steering drive mode is used.

[0053] For example, Figure 2 and Figure 3 As shown in the figure, when the ground is flat, the chassis design driven by Mecanum wheels can move parallel to each other, move at four angles, and turn around on the spot. When the ground is rough, the chassis drive system uses efficient motors and reducers to provide sufficient power and torque.

[0054] The sensor module is used to realize the robot's autonomous navigation and obstacle avoidance, and obtain the robot's surrounding environment information and its own motion status;

[0055] The sensor module includes lidar, visual sensor, and inertial navigation;

[0056] LiDAR, used to achieve 3D mapping and positioning of the robot;

[0057] Vision sensors to identify storage locations and the types and quantities of steel bars;

[0058] Inertial navigation is used to achieve robot posture control and motion control.

[0059] Demonstratively, LiDAR can realize the robot's three-dimensional mapping and positioning, visual sensors can identify the storage location and the type and quantity of steel bars, and inertial navigation can realize the robot's posture control and motion control. LiDAR, visual sensors, and inertial navigation realize the basic functions of the robot. In addition, environmental sensors such as temperature sensors, humidity sensors, and air pressure sensors can be installed to monitor the environmental changes around the robot in real time and improve the robot's adaptability and safety. According to the scene requirements, deep learning algorithms and image recognition technology can be used to enable the robot to recognize steel bars of different shapes and sizes.

[0060] The power system is used to supply power to various systems of the robot.

[0061] The power system includes a battery management subsystem, lithium battery, and charging management subsystem;

[0062] Battery management subsystem, used to monitor the status and health of lithium batteries in real time;

[0063] Lithium batteries, used for charging, discharging and storing electricity;

[0064] The charging management subsystem is used to automatically adjust the charging speed and time according to the power of the lithium battery and the working conditions of the robot.

[0065] Demonstratively, the use of lithium batteries and fast charging technology enables the robot to work for a long time and charge quickly, improving the robot's work efficiency and stability. At the same time, intelligent charging technology is also used, which can automatically adjust the charging speed and charging time according to the robot's power and working conditions. A battery management system is also designed to monitor the status and health of the battery in real time to ensure the battery life and safety of the robot.

[0066] The control module is used to use intelligent control algorithms based on the robot's own motion state, dense mapping of the surrounding environment, and high-precision positioning of the robot, so that the robot can be autonomously controlled and adjusted;

[0067] The positioning module is used to achieve dense mapping of the surrounding environment and high-precision positioning of the robot based on the surrounding environment information of the robot obtained by the sensor module.

[0068] Based on the same inventive concept, please refer to Figure 6 The present invention also provides a control method for a steel bar carrying robot, comprising the following steps:

[0069] Use the sensor module to obtain the robot's surrounding environment information and its own motion status;

[0070] According to the robot's surrounding environment information, the Cartographer algorithm is used to build a three-dimensional map and position the robot, and build SLAM;

[0071] The Cartographer algorithm is used to build a 3D map and locate the robot based on the robot's surrounding environment information. The construction of SLAM includes the following steps:

[0072] Extracting first map information from the robot's surrounding environment information and feature points in the map, and matching the feature points with the map information;

[0073] Converting the first map information into a first robot motion diagram, wherein nodes represent positions of robots and edges represent motions between robots;

[0074] like Figure 4 As shown, a nonlinear optimization algorithm is used to optimize the first robot motion diagram, and a map update and loop detection algorithm is used to update the map information and construct SLAM when the robot passes through an unexplored area.

[0075] Demonstratively, based on lidar and visual sensors, the SLAM algorithm can realize autonomous mapping and positioning of the robot. The Cartographer algorithm converts sensor data into probability distribution, and then uses a Bayesian filter to estimate the position of the robot to obtain the position of the robot. The nonlinear optimization algorithm can optimize the map to improve the precision and accuracy of the map. The Cartographer algorithm is divided into the front end and the back end. The front end is mainly responsible for extracting map information from the robot's sensor data, including lidar data, IMU data, and odometer data, etc. The back end is responsible for optimizing the map to improve the precision and accuracy of the map. It has the advantages of high precision, high efficiency and scalability.

