Multi-radar fusion algorithm-based multi-machine collaborative modular carrying method

Abstract

The invention discloses a multi-machine cooperative modular carrying method based on a multi-radar fusion algorithm, and the method comprises the steps: respectively installing a plurality of radar sensors on different side surfaces of carrying equipment, and obtaining the original data collected by all radar sensors; the original data comprises the surrounding environment of each piece of carrying equipment and the position change of adjacent carrying equipment, and then processing the original data by adopting a multi-radar fusion algorithm to obtain multi-radar fusion data; the multi-radar fusion algorithm adopts a mode of combining data layer fusion, feature layer fusion and decision-making layer fusion to process original data, and controls the motion state of each piece of carrying equipment according to a fusion result and an instruction output by a decision-making layer. The positioning precision and the cooperative control capability of the carrying system are effectively improved, the system is suitable for carrying large equipment in a complex working environment, and the carrying efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of handling equipment, and in particular to a multi-machine collaborative modular handling method based on a multi-radar fusion algorithm. Background Art

[0002] In recent years, with the improvement of industrial automation and the growing demand for intelligent manufacturing, the technology in the field of large equipment handling has also made significant progress. As an advanced technology that can cope with complex handling scenarios, multi-machine collaborative handling systems have been widely used in high-precision handling tasks such as aerospace, shipbuilding, and large-scale machinery assembly. Through the collaborative cooperation of multiple handling equipment, this type of system can achieve efficient and safe handling of oversized and overweight equipment. However, despite certain achievements in technological development, the existing system still has many shortcomings in equipment positioning, environmental perception, and collaborative control, such as:

[0003] 1. Shortcomings of positioning technology: At present, multi-machine collaborative handling systems often use technologies such as laser radar, ultra-wideband (UWB), visual recognition, and inertial navigation for equipment positioning. These technologies can achieve high positioning accuracy in a single scenario, but are easily interfered with in complex environments.

[0004] 2. Disadvantages of collaborative control: The multi-machine collaborative handling system requires efficient and accurate collaboration between multiple devices to ensure the safety and stability of the handling task. However, the current collaborative control technology has deficiencies in real-time and adaptability. On the one hand, in a multi-machine system, real-time communication is required between devices to share information such as position, speed, and posture, but the delay and bandwidth limitations of existing communication technologies may lead to untimely or inaccurate information transmission, thus affecting collaborative efficiency. On the other hand, since the equipment in the handling task may face challenges such as nonlinear dynamics, multiple interference sources, and complex path planning, traditional collaborative control algorithms are difficult to cope with multi-dimensional dynamic changes and task allocation requirements, and are prone to problems such as uncoordinated motion and unbalanced torque between devices, which increases the instability of the system and handling risks.

[0005] 3. Insufficient environmental perception: Handling tasks usually require equipment to fully perceive the environment in order to avoid obstacles and ensure the rationality of path planning. However, the current environmental perception system still has shortcomings in multi-sensor information fusion and efficient algorithms. The accuracy and real-time performance of multi-sensor fusion directly affect the system's ability to perceive the surrounding environment, and traditional fusion methods often find it difficult to balance computing costs and information integrity in complex scenarios. In addition, in dynamic scenarios, there is also much room for improvement in the system's response speed and perception accuracy to rapidly changing environments.

[0006] 4. Standardization and reliability issues: The multi-machine collaborative system has a low degree of standardization at the hardware and software levels, and it is difficult to ensure the interface compatibility and communication protocol consistency between devices, which makes the system complex to build and expand. In addition, when the equipment is running for a long time, problems such as sensor error accumulation and network communication interference may reduce the reliability and accuracy of the system.

