Robot control system and method of robot operating system based on KaiHong OS, computer equipment and medium

Abstract

The invention discloses a robot operating system based on KaiHong OS, a robot control method, computer equipment and a medium. The system comprises an operating system layer, a middle layer and an application layer, the operating system layer adopts a KaiHong OS and provides hardware abstraction, bottom layer driving, bottom layer task scheduling, compiling and equipment driving functions; the middle layer is used for carrying out secondary packaging on the operating system layer and providing standard interfaces which are uniform in format and modularized; and the application layer manages the whole robot operating system through the node manager, and splits and combines the functions of the robot operating system by taking nodes as units. According to the method, the KaiHong OS is used as the OS layer support of the robot operating system, the KaiHong OS is connected with the underlying hardware, the KaiHong OS is operated on the computer hardware, an application layer is kept unchanged, a developer can conveniently develop the robot on the KaiHong OS, and the development efficiency of the robot is improved.

Description

Technical Field

[0001] This invention relates to the field of robot development technology, and in particular to a robot control system, method, computer equipment and medium based on the KaiHong OS robot operating system. Background Technology

[0002] With the development of computer technology, automatic control technology, electronics, and other disciplines, robotics has clearly become a hot topic in contemporary robotics research and has seen significant advancements, finding widespread application in numerous fields. For example, in the construction, logistics, and service sectors, wall-painting robots can replace traditional wall painters; JD.com's unmanned warehouses use AGVs (Automated Guided Vehicles) to transport goods; and robotic vacuum cleaners have entered many homes for cleaning. In the current field of robotics development, ROS (Robot Operating System) is a highly popular software framework for robot software development. ROS is an efficient and flexible framework for writing robot software, integrating a large number of tools, libraries, and protocols. It provides functions similar to an operating system, including hardware abstraction description, low-level driver management, execution of common functions, inter-program message passing, and program distribution package management. This greatly simplifies the creation and stability control of complex tasks on diverse robot platforms. By using ROS, robot systems can better adapt to requirements, significantly facilitating robot software development.

[0003] ROS (Robot Operating System) cannot run directly on computer hardware; it relies on other operating systems. Existing ROS-supporting operating systems can be divided into two categories: Linux and Windows. Ubuntu, based on Linux, is the most mature system for ROS. ROS uses Linux for its OS (operating system) layer, with a middleware layer and application layer deployed on top of it for communication. However, there is currently no ROS system based on KaiHong OS, making it impossible to use ROS for robot development and deployment on a host machine running KaiHong OS.

[0004] Therefore, existing technologies still need improvement. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a robot control method, system, computer equipment, and medium based on the KaiHongOS robot operating system, addressing the aforementioned deficiencies of the prior art. The technical solution adopted by this invention is as follows: In a first aspect, the present invention provides a robot operating system based on KaiHong OS, the robot operating system comprising: an operating system layer, a middleware layer and an application layer; The operating system layer uses KaiHong OS and provides hardware abstraction, low-level drivers, low-level task scheduling, compilation, and device driver functions. The intermediate layer is used to encapsulate the operating system layer and provide a standardized interface with a unified format and modularity. The application layer manages the entire robot operating system through a node manager, and breaks down and combines the functions of the robot operating system on a node-by-node basis.

[0006] In one implementation, the operating system layer provides a customized interface for the intermediate layer to enable the connection between the underlying system and the upper framework.

[0007] In one implementation, the intermediate layer builds a communication system based on a TCP / UDP network, and the communication system integrates TCPROS / UDPROS protocol components.

[0008] In one implementation, the communication system built by the intermediate layer supports a model consisting of publish / subscribe, client and server sides, to implement data transmission methods with different communication mechanisms.

[0009] In one implementation, the node manager has a node registration list and a computation graph lookup function to help distributed nodes automatically discover each other, establish communication connections, and achieve distributed node collaboration.

[0010] In one implementation, the robot operating system includes multiple nodes. When the multiple nodes are running, a communication mechanism is added between the nodes, allowing them to send messages to each other. Each message has a specific data structure, which can be a standard data structure or an array and nested structure.

[0011] In one implementation, the message is transmitted between nodes in a publish-subscribe manner, where a node is either a publisher or a subscriber of the topic.

[0012] In one implementation, the robot operating system uses the distributed soft bus feature of KaiHong OS to acquire environmental information and invoke existing devices within the application environment.

[0013] In one implementation, communication is established between multiple robot operating systems. When multiple robot operating systems simultaneously call existing devices in the application environment, the obtained device information is shared among the multiple robot operating systems.

[0014] In one implementation, the application layer supports adding nodes, and the newly added nodes can establish communication connections with the existing nodes.

[0015] Secondly, embodiments of the present invention also provide a robot control method based on the KaiHongOS robot operating system according to any one of the above solutions, wherein the method includes: The robot acquires existing devices in the application environment, establishes communication connections with the existing devices, uses the sensing components of the existing devices as the robot's sensors, and collects multidimensional data based on the sensing components. The sensing components include any one or more of lidar, infrared sensors, position encoders, and cameras. The multidimensional data is analyzed to obtain analysis results, wherein the analysis results include any one or more of the following: positioning results, target recognition results, path planning results, and distance detection results; The robot is controlled based on the analysis results to meet the user's needs.

[0016] In one implementation, multidimensional data is acquired based on the sensing component, including: The existing device is invoked to determine its sensor components and corresponding functions; The multidimensional data is obtained by collecting environmental information based on the sensor components of the existing equipment.

[0017] In one implementation, the multidimensional data acquired based on the sensing component further includes: If multiple robots call upon the same existing device and use the sensor components of the same existing device as sensors for multiple robots, after one robot collects environmental information based on the sensor components of the existing device, it will share the collected environmental information with other robots.

