High-precision multi-sensor integrated system of mobile robot

Abstract

The utility model relates to a high-precision multi-sensor integrated system of a mobile robot, which comprises a three-level framework consisting of sensor access units, a high-speed network communication layer and a computing control host, the plurality of sensor access units are accessed to the computing control host through the high-speed network communication layer, the sensor access units are pluggable peripheral modules, and the peripheral modules are connected with the computing control host through the high-speed network communication layer. The computing control host is a host module, and the peripheral module is connected with the host module through a network interface; the peripheral module is integrated with universal sensor interfaces, and each interface is provided with a protocol conversion chip; the peripheral module is connected with the operation control host through an Ethernet switch and a CAN FD bus, a hardware queue manager is deployed on the high-speed network communication layer, transmission priorities are distributed according to sensor data types, and control signals are ensured to be transmitted preferentially. According to the utility model, through the design of an extensible hardware module, a high-speed network communication architecture and an open software interface, plug-and-play and flexible extension of sensor peripherals are realized, and the system compatibility is remarkably improved.

Description

Technical Field

[0001] This utility model relates to the field of mobile robot technology, and in particular to a high-precision multi-sensor integrated system for mobile robots, used to realize high-precision environmental perception, positioning and navigation, and motion control of mobile robots. Background Technology

[0002] Traditional mobile robots typically integrate sensors using a distributed, independent connectivity architecture. This means that each sensor (such as LiDAR, vision camera, inertial measurement unit, etc.) communicates directly with the central processing unit via an independent hardware interface (USB, CAN, Ethernet, etc.), and the power supply and signal processing units are also independently configured. This architecture has several technical drawbacks in practical applications:

[0003] (1) At the hardware level, the number of cables increases significantly with the increase of sensors, which not only occupies internal space, but also leads to a decrease in hardware reliability due to problems such as poor contact and electromagnetic interference. The sensor interface protocols of different manufacturers are significantly different. When adding a new sensor, the hardware interface needs to be redesigned and a dedicated driver needs to be developed, which leads to increased R&D costs and extended cycle.

[0004] (2) In terms of data processing and real-time performance, multi-source data is asynchronously transmitted to the central processing unit through independent interfaces, resulting in accumulated processing delays and time synchronization errors, which affect the accuracy of data fusion and the real-time performance of control commands. The distributed power supply design also leads to inconsistent voltage fluctuations in each sensor module, which may cause abnormal sensor output under complex operating conditions, further aggravating data fusion errors.

[0005] (3) In terms of system integration, the distributed architecture lacks a unified hardware modular design. The functional coupling between sensors and processing units is high. When upgrading or replacing sensors, related modules need to be adjusted simultaneously, resulting in increased maintenance costs and low efficiency. The inherent defects of this type of architecture make it difficult to meet the needs of high-precision mobile robots for multi-sensor collaborative perception, real-time data processing and rapid functional iteration when the number of sensors increases. Specifically, it manifests as complex system wiring, poor scalability, insufficient real-time performance and high maintenance difficulty.

[0006] It is evident that traditional distributed sensor integration solutions, due to inherent defects such as dispersed hardware connections, heterogeneous interfaces, and functional coupling, inevitably lead to complex system wiring, poor scalability, insufficient real-time performance, and high maintenance costs as the number of sensors increases. This makes it difficult to meet the needs of high-precision mobile robots for multi-sensor collaborative perception, real-time data processing, and rapid functional iteration.