[0007] Therefore, there is an urgent need for a method that can improve the handling efficiency of large equipment. Summary of the invention

[0008] The purpose of the present invention is to provide a multi-machine collaborative modular handling method based on a multi-radar fusion algorithm to achieve high-precision positioning of the handling equipment and precise collaborative control between multiple machines, thereby improving the overall performance and safety of the handling system.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions: A multi-machine collaborative modular handling method based on a multi-radar fusion algorithm comprises the following steps: Step (1), installing a plurality of radar sensors on different sides of the carrier; Step (2), obtaining raw data collected by all radar sensors; the raw data includes the surrounding environment of each vehicle and the position changes of adjacent vehicles, such as distance, speed, angle and other position-related data; Step (3), using a multi-radar fusion algorithm to process the original data to obtain multi-radar fusion data; the multi-radar fusion algorithm uses a combination of data layer fusion, feature layer fusion and decision layer fusion to process the original data; In the data layer fusion stage, the raw data collected by each radar (such as distance, speed, angle and other information) are processed by time synchronization and spatial registration, and then the target feature information is extracted; In the feature layer fusion stage, the target feature information (such as geometric features, motion features, etc.) after data layer fusion is extracted and matched. The target features from different radars are fused through feature matching and association algorithms to further improve the recognition and positioning accuracy of the target;

[0010] In the decision-making layer fusion stage, based on the results of feature layer fusion, combined with the handling task requirements and control strategies, multi-machine collaborative motion adjustment decisions are generated, including speed adjustment, path optimization and collision avoidance strategies, and decision instructions are sent to the control systems of each carrier. Step (4): Based on the fusion results and the instructions output by the decision layer, the motion state of each transport device is controlled, including acceleration, direction, and speed adjustment, to ensure the synchronization and collaboration of each device and complete the task of transporting large equipment in a complex environment.

[0011] Preferably, the method further comprises: step (5), real-time monitoring and evaluation of the execution results of the handling task; if deviations or abnormalities are found, the multi-radar fusion algorithm and control strategy are optimized through a closed-loop control mechanism to further improve the reliability and accuracy of the system.

[0012] Preferably, the radar sensor adopts a time-of-flight method, and the formula is as follows: Where L is the distance between the target and the radar, c is the speed of light, t1 is the time when the laser signal is emitted, and t2 is the time when the returned laser signal is received.

[0013] Preferably, the target is a transport device in a transport system and related objects in a transport operation environment; The relevant objects in the handling operation environment include obstacles and objects to be handled; obstacles such as shelves, pillars, other equipment or personnel, etc., need to detect their shape, size, whether they are moving, and moving speed and direction for path planning and safe operation; objects to be handled: if there are specific large equipment or goods, it is necessary to understand their geometric characteristics such as shape, size, weight distribution and motion characteristics such as initial position in order to formulate handling strategies.

[0014] The second object of the present invention is to provide a multi-machine collaborative modular handling system based on a multi-radar fusion algorithm for implementing the above method, comprising: Control systems, actuators, communication modules and multiple carriers; Multiple radar sensors are installed on different sides of each handling device, and a posture sensor is installed inside; The control system is used to process radar sensor data, generate motion control instructions, and send motion control instructions to the actuator; the actuator is used to receive motion control instructions and drive the carrier to complete the specified motion trajectory; the communication module is used to realize real-time data sharing between multiple carriers.

[0015] A third object of the present invention is to provide an electronic device, comprising a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above method.

[0016] A fourth object of the present invention is to provide a machine-readable storage medium storing machine-executable instructions, which, when called and executed by a processor, prompt the processor to implement the above method.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. High-precision positioning capability: Through multi-radar fusion technology, the advantages of different types of radars are fully utilized to achieve high-precision measurement of the position and posture of the handling equipment. In complex environments, the location information of the equipment can still be accurately obtained, which effectively improves the positioning accuracy of the handling system and provides a guarantee for accurate handling.

[0018] 2. Precise collaborative control: Real-time and accurate information on the relative position and motion status of multiple machines enables the handling system to achieve precise collaborative control. Each transport device can respond quickly based on the fused information and maintain strict synchronous motion, which improves handling efficiency, reduces the risk of collision between devices, and enhances the safety and stability of the handling process.

[0019] 3. Enhanced environmental adaptability: The combined use of different types of radars improves the adaptability of the handling system to various harsh environments. Whether in the presence of strong light, dust, fog or other interference factors, the multi-radar fusion system can work reliably to ensure the smooth progress of the handling task and expand the scope of application of the handling system.