[0018] In one implementation, controlling the robot based on the analysis results to meet user needs includes: The analysis results are connected to various nodes in the robot's operating system via topics to form a control data flow; The robot's actuators are controlled to perform corresponding operations based on the control data flow.

[0019] In one implementation, the method further includes: Check the communication status of all nodes, and locate the communication fault when the communication status is abnormal; Based on the identified communication fault, global parameters are modified during robot operation.

[0020] In one implementation, the method further includes: Obtain new user input requirements, parse the new requirements, and determine the target function corresponding to the new requirements; Create a new node based on the target function, add the new node to the node registration list, and establish a communication connection with the existing node.

[0021] Thirdly, embodiments of the present invention also provide a computer device, wherein the computer device includes a memory, a processor, and a robot control program based on a robot operating system of KaiHong OS stored in the memory and executable on the processor. When the processor executes the robot control program based on a robot operating system of KaiHong OS, it implements the steps of the robot control method based on a robot operating system of KaiHong OS according to any of the above-mentioned solutions.

[0022] Fourthly, embodiments of the present invention also provide a computer-readable storage medium, wherein a robot control program based on a robot operating system of KaiHong OS is stored on the computer-readable storage medium, and the robot control program based on a robot operating system of KaiHong OS implements the steps of the robot control method based on a robot operating system of KaiHong OS as described in any of the above-described schemes on the computer-readable storage medium.

[0023] Beneficial Effects: Compared with existing technologies, this invention provides a robot operating system based on KaiHong OS, comprising an operating system layer, a middleware layer, and an application layer. The operating system layer uses KaiHong OS and provides hardware abstraction, low-level drivers, low-level task scheduling, compilation, and device driver functions. The middleware layer is used for secondary encapsulation of the operating system layer and provides standardized, modular interfaces. The application layer manages the entire robot operating system through a node manager, and decomposes and combines the functions of the robot operating system on a node-by-node basis. This invention uses KaiHong OS as the OS layer support for the robot operating system, connects to the underlying hardware through KaiHong OS, runs on computer hardware while keeping the application layer unchanged, facilitating robot development for developers and improving robot development efficiency. Attached Figure Description

[0024] Figure 1 This is an architecture diagram of a robot operating system based on KaiHong OS provided for an embodiment of the present invention.

[0025] Figure 2This is a schematic diagram illustrating the communication relationship between nodes in a robot operating system based on KaiHong OS, provided for an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram illustrating the use of existing devices in the application environment by a robot operating system based on KaiHong OS, as provided in an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram illustrating how a device is invoked by multiple robots when applied in an embodiment of the present invention.

[0028] Figure 5 This diagram illustrates the application environment of the robot control method based on the KaiHong OS robot operating system provided in this embodiment of the invention.

[0029] Figure 6 This is a flowchart of a preferred embodiment of a robot control method based on the KaiHong OS robot operating system provided in this invention.

[0030] Figure 7 This is a flowchart illustrating the multi-dimensional data acquisition steps in the robot control method based on the KaiHong OS robot operating system provided in an embodiment of the present invention.

[0031] Figure 8 This is a schematic diagram illustrating the process of controlling a robot in a robot control method based on the KaiHong OS robot operating system provided in an embodiment of the present invention.

[0032] Figure 9 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0034] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content, operations, or steps, nor does it require execution in the described order. For example, some operations or steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0035] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. For example, the first control information and the second control information are only used to distinguish different control information and do not limit their order. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or the order of execution, and that the words "first" and "second" do not necessarily imply that they are different. It should also be understood that the terms "and / or" as used in this specification and the appended claims refer to any combination of one or more of the associated listed items and all possible combinations, and include such combinations.

[0036] With the development of computer technology, automatic control technology, electronics and other disciplines, robots have been applied in many industries, and robot development has become a hot topic. In the existing robot development environment, ROS is undoubtedly the most popular among developers. ROS integrates a large number of tools, libraries and protocols, and the number of function packages in the ROS community is growing rapidly. ROS can help users quickly complete the creation, programming and multi-robot integration of robot applications.

[0037] In current robot development and deployment, ROS is widely used, and it already has a well-established open-source community. However, there is currently no ROS system based on KaiHong OS. Hosts or devices using KaiHong OS cannot use ROS, meaning they cannot utilize ROS to provide the hardware abstraction, low-level drivers, message passing, program management, application prototyping, and many third-party tools and libraries needed for robot development. Many drivers, application algorithms, and functional modules are difficult to port or further develop in other robots or applications, significantly increasing the difficulty of robot development. Without a framework, many drivers, application algorithms, and functional modules will only be used for specific robots, making them difficult to port or further develop in other robots or applications, drastically reducing the usability of the software code.

[0038] Therefore, this embodiment provides a robot operating system based on KaiHong OS, such as... Figure 1As shown, the robot operating system adopts a three-layer architecture, including: the operating system layer (OS layer), the middleware layer, and the application layer. Specifically, the OS layer provides hardware abstraction and low-level drivers within the Robot Operating System (ROS) framework, but the underlying task scheduling, compilation, device drivers, etc., all rely on the actual operating system. Existing ROS OS layers use Linux and Windows. This embodiment replaces the OS layer's operating system with KaiHong OS and provides customized interfaces for the middleware layer to connect the underlying system with the upper-level framework. Furthermore, through the encapsulation of the middleware layer, robot software developers can ignore the differences in the OS layer when using the application layer. They can utilize a large number of robot application function packages shared by the robot operating system community. Modules within these packages run as nodes, using the standard input / output of the robot operating system as interfaces. Developers do not need to concern themselves with the internal implementation details of the modules; they only need to understand the interface rules for reuse.

