Method for Task Scheduling and Path Control of Autonomous Mobile Robot Based on On-Device Computing

Abstract

The present invention relates to a method for scheduling and controlling a path of an autonomous driving robot using on-device AI and sensor fusion technology, comprising: (a) collecting indoor and outdoor environment data using various sensors; (b) identifying drivable areas and obstacles based on the collected data; (c) dynamically designing and modifying a path in real time based on the identified information; and (d) moving the autonomous driving robot by following the designed path.

Description

Technology Field

[0001] The present invention relates to scheduling and path control technology for a robot capable of autonomous driving in indoor and outdoor environments, and more specifically to a method and system for ensuring the efficient execution of tasks and safety of the robot. The present invention can be applied to various fields such as logistics and services, with a particular focus on applications in service sectors such as logistics warehouses, hospitals, and airports. Background Technology

[0003] Recently, the use of autonomous robots has been rapidly increasing across various industrial sectors, including logistics, services, and manufacturing. Autonomous robots can perceive their environment, plan routes, and travel to target destinations to perform tasks independently, without human intervention. Robot scheduling and path control technologies are essential for the efficient operation of these autonomous robots.

[0004] Conventional autonomous driving robot scheduling and path control technologies have primarily relied on a central server to plan and control the robot's path. This centralized control method has the following problems.

[0005] First, the real-time responsiveness of robots may be degraded due to central server overload and communication delays. In situations requiring an immediate response to environmental changes, communication delays can cause serious problems.

[0006] Second, the robot's operation may stop in the event of a communication failure. Due to its high dependence on communication with a central server, autonomous operation of the robot is impossible in the event of a communication failure.

[0007] Third, it has vulnerabilities in terms of personal information protection and security. Since all data is transmitted to external servers for processing, there is a risk of data leakage.

[0008] Fourth, safety may be compromised due to a lack of responsiveness to environmental changes. Real-time path modification in dynamic environments is difficult due to processing delays on the central server.

[0009] Therefore, the development of new autonomous driving robot control technology capable of solving these problems is required. Prior art literature

[0011] Korean Registered Patent No. 10-2542218, Korean Registered Patent No. 10-2096875, Korean Registered Patent No. 10-2112162, Korean Registered Patent No. 10-1695557, Korean Published Patent No. 10-2019-0104010 The problem to be solved

[0012] This invention focuses on improving operational efficiency by analyzing real-time status and location data of an autonomous driving robot to generate an optimal path, and by performing obstacle avoidance and dynamic path modification. Furthermore, it is designed to adapt to various work environments by including user customization and inter-robot collaboration functions. This invention can be effectively utilized in diverse indoor and outdoor environments, such as logistics warehouses, hospitals, and airports.

[0013] The present invention was devised to solve the above-mentioned problems and aims to improve the real-time responsiveness, safety, and security of a robot by providing an on-device-based autonomous driving robot scheduling and path control method.

[0014] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0016] To achieve the above objective, the present invention provides a method for a robot to analyze its surrounding environment and autonomously design and follow a path by utilizing on-device AI and sensor fusion technology.

[0017] The autonomous driving robot scheduling and path control method of the present invention collects indoor and outdoor environmental data using various sensors, identifies drivable areas and obstacles based on this data, and dynamically designs and modifies the path in real time. In addition, it enables adaptation to various environments and working conditions through a modular design.

[0018] The sensor fusion of the present invention integrates multiple sensor data to generate precise environmental information.

[0019] The present invention identifies stationary and moving obstacles in real time and generates an avoidance path. The wheel-based mobile robot used in the present invention can exhibit various movements depending on the attachment form, type, and number of wheels. The robot moves by receiving linear velocity and angular velocity inputs and converting a movable arc-shaped path into a straight path. Furthermore, obstacles can be effectively avoided by driving while holding the center point of an initial guide circle relative to the mobile robot.

[0020] The present invention generates an optimized path tailored to a real-time changing environment.

[0021] The present invention is designed as a modular unit that allows the computational unit, driving unit, and power supply unit to be independently replaced and upgraded, enabling customized combinations according to user requirements.

[0022] The present invention processes data within the device using an on-device method, thereby reducing dependence on external communication and enhancing security.

[0023] The present invention can be implemented in a network-independent form that enables stable autonomous driving and continued operation even when the network connection is disconnected.

[0025] The present invention enables obstacle avoidance and precise path following through the combination of a high-precision sensor and a software algorithm. Effects of the invention

[0027] The on-device autonomous driving robot scheduling and path control method according to the present invention has the following effects.

[0028] The present invention can support stable autonomous driving and task performance even in complex environments by improving the real-time responsiveness of a robot through on-device processing.

[0029] The present invention can enhance data security by minimizing dependency on external communication through on-device AI and ensure stable robot operation even in the event of communication failures.