[0007] Currently, existing mobile robot sensor integration systems suffer from the following core deficiencies in terms of hardware architecture, system communication, modular design, and data processing:

[0008] (1) Processing efficiency and real-time performance issues of centralized computing architecture;

[0009] Existing solutions generally rely on general-purpose CPUs / GPUs as central computing units, centrally processing logic-intensive tasks (such as path planning) and data-intensive tasks (such as point cloud processing and image recognition), leading to an imbalance in resource allocation. On the one hand, different types of tasks compete for processor resources, causing task scheduling delays. For example, when path planning algorithms and vision processing run simultaneously, excessive processor load may cause delays in control command output. On the other hand, the sharing of the same processor between the underlying actuator control (such as motor PID regulation) and the upper-level perception tasks makes control signals with high real-time requirements susceptible to interference from upper-level computing tasks, resulting in a decrease in motion control accuracy. In addition, high-performance processors consume a lot of power when processing multi-sensor data, posing a severe challenge to the endurance and heat dissipation design of mobile robots.

[0010] (2) System integration challenges caused by heterogeneous multi-sensor communication protocols;

[0011] Various sensors (such as LiDAR, cameras, and IMUs) use different communication protocols (Ethernet, GigEVision, SPI, etc.), resulting in poor data interface compatibility. During development, separate conversion modules or drivers need to be designed for different protocols, increasing system complexity and development costs. The asynchronous nature of data acquisition makes time synchronization difficult, requiring calibration using software algorithms (such as timestamp interpolation), which may introduce position or attitude estimation errors. The massive amounts of data generated by high-resolution sensors place higher demands on communication bandwidth, and traditional buses (such as CAN) are unable to meet the real-time transmission requirements, easily causing data packet loss or delays, affecting the collaborative sensing effect of multiple sensors.

[0012] (3) The lack of standardization in modular design leads to insufficient scalability;

[0013] Existing sensor modules generally suffer from inconsistent interfaces and high functional coupling. Sensors from different manufacturers use proprietary interfaces or protocols, resulting in poor hardware and software compatibility. Adding or replacing sensors requires redesigning the hardware interface and adjusting the underlying drivers, sometimes even involving the replacement of the entire control module, leading to high system upgrade costs and long cycles. Some integrated modules rigidly bind the sensor and processing unit (such as integrating LiDAR with a dedicated CPU), making it impossible to upgrade components individually and limiting system flexibility. High-density integrated modules are difficult to heat dissipate, and excessively high operating temperatures may lead to decreased sensor accuracy or hardware failure, affecting long-term stability. Utility Model Content

[0014] The purpose of this utility model is to overcome the above-mentioned shortcomings and provide a high-precision multi-sensor integrated system for mobile robots. This system overcomes the problems in the prior art, such as the difficulty of expansion caused by heterogeneous sensor interfaces, the inefficiency of resource scheduling caused by centralized computing architecture, the hardware and software coupling caused by insufficient modular design, and the limitation of system adaptability due to lack of software openness.

[0015] The purpose of this utility model is achieved as follows:

[0016] A high-precision multi-sensor integrated system for a mobile robot includes a three-level architecture consisting of a sensor access unit, a high-speed network communication layer, and a computing and control host. Multiple sensor access units are connected to the computing and control host through the high-speed network communication layer. The sensor access unit is a pluggable peripheral module, and the computing and control host is a host module. The peripheral modules are connected to the host module through a network interface.

[0017] The peripheral module integrates a universal sensor interface, and each interface is equipped with a protocol conversion chip, which can convert the raw signals of the sensor into a unified high-speed network data format in real time. The peripheral module adopts a DIN rail plug-in interface for connection, and the sensor interfaces with the host module through an aviation plug connector. The peripheral module and the computing and control host are connected through a gigabit Ethernet switch + CAN FD bus to form a distributed network architecture.

[0018] The high-speed network communication layer is equipped with a hardware queue manager that allocates transmission priorities according to sensor data types to ensure that control signals are transmitted first.

[0019] The computing control host connects to the network via a PCIe gigabit network card or a CAN FD controller, and has a built-in hardware abstraction layer driver.

[0020] Furthermore, the peripheral module is equipped with an independent power management chip, supports a wide voltage input of 9-36V, and has a built-in EMI filter to suppress power supply noise and ensure stable power supply to the sensor.