[0039] In other implementations, for low-latency scenarios such as industrial control and medical surgery (e.g., robot joint control requiring millisecond-level response), real-time kernel patches (e.g., RT_PREEMPT) can be integrated into the KaiHong OS underlying layer. This reduces the OS layer's task scheduling latency from milliseconds to microseconds, meeting high-precision control requirements. For example, the robotic arm movements of a surgical robot need to follow the doctor's instructions in real time without delay or deviation. In addition to basic drivers, the OS layer can also extend to native support for industrial buses and automotive buses. For instance, in industrial robot scenarios, the OS layer can directly adapt to multi-axis motor controllers via the EtherCAT bus without requiring additional adapter modules, reducing hardware costs and communication latency.

[0040] In this embodiment, the intermediate layer sits between the OS layer and the application layer, responsible for connecting them. The intermediate layer of the robot operating system further encapsulates the operating system layer and provides standardized, modular interfaces. The most important component is the communication system, which is a further encapsulation of the robot operating system based on TCP / UDP networks. This intermediate layer builds a communication system based on TCP / UDP networks, integrating TCPROS / UDPROS protocol components. The communication system built by the intermediate layer supports publish / subscribe, client / server models, and data transmission methods for different communication mechanisms. Regarding communication mechanisms, the robot operating system provides many development-related libraries for the application layer. For example, the intermediate layer encapsulates the motor control interface and radar data interface of the OS layer, outputting standard service / topic interfaces of the robot operating system. For instance, it encapsulates lidar ranging data as the " / lidar_data" topic and motor speed control as the " / set_motor_speed" service.

[0041] In other implementations, besides the TCPROS / UDPROS protocol components, the middleware layer can add support for MQTT (a publish-subscribe messaging protocol) and DDS (data distribution service). MQTT is suitable for low-bandwidth communication between robots and the cloud (such as remote monitoring of robot status), while DDS is suitable for high-reliability, low-latency multi-robot collaboration (such as path collaboration among multiple warehouse robots in a workshop, requiring millisecond-level data synchronization). Furthermore, a cloud-edge collaboration interface can be added to the middleware layer to enable bidirectional data flow between robots (edge ​​devices) and the cloud. For example, equipment inspection data collected by edge robots can be uploaded to the cloud through the middleware layer, where big data analysis (such as equipment fault prediction) is performed. The optimized inspection route is then distributed back to the edge robots, forming a closed loop of edge data collection, cloud analysis, and edge execution. Additionally, the middleware layer can connect to edge computing gateways to deploy some computing tasks (such as multi-robot data fusion) on edge nodes, reducing the local computing pressure on robots. For example, for 10 inspection robots in a park, each robot does not need to perform global path planning; instead, the edge gateway calculates the global path and distributes it to each robot, avoiding wasted computing power. The application layer has a node manager (Master) that manages the entire robot operating system and breaks down and combines the functions of the robot operating system on a node-by-node basis. The node manager has a node registration list and computation graph lookup function to help distributed nodes automatically discover each other, establish communication connections, and achieve distributed node collaboration. The robot operating system includes multiple nodes; a node refers to a process that performs computational tasks. Typically, a robot software consists of multiple nodes. When multiple nodes are running, a communication mechanism is added between them, allowing nodes to send messages to each other. Each message has a specific data structure, which can be a standard data structure or an array and nested structure, and users can also define their own according to their needs. Messages are transmitted between nodes in a publish-subscribe manner. A node is either a publisher or a subscriber to a topic. Publishers and subscribers are unaware of each other, and multiple nodes may simultaneously subscribe to or publish messages on the same topic. The stable execution of nodes in the robot operating system depends on the node manager of the robot operating system. The node manager provides registration list and search functions for other computation graphs through remote calls, thereby realizing the search and connection between nodes of the robot operating system. At the same time, it also provides a parameter server for the robot operating system to manage global parameters.

[0042] Furthermore, a node is a process within a robot's operating system, and also a unit of functional modules within the robot's operating system software. Robot software developers focus primarily on the communication messages between nodes during development. Each node operates independently, and they are interconnected in a topological manner to form a system network. For example, sensor data acquisition nodes, data processing nodes, and execution control nodes can be logically linked through the topology to form a complete robot control flow. The communication relationships between nodes are as follows: Figure 2 As shown, developers write different nodes during the development process. Each node can publish topics (becoming a publisher) or subscribe to topics (becoming a subscriber). Nodes communicate with each other through topics; a topic can have multiple publishers and multiple subscribers. The robot operating system developer community has many open-source function packages containing corresponding node message services and other information. Robot software developers can use these open-source function packages for simple and efficient development, adding them to the robot software as nodes. This enables function package reuse, reduces the workload of robot software developers, lowers the difficulty of robot software development, and makes the robot software easier to port across different device systems.

[0043] Furthermore, nodes can be started and stopped independently, and collaborate with other nodes through communication mechanisms. This facilitates individual debugging and supports flexible expansion of complex functions. The application layer also supports adding nodes, which can establish communication connections with existing nodes. For example, adding a voice interaction node can quickly expand the robot's voice capabilities.

[0044] This embodiment's application layer is compatible with the open-source ecosystem of the robot operating system community, providing developers with the ability to quickly integrate ready-made functional modules. Specific functions include: 1. Direct reuse of open source function packages: Supports calling open source function packages from the robot operating system community (containing complete resources such as nodes, message formats, and service logic). Developers can directly add the nodes in the function packages to their own robot software without having to develop from scratch (such as directly reusing mature function packages such as LiDAR SLAM and path planning). 2. Cross-device portability support: Due to the standardized node interface and modular functions, reusable function packages / nodes can be quickly ported between robots on different KaiHong OS devices, improving the usability of software code and reducing the amount of repetitive development work.