[0030] The present invention can ensure safety by improving obstacle avoidance capabilities through high-precision sensors and sensor fusion technology.

[0031] Since the present invention can operate without relying on a communication network, it can ensure stable operation even in environments where network connectivity is unstable.

[0032] The present invention allows for the configuration of robots to meet the requirements of various industrial fields through a modular design. Brief explanation of the drawing

[0034] FIG. 1 is a block diagram of an on-device computing-based autonomous driving robot task scheduling and path control system according to a preferred embodiment of the present invention. FIG. 2 is a flowchart of an on-device computing-based autonomous driving robot task scheduling and path control method according to one embodiment. FIG. 3 is a block diagram illustrating a sensor fusion method according to one embodiment. FIG. 4 is a flowchart illustrating an obstacle avoidance process according to one embodiment. FIG. 5 is an example diagram illustrating the operation method of an optimization algorithm according to one embodiment. FIG. 6 is a block diagram showing the configuration of an on-device computing-based autonomous driving robot module according to one embodiment. FIG. 7 is an exemplary diagram illustrating an on-device computing-based autonomous driving robot task scheduling and path control method according to one embodiment. Specific details for implementing the invention

[0035] The advantages and features of the present invention and the methods for achieving them will become clear from the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. "And / or" includes each of the mentioned items and all combinations of one or more.

[0036] Although terms such as "first," "second," etc. are used to describe various elements, components, and / or sections, it goes without saying that these elements, components, and / or sections are not limited by these terms. These terms are used merely to distinguish one element, component, or section from another. Accordingly, it goes without saying that the first element, first component, or first section mentioned below may be a second element, second component, or second section within the technical scope of the present invention.

[0037] Furthermore, the identifiers (e.g., a, b, c, etc.) for each step are used for convenience of explanation and do not describe the order of the steps; the steps may occur differently from the specified order unless a specific order is clearly indicated in the context. That is, the steps may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

[0038] The terms used herein are for describing the embodiments and are not intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, “comprises” and / or “comprising” do not exclude the presence or addition of one or more other components, steps, actions, and / or elements to the mentioned components, steps, actions, and / or elements.

[0039] Unless otherwise defined, all terms used in this specification (including technical and scientific terms) may be used in a meaning that is commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0040] Furthermore, in describing the embodiments of the present invention, if it is determined that a detailed description of known functions or configurations could unnecessarily obscure the essence of the invention, such detailed description will be omitted. Additionally, the terms described below are defined considering the functions in the embodiments of the present invention, and these may vary depending on the intentions or conventions of the user or operator. Therefore, such definitions should be based on the content throughout this specification.

[0042] FIG. 1 is a block diagram of an on-device computing-based autonomous driving robot task scheduling and path control system according to a preferred embodiment of the present invention.

[0043] Referring to FIG. 1, an on-device computing-based autonomous driving robot task scheduling and path control system may include a computation unit (110), a sensor unit (120), a communication unit (130), a driving unit (140), and a power supply unit (150).

[0044] The computing unit (110) is a device that executes scheduling and path control algorithms for autonomous driving of the robot. Based on on-device AI technology, it processes sensor data in real time and dynamically plans and modifies the path according to environmental changes. It also performs collaboration with other robots and user-defined functions. For example, the computing unit can divide tasks through communication between robots or adjust work schedules by assigning priority to specific robots. Additionally, the user can set the robot's work area, movement speed, avoidance method, etc., through a smartphone application or voice command.

[0046] The sensor unit (120) includes various sensors that collect information about the robot's surrounding environment. For example, LiDAR, cameras, ultrasonic sensors, etc. may be used. The LiDAR sensor generates a three-dimensional map of the surrounding environment, and the camera analyzes image information to identify obstacles and drivable areas. The ultrasonic sensor is used to detect obstacles at close range. Referring to FIG. 3, data collected from various sensors can be integrated through sensor fusion technology to generate accurate and reliable environmental information.

[0047] The communication unit (130) performs the function of communication between robots or communication with a user. For example, it can exchange information with other robots using communication technologies such as Wi-Fi, Bluetooth, 5G, or convey the status of the robot and the progress of the work to the user.

[0048] The drive unit (140) includes a motor, wheels, etc. for the movement of the robot. It moves the robot accurately and safely according to the path information generated by the computation unit.

[0049] The power supply unit (150) supplies power to the robot. Various power supply devices, such as batteries and fuel cells, can be used.

[0050] FIG. 2 is a flowchart of an on-device computing-based autonomous driving robot task scheduling and path control method according to one embodiment.

[0051] Referring to FIG. 2, data of the robot's surrounding environment is collected through the sensor unit (120).