[0020] 4. Improve system reliability and flexibility: Multi-radar fusion technology provides redundant position detection means for the handling system. When a radar fails, the system can still rely on other radars to work normally, which improves the reliability of the system. At the same time, this technology can flexibly adjust the working mode and parameters of the radar according to different handling tasks and environmental requirements, further improving the flexibility and adaptability of the handling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the present invention.

[0022] Figure 2 This is the architecture diagram of the multi-radar fusion algorithm. DETAILED DESCRIPTION

[0023] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0024] like Figure 1 As shown, the present invention provides a multi-machine collaborative modular handling method based on a multi-radar fusion algorithm, comprising the following steps: 1. Radar installation and debugging According to the designed radar layout plan, the radar is installed on each carrier. During the installation process, high-precision mounting brackets and positioning tools are used to ensure that the installation angle and position accuracy of the radar meet the requirements. After the installation is completed, the radar is debugged. First, the function test of a single radar is carried out to check whether its transmission and reception signals are normal and whether the measurement accuracy meets the index requirements. Then the joint debugging of the multi-radar system is carried out. By setting multiple targets with known positions, the detection, positioning and tracking capabilities of the multi-radar system are tested, and the radar parameters (such as transmission power, frequency, scanning frequency, etc.) are fine-tuned to optimize the performance of the radar system.

[0025] By combining the high-precision attitude sensors built into the carrier with multiple radar sensors installed on the sides of the carrier, dual monitoring is achieved to ensure high-precision, low-latency collaborative control between carriers. The attitude sensors built into each carrier can monitor the motion state of the carrier in real time, including key data such as acceleration, angular velocity, and tilt angle. These data are processed by the embedded control system to accurately control the attitude and motion trajectory of each carrier to ensure the stability and synchronization of each carrier in the handling task. At the same time, multiple radar sensors are installed on different sides of the carrier to monitor the surrounding environment and the position changes of adjacent carriers. The radar locates the relative position of other carriers in real time by emitting electromagnetic waves and receiving reflected signals. Through the collaborative work of multiple radars, the system can accurately sense the distance and relative speed between carriers, ensuring that multiple carriers maintain synchronization and coordination under various operating conditions. At the same time, the data of the attitude sensors and radar sensors will be fused and feedback adjusted in real time through the closed-loop control system to achieve low-latency, high-precision synchronous operation of carriers.

[0026] 2. Implementation of multi-radar data fusion algorithm In the central control unit of the handling system, a multi-radar data fusion algorithm program is written. Efficient programming languages ​​(such as C++, Python, etc.) and algorithm libraries (such as OpenCV, MATLAB, etc.) are used to implement time synchronization and spatial registration algorithms in data layer fusion.

[0027] like Figure 2 As shown in the figure, multi-radar data are processed by combining data layer fusion, feature layer fusion and decision layer fusion.

[0028] In the data layer fusion stage, the raw data collected by each radar (such as distance, speed, angle and other information) is time synchronized and spatially aligned. Time synchronization is achieved through a precise clock synchronization system to ensure that the data from each radar is fused under the same time reference.

[0029] Develop feature layer fusion algorithms to achieve the functions of target feature extraction, matching and fusion. Use image processing technology and machine learning algorithms to process radar echo data, extract target feature information, and perform feature association and fusion through feature matching algorithms. Feature layer fusion mainly processes target feature information after data layer fusion. Extract the geometric features and motion features of the target, match and fuse target features from different radars through feature matching and association algorithms, and further improve the accuracy of target recognition and positioning. For example, use the similarity of the target's contour features under different radar perspectives to match and determine the target's true position and posture.