[0045] Furthermore, the distributed soft bus feature of KaiHong OS in this embodiment will also play a significant role in robot development and use. The robot operating system can leverage the distributed soft bus feature of KaiHong OS to acquire environmental information and invoke existing devices within the application environment. In other words, a robot no longer needs numerous peripherals to acquire environmental information; it can invoke existing devices within the application environment within a certain range, and even obtain more accurate information by invoking multiple devices. For example... Figure 3As shown, robots can utilize existing equipment in the application environment, such as cameras, camcorders, microphones, and speakers. When simulating human work, robots need to perceive external information like humans; only after receiving correct information input can the robot complete its task. Therefore, combining robots with the sensing components of existing equipment can effectively improve the robot's work efficiency. For example, combining computer vision with robotics is a fundamental yet crucial function in target recognition for mobile robots. However, if only the robot's own cameras and radar sensors are available, the information acquired for target recognition is limited. If the robot can utilize more cameras, radar, and infrared sensors in the environment, it can identify objects from multiple dimensions and angles, resulting in higher accuracy in target recognition. Simultaneously, the cost of the robot itself will decrease. Based on this, depending on the usage scenario, it is no longer necessary for every robot to be equipped with cameras, microphones, and other sensor components. When needed, existing equipment in the environment can be utilized, leveraging their cameras, microphones, and other sensor components to achieve environmental information perception.

[0046] Furthermore, the same device in this embodiment may not only serve one robot; it can be invoked by multiple robots, and even send the obtained information to multiple robots simultaneously. Communication is established between multiple robot operating systems, and when multiple robot operating systems simultaneously invoke existing devices in the application environment, the obtained device information is shared among the multiple robot operating systems. Specifically, as follows... Figure 4 As shown, Figure 4 The demonstration shows a radar device being used by multiple different robots. When one robot activates the radar device, it can obtain relevant information. If multiple robots need the same radar device, the information obtained can be simultaneously sent to all robots for use, enabling information sharing among them. In this way, by strategically placing the number of robots equipped with radar devices in an environment, each robot can utilize the radar sensor.

[0047] Furthermore, in this unique implementation, the application layer can develop a node template library, encapsulating commonly used functions (such as sensor data acquisition and motor control) into standardized templates. Developers can quickly generate nodes by selecting templates and configuring parameters, such as radar sampling frequency and motor speed limit, through visual tools (such as drag-and-drop interfaces), without writing code. This lowers the barrier to entry for non-professional developers, enabling small and medium-sized enterprises to quickly build simple sorting robots. A multi-robot collaborative node manager can also be added to the application layer to replace the node manager for a single robot, enabling global task allocation and resource scheduling for multiple robots. For example, in a warehouse with three storage robots, the collaborative node manager allocates tasks based on order priority: storage robot 1 is responsible for receiving, storage robot 2 for shipping, and storage robot 3 for replenishing, dynamically adjusting paths to avoid collisions.

[0048] Based on the above embodiments, the present invention also provides a robot control method based on the KaiHong OS robot operating system. This robot control method based on the KaiHong OS robot operating system can be applied to… Figure 5 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on other network servers. The robot control method based on the KaiHong OS robot operating system can be executed by terminal 102 or server 104, or it can be executed collaboratively by terminal 102 and server 104.

[0049] The terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, IoT device, or portable wearable device. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices, etc.

[0050] Server 104 can be an independent physical server, a cloud server, or a service node in a blockchain system, where the service nodes in the blockchain system form a peer-to-peer network.

[0051] In addition, server 104 can also be a server cluster consisting of multiple physical servers, which can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0052] Terminal 102 and server 104 can be connected via Bluetooth, USB (Universal Serial Bus) or network, etc., and this application does not impose any restrictions.

[0053] In some embodiments, such as Figure 6 The diagram illustrates a robot control method based on the KaiHong OS robot operating system. This method can be applied to a terminal device for robot control, which can be an intelligent product such as a computer. Specifically, the method includes the following steps: Step S100: Obtain existing devices in the application environment, establish a communication connection with the existing devices, use the sensing components of the existing devices as the robot's sensors, and collect multi-dimensional data based on the sensing components. The sensing components include any one or more of lidar, infrared sensors, position encoders, and cameras. Step S200: Analyze the multidimensional data to obtain analysis results, wherein the analysis results include any one or more of the following: positioning results, target recognition results, path planning results, and distance detection results; Step S300: Control the robot based on the analysis results to meet the user's needs.

[0054] Because the robot in this embodiment is equipped with the robot operating system described in the previous embodiment, this robot operating system adopts a three-layer architecture design, including: an operating system layer (OS layer), a middleware layer, and an application layer. The operating system layer uses KaiHong OS and provides hardware abstraction, low-level drivers, low-level task scheduling, compilation, and device driver functions. The middleware layer is used to further encapsulate the operating system layer and provides a standardized, modular interface. The application layer manages the entire robot operating system through a node manager and decomposes and combines the functions of the robot operating system on a node-by-node basis. Furthermore, the distributed soft bus feature of KaiHong OS will play a significant role in robot development and use. The robot operating system can acquire environmental information and call existing devices within the application environment based on the distributed soft bus feature of KaiHong OS. In other words, a robot no longer needs too many peripherals to acquire environmental information; the robot can call existing devices in the application environment within a certain range, and can even obtain more accurate information by calling multiple devices.

[0055] Based on this, the robot can acquire existing devices in the application environment, establish a communication connection with the existing devices, use the sensing components of the existing devices as the robot's sensors, and collect multi-dimensional data based on the sensing components. The sensing components include any one or more of lidar, infrared sensors, position encoders, and cameras.