[0076] Based on SLAM, VSLAM is constructed using the ORB-SLAM3 algorithm to achieve dense mapping of the surrounding environment and high-precision positioning of the robot;

[0077] Building VSLAM using the ORB-SLAM3 algorithm based on SLAM to achieve dense mapping of the surrounding environment and high-precision positioning of the robot includes the following steps:

[0078] Using a camera of a robot sensor module to obtain environmental information, and extracting second map information according to the environmental information, wherein the map information includes image feature points, depth information, and camera pose;

[0079] Matching the image feature points with the map information in SLAM to obtain a first intermediate map;

[0080] The first intermediate map is converted into a second robot motion diagram, and the diagram is optimized using a nonlinear optimization algorithm; wherein the nodes in the second robot motion diagram represent the positions of the robots, and the edges represent the motions between the robots;

[0081] like Figure 5 As shown, the bag-of-words model algorithm is used to convert image feature points into a bag of words, and the bag of words is matched with the first intermediate map. When it is detected that the robot passes through an unexplored area, the map information is updated.

[0082] Intelligent control algorithms are used based on the robot's own motion state, dense mapping of the surrounding environment and high-precision positioning of the robot to enable the robot to control and adjust itself autonomously.

[0083] Demonstratively, the ORB-SLAM3 algorithm includes the front-end, back-end and closed-loop detection; the front-end part is mainly responsible for extracting map information from the robot's camera data, including image feature points, depth information and camera pose, etc.; the back-end part is mainly responsible for optimizing the map to improve the map's precision and accuracy; the closed-loop detection part is mainly responsible for detecting whether the robot has passed through an area that has been explored before and updating the map information.

[0084] The steps include using intelligent control algorithms based on dense mapping of the surrounding environment and high-precision positioning of the robot to enable the robot to control and adjust autonomously, and also include:

[0085] Use cloud computing and big data technologies to analyze and process robot data in real time.

[0086] Demonstratively, the robot's automatic control algorithm can control and adjust itself, improving the robot's flexibility and adaptability. Among them, the fuzzy control algorithm and PID control algorithm are used to automatically adjust the robot's speed and direction according to the robot's working conditions. The robot's path planning algorithm is designed, which can automatically plan the robot's movement path and loading order according to the storage location and quantity of the steel bars, improving the robot's work efficiency and accuracy.

[0087] Cloud computing and big data technologies can analyze and process robot data in real time, improve the robot's decision-making ability and intelligence level. When analyzing and processing data, data collection and analysis algorithms are used to collect and analyze the robot's operating data and environmental data in real time, improving the robot's work efficiency and stability.

[0088] During the robot training process, the robot is placed in different environments and terrains for testing to ensure the robot's stability and reliability. Optimizing the robot's hardware and software can improve the robot's work efficiency and performance, and multiple field tests and simulations are conducted to ensure the robot's performance and reliability.

[0089] For the specific implementation process of the above system, please refer to the steel bar carrying robot provided in the embodiment of the present application, which will not be repeated here.

[0090] To summarize, the steel bar carrying robot and control method provided in the embodiment of the present application have designed multiple driving modes for multiple chassis and can adapt to different handling environments; adopted multiple sensors to realize information recognition and autonomous navigation of the robot; constructed SLAM and VSLAM to obtain high-accuracy maps and realize real-time updating of the maps; adopted intelligent control algorithms to realize autonomous control and adjustment of the robot; improved work efficiency, improved the adaptability and safety of the robot, and improved the navigation accuracy and speed of the robot.

[0091] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A steel bar carrying robot, characterized in that: Including robot chassis, positioning module, control module, sensor module and power supply system; The robot chassis is used to carry various equipment of the robot and is responsible for the movement and driving of the robot; The sensor module is used to realize the robot's autonomous navigation and obstacle avoidance, and obtain the robot's surrounding environment information and its own motion status; A power system, used to supply power to various systems of the robot; The control module is used to use intelligent control algorithms based on the robot's own motion state, dense mapping of the surrounding environment, and high-precision positioning of the robot, so that the robot can be autonomously controlled and adjusted; The positioning module is used to achieve dense mapping of the surrounding environment and high-precision positioning of the robot based on the surrounding environment information of the robot obtained by the sensor module.