[0021] Furthermore, the Ethernet between the peripheral module and the computing control host adopts the IEEE 1588 precise clock synchronization protocol to ensure that the timestamp error of multi-sensor data acquisition is <1μs; the CAN FD bus supports a data rate of up to 5Mbps to meet the requirements of low-latency control signal transmission.

[0022] Furthermore, the hardware queue manager of the high-speed network communication layer adopts a CPLD logic unit.

[0023] Furthermore, the computing control host uses Jetson as the main processor, which can perform data-intensive tasks such as visual deep learning and point cloud computing, and realize computing power preprocessing.

[0024] Furthermore, it also includes a three-layer protocol conversion architecture: Device layer: adapts to sensor private protocols and realizes signal parsing through the protocol conversion chip of the peripheral module; Network layer: encapsulates the parsed data into a unified format and transmits it through Ethernet / IP or CAN FD protocols; Application layer: the computing host reads standardized data in real time through the protocol stack API and supports dynamic loading of sensor description files to realize device discovery and parameter configuration.

[0025] Furthermore, the peripheral module has built-in lightweight firmware to achieve sensor initialization and data preprocessing.

[0026] Furthermore, the protocol stack of the computing control host provides standardized APIs, shielding the differences in underlying hardware.

[0027] Furthermore, the computing control host is pre-installed with a dual-system bootloader, which can switch between ROS for algorithm development and RTOS, and realize data interaction through a shared memory mechanism; it provides a general software library interface, compatible with mainstream robot algorithms.

[0028] Furthermore, the peripheral module has a built-in hardware watchdog that automatically restarts when a sensor malfunction is detected; the computing control host protocol stack supports hot-swapping of sensors.

[0029] Compared with the prior art, the beneficial effects of this utility model are:

[0030] This invention provides a high-precision multi-sensor integrated system for mobile robots. Through scalable hardware module design, high-speed network communication architecture, and open software interfaces, it achieves plug-and-play and flexible expansion of sensor peripherals, significantly improving system compatibility. Adding or replacing sensors does not require complex hardware adaptation and driver development. High-speed network transmission and priority scheduling mechanisms ensure high-precision time synchronization and efficient transmission of multi-sensor data, effectively improving data interaction efficiency and real-time control accuracy. Thorough modular decoupling of hardware and software and support for multiple operating systems provide developers with an open and independent software ecosystem, greatly shortening the algorithm adaptation cycle. Distributed power supply, hot-swapping, and fault self-healing design ensure that the failure of a single sensor does not affect system operation, significantly enhancing hardware reliability. Ultimately, it achieves high scalability, high compatibility, strong real-time performance, and stable and reliable operation of the multi-sensor integrated system for mobile robots. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the cascaded logic of this utility model.

[0032] Figure 2 This is a schematic diagram of the software architecture of this utility model.

[0033] Figure 3 This is a schematic diagram of the hardware architecture of this utility model. Detailed Implementation

[0034] To better understand the technical solution of this utility model, a detailed description will be provided below in conjunction with relevant illustrations. It should be understood that the specific embodiments described below are not intended to limit the specific implementation of the technical solution of this utility model, but are merely possible implementations of the technical solution of this utility model. It should be noted that the descriptions of the positional relationships of the components herein, such as component A being located above component B, are based on the relative positions of the components in the illustrations and are not intended to limit the actual positional relationships of the components.

[0035] See Figure 1-3 , Figure 1 A schematic diagram of the high-precision multi-sensor integrated system for mobile robots in Embodiment 1 is shown. As shown in the figure, the high-precision multi-sensor integrated system for mobile robots involved in Embodiment 1 includes a three-level architecture consisting of sensor access units, a high-speed network communication layer, and a computing control host. Multiple sensor access units are connected to the computing control host through the high-speed network communication layer. The sensor access units are pluggable peripheral modules, and the computing control host is a host module. The peripheral modules are connected to the host module through standardized interfaces.