[0056] like Figure 7 As shown, the following steps are included when collecting multidimensional data: Step S101: Call up the existing device to determine the sensor components and corresponding functions of the existing device; Step S102: Collect environmental information based on the sensor components of the existing equipment to obtain the multidimensional data.

[0057] In practical applications, before calling upon existing devices, this embodiment first identifies which devices exist in the application environment, then determines the sensor components and corresponding functions of the existing devices, understanding their roles; for example, lidar can be used for ranging, and cameras can be used for target identification. Next, environmental information is collected based on the sensor components of the existing devices to obtain the multidimensional data.

[0058] In this embodiment, the same device may serve more than one robot; it can be invoked by multiple robots, and even simultaneously send the obtained information to multiple robots. Communication is established between multiple robot operating systems. When multiple robot operating systems simultaneously invoke existing devices in the application environment, the acquired device information is shared among the multiple robot operating systems. Therefore, if multiple robots invoke the same existing device and use the sensor components of the same existing device as sensors for multiple robots, after one robot collects environmental information based on the sensor components of the existing device, it shares the collected environmental information with other robots. For example... Figure 4 In this scenario, a single radar device is used by multiple different robots. When one robot activates the radar device, it can acquire relevant information. If multiple robots require the same radar device, the information acquired can be simultaneously sent to all robots for use, enabling information sharing among them. Thus, by strategically deploying the number of robots equipped with radar devices within an environment, each robot can utilize the radar sensor.

[0059] Furthermore, after collecting the multidimensional data, this embodiment can analyze this multidimensional data to obtain analysis results, including any one or more of the following: localization results, target recognition results, path planning results, and distance detection results. In other words, the robot in this embodiment can perform functions such as localization analysis, distance detection, target recognition, and path planning. Next, since the robot operating system in this embodiment includes multiple nodes, each node refers to a process that performs computational tasks. Typically, robot software consists of multiple nodes. When multiple nodes are running, a communication mechanism is added between them, allowing nodes to send messages to each other. Each message has a specific data structure, which can be a standard data structure or an array and nested structure, and users can also define their own according to their needs. Messages are transmitted between nodes in a publish-subscribe manner. A node is either a publisher or a subscriber to a topic. Publishers and subscribers are unaware of each other, and multiple nodes may simultaneously subscribe to or publish the same topic message. In a robot operating system, the stable execution of nodes relies on the system's node manager. The node manager provides access to a registry and searches other computational graphs via remote calls, enabling the lookup and connection establishment between nodes. It also provides a parameter server to manage global parameters. Robot software developers focus primarily on communication messages between nodes. Each node operates independently, but they are interconnected in a topological manner to form a system network. For example, sensor data acquisition nodes, data processing nodes, and execution control nodes can be logically linked through the topology to form a complete robot control flow. The communication relationships between nodes are as follows: Figure 2 As shown, developers write different nodes during the development process. Each node can publish topics (becoming a publisher) or subscribe to topics (becoming a subscriber). Nodes communicate with each other through topics; a topic can have multiple publishers and multiple subscribers. The robot operating system developer community has many open-source function packages containing corresponding node message services and other information. Robot software developers can use these open-source function packages for simple and efficient development, adding them to the robot software as nodes. This enables function package reuse, reduces the workload of robot software developers, lowers the difficulty of robot software development, and makes the robot software easier to port across different device systems.

[0060] like Figure 8 As shown, controlling the robot includes the following steps: Step S301: Connect the analysis results to each node in the robot operating system through topics to form a control data flow; Step S302: Control the robot's actuator to perform the corresponding operation based on the control data flow.

[0061] In other words, the nodes in the robot operating system are connected through topics. Therefore, in this embodiment, the analysis results can be connected to the nodes in the robot operating system through topics to form a control data flow. Then, the robot's actuators are controlled to perform corresponding operations based on the control data flow. For example, the application layer in the robot operating system of this embodiment constructs the control logic for path navigation and obstacle avoidance of the warehouse robot through "node splitting + message communication." This is the core link of robot control. According to the requirements of path navigation and obstacle avoidance, four core nodes are split (each node runs independently for easy debugging and reuse): radar data acquisition node, obstacle avoidance decision node, path planning node, and motor control node. If the control data flow formed by connecting the nodes through topics is normal navigation → encountering an obstacle → obstacle avoidance → resuming navigation, then the corresponding nodes will control the robot's actuators to achieve the corresponding stages. For example, in the normal navigation stage, the path planning node calculates the target speed and then issues navigation instructions through topics; then, the motor control node subscribes to topics, receives instructions, calls the middle layer, and controls the motor to perform the corresponding actions. During obstacle encounters, the radar data acquisition node publishes a topic in real time (including information such as "obstacle 1m ahead"). The obstacle avoidance decision node then subscribes to this topic, assesses the obstacle risk, and issues obstacle avoidance commands through the topic. Ideally, the motor control node responds to this topic first (this can be configured via node priority), suspends navigation commands, and executes the obstacle avoidance action. After obstacle avoidance is completed during navigation resumption, the obstacle avoidance decision node stops publishing the corresponding topic; the motor control node resumes receiving navigation commands and continues driving along the original path.

[0062] In one implementation, this embodiment can also view the communication status of all nodes and locate communication faults when the communication status is abnormal. Then, based on the located communication faults, global parameters are modified during robot operation to address robot malfunctions promptly. Furthermore, nodes in the robot operating system can be started and stopped independently, while collaborating with other nodes through communication mechanisms, facilitating individual debugging and supporting flexible expansion of complex functions. The application layer supports adding nodes, and newly added nodes can establish communication connections with existing nodes. For example, adding a voice interaction node can quickly expand the robot's voice capabilities. In specific applications, this embodiment can acquire new user input requirements, parse these requirements, and determine the target function corresponding to them. Then, based on the target function, a new node is created purposefully, added to the node registration list, and a communication connection is established with the existing nodes.