[0030] In the decision-making layer fusion part, according to the control strategy and task requirements of the handling system, write decision logic programs such as collision avoidance algorithm and path planning algorithm. Take the fused position and motion state information as input, calculate the optimal motion control instructions for each carrier, and send them to the actuator through the communication interface. The decision-making layer fusion is based on the results of the data layer and feature layer fusion, combined with the task requirements and control strategy of the handling system to make decisions. For example, when it is detected that the distance between multiple handling devices is too close, which may cause a collision risk, the optimal motion adjustment strategy of each device is calculated according to the fused position and speed information, through the preset collision avoidance algorithm, including speed adjustment amount, steering angle, etc., and the decision instructions are sent to the control system of each carrier to achieve collaborative control between multiple machines.

[0031] 3. System integration and testing Integrate the multi-radar fusion system with other components of the handling system, including control systems, actuators, communication modules, etc. Ensure that the electrical connections between components are reliable, the communication protocols are compatible, and the data transmission is stable.

[0032] Conduct system tests in actual handling scenarios. Select large equipment of different types and sizes for handling tests, simulate various complex environmental conditions (such as strong light, dust, different terrains, etc.), and observe the operation of the handling system. By monitoring the actual movement trajectory and posture changes of the equipment, and comparing them with the preset handling path and requirements, evaluate the impact of multi-radar fusion technology on handling accuracy and collaborative control effects. Based on the test results, further optimize and adjust the system to ensure that the system can operate stably and reliably to meet actual handling needs.

[0033] It is to be understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-machine collaborative modular handling method based on a multi-radar fusion algorithm, characterized in that: The following steps are involved: Step (1), installing a plurality of radar sensors on different sides of the carrier; Step (2), obtaining raw data collected by all radar sensors; the raw data includes the surrounding environment of each vehicle and the position change of adjacent vehicles; Step (3), using a multi-radar fusion algorithm to process the original data to obtain multi-radar fusion data; the multi-radar fusion algorithm uses a combination of data layer fusion, feature layer fusion and decision layer fusion to process the original data; In the data layer fusion stage, the raw data collected by each radar is processed for time synchronization and spatial registration, and then the target feature information is extracted; In the feature layer fusion stage, the target feature information after data layer fusion is extracted and matched; The target features from different radars are integrated through feature matching and correlation algorithms to further improve the target recognition and positioning accuracy; In the decision-making layer fusion stage, based on the results of feature layer fusion, combined with the handling task requirements and control strategies, multi-machine collaborative motion adjustment decisions are generated, including speed adjustment, path optimization and collision avoidance strategies, and decision instructions are sent to the control systems of each carrier. Step (4): Based on the fusion results and the instructions output by the decision layer, the motion state of each transport device is controlled, including acceleration, direction, and speed adjustment, to ensure the synchronization and collaboration of each device and complete the task of transporting large equipment in a complex environment.

2. The multi-machine collaborative modular handling method based on multi-radar fusion algorithm according to claim 1 is characterized in that: The target feature information includes geometric features and motion features.

3. The multi-machine collaborative modular handling method based on multi-radar fusion algorithm according to claim 1 is characterized in that: The method further includes: step (5), real-time monitoring and evaluation of the execution results of the handling task; if deviations or abnormalities are found, the multi-radar fusion algorithm and control strategy are optimized through a closed-loop control mechanism to further improve the reliability and accuracy of the system.

4. The multi-machine collaborative modular handling method based on multi-radar fusion algorithm according to claim 1 is characterized in that: The radar sensor uses the time-of-flight method, and the formula is as follows: ； Where L is the distance between the target and the radar, c is the speed of light, t1 is the time when the laser signal is emitted, and t2 is the time when the returned laser signal is received.

5. A multi-machine collaborative modular handling system based on a multi-radar fusion algorithm for implementing the method described in any one of claims 1 to 4, characterized in that: include: Control systems, actuators, communication modules and multiple carriers; Multiple radar sensors are installed on different sides of each handling device, and a posture sensor is installed inside; The control system is used to process radar sensor data, generate motion control instructions, and send motion control instructions to the actuator; the actuator is used to receive motion control instructions and drive the carrier to complete the specified motion trajectory; the communication module is used to realize real-time data sharing between multiple carriers.

6. An electronic device comprising a processor and a memory, characterized in that: The memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the method according to any one of claims 1 to 4.

7. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the method described in any one of claims 1 to 4.

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