[0036] 1. Sensor access unit (peripheral module):

[0037] Multi-interface compatible design: The peripheral module integrates universal sensor interfaces (USB 3.0, GigE Vision, RS485, SPI, etc.), and each interface is equipped with a protocol conversion chip (such as CPLD or dedicated ASIC), which can convert the raw signals of sensors such as LiDAR, camera, and IMU into a unified high-speed network data format in real time (such as the Ethernet-based IEEE1588 time synchronization protocol or a custom binary protocol).

[0038] Plug-and-play modular design: The peripheral modules are connected via a rail-mounted plug-in interface, such as an industrial-grade DIN rail, which supports hot-swapping. Sensors are connected to the modules via aviation connectors. The physical interfaces comply with the IP67 protection standard and are suitable for complex environments such as dust and humidity.

[0039] Distributed power supply and filtering: Each peripheral module is equipped with an independent power management chip (PMIC), which supports a wide voltage input of 9-36V and has a built-in EMI filter to suppress power supply noise and ensure stable power supply to the sensor.

[0040] 2. High-speed network communication layer:

[0041] The peripheral modules are connected to the computing and control host via a gigabit Ethernet switch and a CAN FD bus, forming a distributed network architecture.

[0042] The Ethernet uses the IEEE 1588 precision clock synchronization protocol to ensure that the timestamp error of multi-sensor data acquisition is <1μs; the CAN FD bus supports data rates up to 5Mbps to meet the requirements of low-latency control signal transmission.

[0043] Data buffering and priority scheduling: The high-speed network communication layer is equipped with a hardware queue manager (such as a CPLD logic unit) that allocates transmission priorities according to the sensor data type (such as real-time control, sensing, and log types) to ensure that control signals (such as motor commands) are transmitted first, while slightly sacrificing the latency of non-real-time data (such as sensor configuration parameters).

[0044] 3. Computation and control host (host module, also known as the core processing unit):

[0045] Standardized communication interface: The computing control host uses Jetson as the main processor, which can perform data-intensive tasks such as visual deep learning and point cloud computing, and realize computing power preprocessing.

[0046] The computing control host connects to the network via a PCIe gigabit network card or a CAN FD controller. It has a built-in hardware abstraction layer (HAL) driver, so there is no need to develop a dedicated driver for specific sensors. Adaptation can be completed simply by loading the corresponding protocol configuration file.

[0047] This embodiment 1 relates to a high-precision multi-sensor integrated system for a mobile robot, which includes a protocol stack and a modular decoupling mechanism.

[0048] (1) Design of a unified data protocol stack

[0049] The multi-sensor integrated system in this embodiment adopts a three-layer protocol conversion architecture:

[0050] Device layer: Adapts to sensor proprietary protocols (such as Velodyne LiDAR's UDP protocol and Basler camera's GigEVision protocol), and realizes signal parsing through the protocol conversion chip of the peripheral module;

[0051] Network layer: Encapsulates the parsed data into a uniform format (such as binary frames containing timestamps, sensor IDs, and data types) and transmits it via Ethernet / IP or CAN FD protocols;

[0052] Application layer: The computing host reads standardized data in real time through protocol stack APIs (such as ROS's Message interface) and supports dynamic loading of sensor description files (.urdf) ​​to realize device discovery and parameter configuration.

[0053] (2) Modular decoupling of hardware and software

[0054] Modular hardware: Peripheral modules are connected to the host via a network interface. The physical location and interface type of the sensor do not affect the host's computing logic. For example, when adding a millimeter-wave radar, only the corresponding peripheral module needs to be connected, without modifying the host hardware design.

[0055] (3) Software layered architecture

[0056] Driver layer: The peripheral module has built-in lightweight firmware (such as FreeRTOS) to implement sensor initialization and data preprocessing;

[0057] Middleware layer: The computing control host protocol stack provides standardized APIs (such as C++ / Python interfaces) to shield the differences in underlying hardware;

[0058] Application layer: Supports ROS, RTOS (such as VxWorks) and custom algorithm frameworks, and achieves isolation and coexistence of different software environments through Docker container technology.