2. A steel bar carrying robot as claimed in claim 1, characterized in that: A suspension system is also included to stabilize the robot for transport depending on its load and travel speed.

3. A steel bar carrying robot as claimed in claim 1, characterized in that: The robot chassis is provided with a Mecanum wheel drive mode and a rear-wheel drive and front-wheel Ackerman steering drive mode according to the surrounding environment information; when the surrounding environment information shows that the environment is a flat ground, the Mecanum wheel drive mode is used; when the surrounding environment information shows that the environment is a rough ground, the rear-wheel drive and front-wheel Ackerman steering drive mode is used.

4. A steel bar carrying robot as claimed in claim 1, characterized in that: The sensor module includes laser radar, visual sensor, and inertial navigation; LiDAR, used to achieve 3D mapping and positioning of the robot; Vision sensors to identify storage locations and the types and quantities of steel bars; Inertial navigation is used to achieve robot posture control and motion control.

5. The steel bar carrying robot according to claim 1, characterized in that: The power supply system includes a battery management subsystem, a lithium battery and a charging management subsystem; Battery management subsystem, used to monitor the status and health of lithium batteries in real time; Lithium batteries, used for charging, discharging and storing electricity; The charging management subsystem is used to automatically adjust the charging speed and time according to the power of the lithium battery and the working conditions of the robot.

6. A control method for a steel bar transporting robot, applied to a steel bar transporting robot as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: Use the sensor module to obtain the robot's surrounding environment information and its own motion status; According to the robot's surrounding environment information, the Cartographer algorithm is used to build a three-dimensional map and position the robot, and build SLAM; Based on SLAM, VSLAM is constructed using the ORB-SLAM3 algorithm to achieve dense mapping of the surrounding environment and high-precision positioning of the robot; Intelligent control algorithms are used based on the robot's own motion state, dense mapping of the surrounding environment and high-precision positioning of the robot to enable the robot to control and adjust itself autonomously.

7. The control method of a steel bar carrying robot according to claim 6, characterized in that: The steps include using intelligent control algorithms based on dense mapping of the surrounding environment and high-precision positioning of the robot to enable the robot to control and adjust autonomously, and also include: Use cloud computing and big data technologies to analyze and process robot data in real time.

8. The control method of a steel bar carrying robot according to claim 6, characterized in that: The three-dimensional mapping and positioning of the robot using the Cartographer algorithm according to the robot's surrounding environment information, and the construction of SLAM includes the following steps: Extracting first map information from the robot's surrounding environment information and feature points in the map, and matching the feature points with the map information; Converting the first map information into a first robot motion diagram, wherein nodes represent positions of robots and edges represent motions between robots; The motion diagram of the first robot is optimized using a nonlinear optimization algorithm, and the map update and loop detection algorithms are used to update the map information and construct SLAM when the robot passes through an unexplored area.

9. The control method of a steel bar carrying robot according to claim 6, characterized in that: Building VSLAM using the ORB-SLAM3 algorithm based on SLAM to achieve dense mapping of the surrounding environment and high-precision positioning of the robot includes the following steps: Using a camera of a robot sensor module to obtain environmental information, and extracting second map information according to the environmental information, wherein the map information includes image feature points, depth information, and camera pose; Matching the image feature points with the map information in SLAM to obtain a first intermediate map; The first intermediate map is converted into a second robot motion diagram, and the diagram is optimized using a nonlinear optimization algorithm; wherein the nodes in the second robot motion diagram represent the positions of the robots, and the edges represent the motions between the robots; The bag-of-words model algorithm is used to convert image feature points into a bag of words, and the bag of words is matched with the first intermediate map. When it is detected that the robot passes through an unexplored area, the map information is updated.

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