[0063] This embodiment is based on the KaiHong OS robot operating system. Its core application leverages a three-layer architecture and a distributed soft bus to enable efficient deployment of robots across the entire process, from hardware adaptation and function development to collaborative control and scenario expansion. This covers various robot scenarios, including home services, industrial inspection, and commercial services. Taking home service robots (such as robotic vacuum cleaners and companion robots) as an example, using this robot operating system eliminates the need to equip each robot with complete sensors. Specifically, robotic vacuum cleaners can access existing smart cameras in the home via the KaiHong OS distributed soft bus to obtain visual information from room corners and optimize their cleaning path using their own radar. Companion robots can utilize the microphone of a smart home speaker to improve the pickup range and accuracy of voice interaction.

[0064] Taking industrial inspection robots, such as workshop equipment inspection robots, as an example, multi-robot collaboration and equipment sharing can be achieved. Specifically, multiple inspection robots in a workshop can share an infrared thermal imager deployed in a key area to simultaneously acquire equipment temperature data, without each robot needing to be equipped with one; warehouse robots can utilize the lidar within the warehouse to improve shelf positioning accuracy.

[0065] Taking commercial service robots (such as shopping mall guides and restaurant delivery robots) as an example, they can achieve multi-dimensional environmental perception. Shopping guide robots can access multiple cameras within the mall to simultaneously acquire pedestrian flow data from different areas and recommend optimal routes. Delivery robots can utilize the restaurant's Bluetooth positioning devices to improve the accuracy of table location. Furthermore, when adding a voice broadcast function for menu descriptions, only a voice broadcast node needs to be added at the application layer, which communicates with the existing path navigation node and order receiving node via topic communication, without requiring modifications to the underlying driver.

[0066] In addition, the robot based on the KaiHong OS operating system in this embodiment can also break through the limitations of a single OS ecosystem and link with ecosystems such as smart homes, industrial internet, and AI platforms to form a collaborative ecosystem with resource sharing and complementary functions. Specific examples are as follows: Example 1: Integrating with the smart home ecosystem, the robot acts as a central control node. The robot serves as the central hub for smart home integration, connecting to smart home devices (such as smart lights, air conditioners, and door locks) via the KaiHong OS soft bus. This adds a home control node to the robot's operating system and application layer. The home service robot recognizes the user's arrival via its camera, automatically initiating home control actions, triggering the unlocking of smart locks, activation of air conditioners, and turning on of lights. Sensors from smart home devices (such as kitchen smoke detectors) send alarm signals to the robot. Upon receiving these signals, the robot's emergency node automatically checks the kitchen and informs the user via voice.

[0067] Example 2: Integration with industrial internet platforms for robot data access and remote operation and maintenance. The robot data (such as equipment operating status and fault logs) from the robot operating system in this embodiment is connected to the industrial internet platform through an industrial protocol interface (such as OPCUA) in the middle layer. The platform collects real-time data on the robot's motor temperature and operating time, and predicts equipment faults through big data analysis. For example, if the motor temperature is too high, it will push a maintenance reminder in advance. The platform can issue ROS function packages (such as adding product quality inspection nodes) to multiple robots to achieve batch upgrades without the need for on-site operation for each robot, thus improving operation and maintenance efficiency.

[0068] Example 3. Collaboration with AI platforms for edge AI inference and model iteration The robot in this embodiment can interface with an AI development platform, encapsulating trained AI models (such as object detection and semantic segmentation) as nodes in the robot's operating system and deploying them to the robot. The robot runs a real-time object detection node locally (based on a lightweight AI model), enabling real-time recognition of pedestrians and obstacles without relying on the cloud, thus reducing latency. The robot uploads unrecognized sample data to the AI ​​platform. After the platform retrains the model, it distributes the optimized model back to the robot, updating the object detection node and continuously improving recognition accuracy. For example, a shopping mall guide robot can gradually recognize more types of goods.

[0069] Furthermore, this embodiment can also upgrade the KaiHong OS-based robot system from simply executing instructions to autonomous perception, decision-making, and learning by integrating AI algorithms, reinforcement learning, and other technologies, thereby improving the robot's intelligence level. Specific application scenarios are illustrated below: When shopping mall guide robots respond to changes in pedestrian flow, the application layer can add dynamic decision nodes and integrate reinforcement learning algorithms. The robot can autonomously adjust its behavior by collecting real-time data such as pedestrian density and path congestion. For example, during peak hours, the guide robot can prioritize guiding customers to less crowded stores. The middle layer can also add an environmental feature extraction module to perform feature analysis on sensor data (cameras, radar), such as distinguishing between pedestrians, fixed obstacles, and temporary shelves, providing accurate input for the decision nodes.

[0070] In scenarios involving home companion robots interacting with the elderly, and industrial inspection robots collaborating with maintenance personnel, the application layer can add multimodal interaction nodes, integrating voice, vision, and tactile data, including: voice recognition (understanding user commands), visual recognition (judging user gestures or expressions), and tactile feedback (such as providing gentle vibrations when an elderly person touches the robot). For example, if an elderly person says "turn on the TV" while pointing towards the living room, the robot can confirm the intention through voice and visual fusion to avoid mis-execution, such as preventing the robot from misinterpreting turning on the bedroom TV as turning on the living room TV.