[0059] This embodiment 1 relates to a high-precision multi-sensor integrated system for a mobile robot, with a standardized software interface design.

[0060] Multi-operating system support: The computing and control host is pre-installed with a dual-system bootloader, which can switch between ROS (for algorithm development) and RTOS (for hard real-time control) and realize data interaction through a shared memory mechanism; it provides general software library interfaces (such as C++ SDK, Python API) and is compatible with mainstream robot algorithms (such as A* path planning, EKF fusion algorithm).

[0061] Developer toolchain: It provides a sensor configuration tool (graphical interface), a protocol debugging assistant (packet capture and analysis) and a performance monitoring platform (real-time display of network load and sensor status) to reduce the threshold for secondary development.

[0062] This embodiment 1 relates to a high-precision multi-sensor integrated system for a mobile robot, which features performance optimization and fault tolerance mechanisms.

[0063] Real-time balancing strategy: Hard real-time scheduling (cycle ≤100μs) is adopted for control data (such as IMU attitude and encoder signals), and asynchronous transmission is adopted for non-real-time data (such as panoramic images and log files). The QoS (Quality of Service) mechanism ensures that critical data is processed first. The measured end-to-end latency is reduced by 40% compared with the traditional solution.

[0064] Fault self-healing design: The peripheral module has a built-in hardware watchdog that automatically restarts when a sensor malfunction is detected; the host protocol stack supports hot sensor replacement (e.g., when a camera malfunctions, the system automatically switches to the backup camera and updates the data routing table).

[0065] Working principle:

[0066] This invention provides a high-precision multi-sensor integrated system for mobile robots, employing a modular design and comprising a sensor access unit, a high-speed network communication layer, and a computing and control host. The sensor access unit has multiple universal interfaces and a built-in protocol conversion unit, capable of converting the proprietary protocols of different sensors into a unified network data format. It supports hot-swapping via a rail-type plug-in structure, enabling plug-and-play sensor functionality. The high-speed network communication layer connects all components via a high-speed local area network, utilizing a clock synchronization mechanism to ensure time synchronization of multi-sensor data and employing a priority scheduling strategy to guarantee low-latency transmission of critical data. The computing and control host integrates a high-performance processor, running a robot operating system and a real-time operating system, and automatically identifies and configures the connected sensors through a hardware abstraction layer.

[0067] The software employs a three-layer protocol conversion architecture: the device layer parses sensor protocols, the network layer encapsulates data, and the application layer provides standardized API interfaces. The software is layered and decoupled; the driver layer handles sensor initialization, the middleware layer implements device discovery, and the application layer supports multiple operating systems and mainstream algorithms. The system also provides open software interfaces and toolchains for easy developer use, and adopts a modular reliability design, including distributed power supply, a hardware watchdog mechanism, and sensor hot-swap functionality, to ensure stable system operation.

[0068] The entire system of this utility model solves the problems of sensor expansion, protocol compatibility and hardware-software coupling in the prior art through hardware modularization and software layered architecture, realizing flexible configuration and efficient collaboration of multi-sensor systems.

[0069] This invention achieves plug-and-play functionality and protocol standardization for sensor peripherals by designing scalable hardware modules and a high-speed network transmission architecture. It completely modularizes computing power and peripheral interfaces to decouple hardware and software functions. While ensuring stable overall performance, it provides open interfaces compatible with Robot Operating System (ROS), Real-Time Operating System (RTOS), and various software libraries. This solves technical problems such as poor scalability, difficulty in heterogeneous protocol compatibility, high dependence on software and hardware iteration, and low efficiency of real-time data interaction when integrating multiple sensors. It meets the needs of mobile robots in complex scenarios for flexible sensor configuration, rapid function iteration, and multi-platform software compatibility.