[0052] The computational unit analyzes collected sensor data to extract information such as drivable areas, obstacles, and target points. By utilizing machine learning techniques, it can extract features from the sensor data and improve adaptability to environmental changes.

[0053] Referring to Fig. 5, the optimal path to the target point is planned based on the extracted information. The path is dynamically generated and modified by taking into account the environment that changes in real time.

[0054] The robot can be moved along a planned path. Referring to Fig. 4, if an obstacle is detected while driving, the path is modified in real time to avoid the obstacle.

[0055] The operations performed through each configuration of the on-device computing-based autonomous driving robot task scheduling and path control system illustrated in FIG. 2 will be described in detail below with reference to FIG. 3 to 7. Although each step described with reference to FIG. 3 is described as being performed by different configurations, it is not limited thereto, and depending on the embodiment, at least some of the steps may be performed in the same or different configurations.

[0056] FIG. 3 is a block diagram illustrating a sensor fusion method according to one embodiment.

[0057] The on-device autonomous driving robot of the present invention can perform various tasks in indoor and outdoor environments. For example, it can perform tasks such as transporting goods in a logistics warehouse, delivering medicine to patients in a hospital, or transporting passengers' luggage at an airport.

[0058] Meanwhile, the steps of the method or algorithm described in connection with the embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present invention belongs.

[0059] The components of the present invention may be implemented as a program (or application) and stored on a medium to be executed in combination with a computer, which is hardware. The components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as combinations of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors.

[0061] Although preferred embodiments of the method and system for task scheduling and path control of an autonomous driving robot based on on-device computing according to the present invention have been described above, the present invention is not limited thereto and can be implemented with various modifications within the scope of the claims, the detailed description of the invention, and the attached drawings, and such modifications are also included in the present invention. Explanation of the symbols

[0063] 100: Autonomous robot 110: Operation unit 111: Processor 112: Memory 120: Sensor section 121: LiDAR sensor 122: Camera 123: Ultrasonic sensor 130: Communications Department 131: Wireless communication module 140: Drive unit 141: Motor 142: Wheel 150: Power supply 151: Battery 200: Path Planning System 210: Input section 220: Path generation section 230: Path Evaluation Unit 240: Output section 300: On-device AI System 310: AI Model 320: Training data 330: Execution Environment 340: Output Information

Claims

Claim 1 A method for scheduling and controlling a path of an autonomous driving robot using on-device AI and sensor fusion technology, comprising: (a) collecting indoor and outdoor environment data using various sensors; (b) identifying drivable areas and obstacles based on the collected data; (c) dynamically designing and modifying a path in real time based on the identified information; (d) moving the autonomous driving robot by following the designed path; and an on-device computing-based method for scheduling and controlling a path of an autonomous driving robot. Claim 2 A method for on-device computing-based autonomous driving robot task scheduling and path control, wherein, in claim 1, step (a) collects environmental data regarding fixed obstacles and moving obstacles, etc. using multi-sensor data such as LiDAR, camera, and ultrasonic sensor. Claim 3 In paragraph 2, the above step (b) is characterized by integrating multiple sensor data through sensor fusion to generate precise environmental information, in an on-device computing-based autonomous driving robot task scheduling and path control method. Claim 4 A method for on-device computing-based autonomous driving robot task scheduling and path control, wherein, in claim 1, the above step (c) optimizes the path according to the characteristics of the indoor and outdoor environment. Claim 5 A method for on-device computing-based autonomous driving robot task scheduling and path control, characterized in that, in claim 1, the autonomous driving robot provides a structure that allows the computational unit, driving unit, and power supply unit of the robot to be independently replaced or upgraded through a modular design. Claim 6 A method for on-device computing-based autonomous driving robot task scheduling and path control according to claim 1, characterized in that the autonomous driving robot operates independently with on-device AI technology that reduces dependence on external communication, and can enhance security and minimize the risk of personal information leakage by processing data within the device. Claim 7 A method for on-device computing-based autonomous driving robot task scheduling and path control, characterized in that, in claim 1, the autonomous driving robot can perform not only obstacle avoidance but also precise path following through a software algorithm combined with a high-precision sensor. Claim 8 A method for on-device computing-based autonomous driving robot task scheduling and path control, characterized in that, in claim 1, the autonomous driving robot can recognize and operate user commands through a voice control function and can be operated remotely through a smartphone application. Claim 9 The method for on-device computing-based autonomous driving robot task scheduling and path control according to claim 1, characterized in that the autonomous driving robot responds robustly to changes in indoor and outdoor environments and is manufactured with highly durable materials and design, enabling stable operation even under extreme weather conditions. Claim 10 A computer program stored on a computer-readable medium, characterized in that when an instruction of the computer program is executed, a method according to any one of claims 1 to 9 is performed.

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