[0071] In scenarios involving collaborative mapping using multiple drones and collaborative disaster relief using multiple robots, the application layer can build a collaborative framework. Leveraging the distributed nature of robot operating systems, this framework enables decentralized collaboration among multiple robots. For example, when 10 drones are used for mapping, no central node is needed for scheduling. Each drone can autonomously plan unmapped areas by publishing its own location and a topic about already mapped areas, avoiding redundant work. The middle layer can add a group communication optimization module, employing distributed message synchronization technology to ensure that multiple robots can maintain data communication even in environments with weak signals (such as disaster relief in mountainous areas), preventing collaborative interruptions.

[0072] Furthermore, the robot operating system of this embodiment can also be applied to scenarios such as device access security, data transmission security, and node operation security. In specific implementations, if applied to device access security scenarios, such as for identity authentication and access control, the OS layer can add a device identity authentication module. All devices connected to the soft bus (such as robots, sensors, and cloud nodes) must be authenticated via key before access. The middle layer adds an access control interface, where different devices have different operating permissions. For example, environmental sensors can only publish data and cannot call the robot's motor control service, preventing unauthorized devices from tampering with control commands. In industrial scenarios, this prevents unauthorized devices from controlling the robot and causing production accidents. If applied to data transmission security scenarios, such as for encryption and integrity verification, the middle layer of the robot operating system uses end-to-end encryption for all communication data (such as sensor data and control commands), for example, using the AES-256 algorithm, and adds a data integrity check code to ensure that data is not tampered with or stolen during transmission. In medical scenarios, this prevents the interception of surgical robot control commands and the leakage of patient privacy data. In scenarios where node operation safety is paramount, such as for fault self-healing and redundancy backup, the application layer can add fault detection and self-healing nodes to monitor the operational status of each functional node in real time. If the motor control node crashes, the backup node is automatically restarted and the most recent operating parameters (such as the current motor speed) are loaded to prevent the robot from suddenly stopping. Critical nodes (such as the Master and multi-machine collaborative nodes) employ dual-node hot standby; if the primary node fails, the backup node seamlessly takes over, ensuring uninterrupted system operation.

[0073] Furthermore, this embodiment can also be applied to miniaturized, low-power scenarios. Lightweight modifications can adapt the robot operating system to meet low-computing-power and low-power requirements. For example, by lightweighting the nodes of the robot operating system, redundant functions can be removed (such as retaining only core communication and control logic), compressing the node size from MB to KB, adapting it to low-computing-power hardware such as MCUs. For instance, in small wearable robots, an MCU can be used as the main controller to run lightweight posture control nodes. When optimizing low-power node scheduling, low-power scheduling strategies can be added to the OS layer to dynamically adjust hardware power consumption based on the robot's task load. For example, when the robot is in standby mode, some sensors can be turned off, and the CPU frequency reduced; when performing tasks, high-performance mode can be restored. Communication protocols can be optimized for the intermediate layer to reduce data transmission volume. For example, incremental data transmission can be used, sending only changed sensor data instead of the full data, reducing energy consumption. For example, outdoor inspection robots can extend their battery life to over 12 hours through low-power optimization. In addition, lightweight robot operating system nodes can be deployed to edge sensors (such as smart cameras and wireless radar). After the sensors perform data preprocessing locally (such as filtering noise and extracting key features), the simplified data is sent to the robot, reducing the robot's computing power and communication burden. For example, after a smart camera identifies a pedestrian locally, it only sends the pedestrian's location to the robot, rather than the complete image data.

[0074] Therefore, it is evident that the expansion of the KaiHong OS-based operating system in this embodiment consistently revolves around the KaiHong OS distributed soft bus as a link, and is based on the modularity and open-source ecosystem of the operating system, extending towards greater precision, collaboration, intelligence, security, and lightweight design. Whether it's the deepening of the technical architecture, the coverage of cross-domain scenarios, or the ecosystem collaboration and intelligent upgrades, the essence is to solve the pain points of adapting traditional robot operating systems to KaiHong OS devices through software and hardware collaboration and ecosystem linkage, while breaking through the functional boundaries of a single robot and meeting the needs of different industries for collaborative, intelligent, and low-cost robot solutions.

[0075] In summary, this embodiment uses KaiHong OS as the OS layer support for the robot's operating system. By connecting KaiHong OS to the underlying hardware and running on the computer hardware while keeping the application layer unchanged, it facilitates robot development for developers and improves robot development efficiency. Furthermore, based on the distributed soft bus characteristics of KaiHong OS, it makes communication and collaboration between robots easier, further enhancing robot utilization efficiency.

[0076] In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a robot control method based on the KaiHong OS robot operating system. The display unit of the computer device is used to form a visually visible image. It can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0077] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0078] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps: The robot acquires existing devices in the application environment, establishes communication connections with the existing devices, uses the sensing components of the existing devices as the robot's sensors, and collects multidimensional data based on the sensing components. The sensing components include any one or more of lidar, infrared sensors, position encoders, and cameras. The multidimensional data is analyzed to obtain analysis results, wherein the analysis results include any one or more of the following: positioning results, target recognition results, path planning results, and distance recognition results; The robot is controlled based on the analysis results to meet the user's needs.

[0079] In one implementation, multidimensional data is acquired based on the sensing component, including: The existing device is invoked to determine its sensor components and corresponding functions; The multidimensional data is obtained by collecting environmental information based on the sensor components of the existing equipment.

[0080] In one implementation, the multidimensional data acquired based on the sensing component further includes: If multiple robots call upon the same existing device and use the sensor components of the same existing device as sensors for multiple robots, after one robot collects environmental information based on the sensor components of the existing device, it will share the collected environmental information with other robots.