[0070] In summary, this utility model has the following core advantages:

[0071] Enhanced scalability: Through standardized network interfaces and protocol stacks, the adaptation time for new sensors is reduced from weeks in traditional solutions to hours, and hardware redesign costs are reduced by 70%.

[0072] Enhanced heterogeneous compatibility: Supports simultaneous access to more than 20 types of sensors (such as 128-line LiDAR, 8K camera, UWB positioning module), with protocol conversion latency <5μs, solving the interface compatibility problem of traditional solutions;

[0073] Software openness: It provides support for both ROS and RTOS platforms, so algorithm developers do not need to worry about hardware details and can call sensor data through API;

[0074] Modular reliability: Distributed power supply and hot-swappable design ensure that the failure of a single sensor does not affect the operation of the system.

[0075] The above are merely specific application examples of this utility model and do not constitute any limitation on the scope of protection of this utility model. All technical solutions formed by equivalent transformations or equivalent substitutions fall within the scope of protection of this utility model.

Claims

1. A high-precision multi-sensor integrated system for mobile robots, characterized in that: The system comprises a three-tier architecture consisting of sensor access units, a high-speed network communication layer, and a computing and control host. Multiple sensor access units are connected to the computing and control host through the high-speed network communication layer. The sensor access units are pluggable peripheral modules, and the computing and control host is a host module. The peripheral modules are connected to the host module through a network interface. The peripheral module integrates a universal sensor interface, and each interface is equipped with a protocol conversion chip, which can convert the raw signals of the sensor into a unified high-speed network data format in real time. The peripheral module adopts a DIN rail plug-in interface for connection, and the sensor interfaces with the host module through an aviation plug connector. The peripheral module and the computing and control host are connected through a gigabit Ethernet switch and a CAN FD bus to form a distributed network architecture. The high-speed network communication layer is equipped with a hardware queue manager that allocates transmission priorities according to sensor data types to ensure that control signals are transmitted first. The computing control host connects to the network via a PCIe gigabit network card or a CAN FD controller, and has a built-in hardware abstraction layer driver.

2. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The peripheral module is equipped with an independent power management chip, supports a wide voltage input of 9-36V, and has a built-in EMI filter to suppress power supply noise and ensure stable power supply to the sensor.

3. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The Ethernet between the peripheral module and the computing control host adopts the IEEE 1588 precise clock synchronization protocol to ensure that the timestamp error of multi-sensor data acquisition is <1μs; the CAN FD bus supports a data rate of up to 5Mbps to meet the requirements of low-latency control signal transmission.

4. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The hardware queue manager of the high-speed network communication layer uses CPLD logic units.

5. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The computing control host uses Jetson as the main processor, which can perform data-intensive tasks such as visual deep learning and point cloud computing, and realize computing power preprocessing.

6. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: It also includes a three-layer protocol conversion architecture: device layer: adapts to sensor proprietary protocols and realizes signal parsing through the protocol conversion chip of the peripheral module; network layer: encapsulates the parsed data into a unified format and transmits it through Ethernet / IP or CAN FD protocols; Application Layer: The computing host reads standardized data in real time through the protocol stack API, and supports dynamic loading of sensor description files to realize device discovery and parameter configuration.

7. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The peripheral module has built-in lightweight firmware to realize sensor initialization and data preprocessing.

8. The high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The protocol stack of the computing control host provides standardized APIs, shielding the differences in underlying hardware.

9. A high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The computing and control host is pre-installed with a dual-system bootloader, which can switch between ROS for algorithm development and RTOS, and realize data interaction through a shared memory mechanism; it provides a general software library interface, which is compatible with mainstream robot algorithms.

10. A high-precision multi-sensor integrated system for a mobile robot according to claim 1, characterized in that: The peripheral module has a built-in hardware watchdog that automatically restarts when a sensor malfunction is detected; the computing control host protocol stack supports hot-swapping of sensors.

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