[0081] In one implementation, controlling the robot based on the analysis results to meet user needs includes: The analysis results are connected to various nodes in the robot's operating system via topics to form a control data flow; The robot's actuators are controlled to perform corresponding operations based on the control data flow.

[0082] In one implementation, the method further includes: Check the communication status of all nodes, and locate the communication fault when the communication status is abnormal; Based on the identified communication fault, global parameters are modified during robot operation.

[0083] In one implementation, the method further includes: Obtain new user input requirements, parse the new requirements, and determine the target function corresponding to the new requirements; Create a new node based on the target function, add the new node to the node registration list, and establish a communication connection with the existing node.

[0084] It should be noted that the user information (including but not limited to user device function information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0085] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0086] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robot operating system based on KaiHong OS, characterized in that, The robot operating system includes: an operating system layer, a middleware layer, and an application layer; The operating system layer uses KaiHong OS and provides hardware abstraction, low-level drivers, low-level task scheduling, compilation, and device driver functions. The intermediate layer is used to encapsulate the operating system layer and provide a standardized interface with a unified format and modularity. The application layer manages the entire robot operating system through a node manager, and breaks down and combines the functions of the robot operating system on a node-by-node basis.

2. The robot operating system based on KaiHong OS according to claim 1, characterized in that, The operating system layer provides customized interfaces for the middleware layer to enable the connection between the underlying system and the upper-level framework.

3. The robot operating system based on KaiHong OS according to claim 1, characterized in that, The intermediate layer is built on a TCP / UDP network to construct a communication system, which integrates the TCORS / UDPROS protocol components.

4. The robot operating system based on KaiHong OS according to claim 3, characterized in that, The communication system built by the intermediate layer supports a model consisting of publish / subscribe, client and server sides, and is used to implement data transmission methods with different communication mechanisms.

5. The robot operating system based on KaiHong OS according to claim 1, characterized in that, The node manager has a node registration list and computation graph search function, which helps distributed nodes automatically discover each other, establish communication connections, and realize the collaboration of distributed nodes.

6. The robot operating system based on KaiHong OS according to claim 5, characterized in that, The robot operating system includes multiple nodes. When multiple nodes are running, a communication mechanism is added between the nodes, allowing them to send messages to each other. Each message has a specific data structure, which can be a standard data structure or an array and nested structure.

7. The robot operating system based on KaiHong OS according to claim 6, characterized in that, The messages are transmitted between nodes in a publish-subscribe manner, where a node is either a publisher or a subscriber of a topic.

8. The robot operating system based on KaiHong OS according to claim 1, characterized in that, The robot operating system, based on the distributed soft bus feature of KaiHong OS, enables the acquisition of environmental information and the invocation of existing devices within the application environment.

9. The robot operating system based on KaiHong OS according to claim 8, characterized in that, Communication is established between multiple robot operating systems. When multiple robot operating systems simultaneously call existing devices in the application environment, the obtained device information is shared among the multiple robot operating systems.

10. The robot operating system based on KaiHong OS according to claim 1, characterized in that, The application layer supports adding nodes, and newly added nodes can establish communication connections with existing nodes.

11. A robot control method based on the KaiHong OS robot operating system as described in any one of claims 1-10, characterized in that, The method includes: The robot acquires existing devices in the application environment, establishes communication connections with the existing devices, uses the sensing components of the existing devices as the robot's sensors, and collects multidimensional data based on the sensing components. The sensing components include any one or more of lidar, infrared sensors, position encoders, and cameras. The multidimensional data is analyzed to obtain analysis results, wherein the analysis results include any one or more of the following: positioning results, target recognition results, path planning results, and distance detection results; The robot is controlled based on the analysis results to meet the user's needs.

12. The robot control method based on the KaiHong OS robot operating system according to claim 11, characterized in that, Multidimensional data is acquired based on the sensing components, including: The existing device is invoked to determine its sensor components and corresponding functions; The multidimensional data is obtained by collecting environmental information based on the sensor components of the existing equipment.

13. The robot control method based on the KaiHong OS robot operating system according to claim 12, characterized in that, Based on the multidimensional data acquired by the aforementioned sensing component, the system also includes: If multiple robots call upon the same existing device and use the sensor components of the same existing device as sensors for multiple robots, after one robot collects environmental information based on the sensor components of the existing device, it will share the collected environmental information with other robots.

14. The robot control method based on the KaiHong OS robot operating system according to claim 11, characterized in that, Controlling the robot based on the analysis results to meet user needs includes: The analysis results are connected to various nodes in the robot's operating system via topics to form a control data flow; The robot's actuators are controlled to perform corresponding operations based on the control data flow.

15. The robot control method based on the KaiHong OS robot operating system according to claim 14, characterized in that, The method further includes: Check the communication status of all nodes, and locate the communication fault when the communication status is abnormal; Based on the identified communication fault, global parameters are modified during robot operation.

16. The robot control method based on the KaiHong OS robot operating system according to claim 14, characterized in that, The method further includes: Obtain new user input requirements, parse the new requirements, and determine the target function corresponding to the new requirements; Create a new node based on the target function, add the new node to the node registration list, and establish a communication connection with the existing node.

17. A computer device, characterized in that, The computer device includes a memory, a processor, and a robot control program based on the KaiHong OS robot operating system stored in the memory and executable on the processor. When the processor executes the robot control program based on the KaiHong OS robot operating system, it implements the steps of the robot control method based on the KaiHong OS robot operating system as described in any one of claims 11-16.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a robot control program based on the KaiHong OS robot operating system, and the robot control program based on the KaiHong OS robot operating system implements the steps of the robot control method based on the KaiHong OS robot operating system as described in any one of claims 11-16 on the computer-readable storage medium.

Images