Research on Key Technologies of Multi robot Collaborative Automation Control

Research on Key Technologies of Multi robot Collaborative Automation Control

Liu Zhiyong

Institude of Spacecraft System Engineering    100094

  Abstract: This article aims to explore the key technologies of multi robot collaborative automation control, in order to achieve efficient collaboration and autonomous control of robot equipment. By establishing a cloud platform to collect and process data from multiple robots, optimal path planning and collision avoidance control can be achieved. The research results indicate that this technology can improve task completion efficiency and accuracy, reduce labor costs, and provide reference for future intelligent manufacturing and automation industries.

  Keywords: Multi robot; Collaboration; Automation control; technology

  1 Introduction

With the continuous development of robotics technology, multi robot collaborative automation control has been widely applied in many fields, such as industrial production, medical health, military reconnaissance, etc. Multi robot collaborative automation control can improve the efficiency and quality of task execution, reduce labor costs, and enhance the reliability and safety of the system. This article will explore the key technologies of multi robot collaborative automation control, including collaborative perception, collaborative decision-making, and collaborative action.

  2 Overview of Multi robot Collaborative Automation Control

  2.1 Collaborative Control Concept

Collaborative control is the core of multi robot collaborative automation control, which refers to the process of multiple robots completing a task together through mutual coordination and cooperation. Collaborative control aims to achieve organic coordination between robots and improve the overall efficiency and quality of the system. In the process of collaborative control, robots can achieve key operations such as information sharing, resource optimization configuration, and task pision to maximize collaborative effects. Collaborative control technology can be widely applied in multiple fields, such as industrial production, medical health, military reconnaissance, etc., providing more efficient and intelligent services for humanity.

  2.2 Collaborative Control Process

The collaborative control process is the core link of multi robot collaborative automation control, which includes the following steps. Firstly, task allocation. According to task requirements and robot capabilities, the total task is decomposed into several sub tasks and assigned to each robot for execution. When assigning tasks, it is necessary to consider the performance, environmental factors, and collaborative efficiency of the robot. Secondly, information sharing. Robots share information and transmit real-time data such as task progress, target status, and environmental information to facilitate collaborative work. Once again, dynamically adjust. During the task execution process, dynamic adjustments such as formation adjustment, path planning, and task reassignment need to be made between robots based on task progress and environmental changes to ensure that the overall system can adapt to environmental changes and maintain efficient collaborative effects. Finally, end the task. After all subtasks are completed, the collaborative control process ends. Summarize and evaluate between robots to provide experience reference for subsequent collaborative tasks.

  3 Multi robot collaborative automation perception technology

  3.1 Distributed Sensor Networks

Distributed sensor networks are an important technology in multi robot collaborative automation control. It achieves information sharing and collaborative work by integrating multiple sensors into a network. Distributed sensor networks can perceive changes in the surrounding environment and monitor, track, and record these changes. At the same time, it can also analyze and fuse the collected data to obtain more accurate environmental information. By applying distributed sensor network technology, the perception, collaboration, and adaptability of robots can be improved, thereby completing various tasks more efficiently.

  3.2 Fusion Perception Algorithm

The fusion perception algorithm is an important component of multi robot collaborative automation perception technology. It improves the accuracy and reliability of perception results by fusing and processing data obtained from multiple sensors. The fusion perception algorithm follows certain algorithm rules to optimize and integrate the data obtained from multiple sensors to obtain more comprehensive and accurate perception results. Commonly used fusion perception algorithms include Bayesian estimation, Kalman filtering, neural networks, etc. These algorithms can be selected and optimized based on different application scenarios and requirements to achieve more efficient perception effects.

  3.3 Collaborative positioning methods

Collaborative positioning is one of the key technologies in multi robot collaborative automation control, which utilizes multiple sensor information to achieve precise positioning and collaborative navigation among multiple robots. Collaborative positioning methods not only consider the positioning accuracy of a single robot, but also consider the relative positions and collaborative relationships between multiple robots. Commonly used collaborative localization methods include RSSI localization based on signal strength attenuation, TOA localization based on signal propagation time, and TDOA localization based on signal propagation speed. These algorithms have their own advantages and disadvantages, and they need to be selected based on specific application scenarios and requirements. Collaborative positioning also needs to consider environmental factors and signal interference to improve positioning accuracy and stability.

  4 Multi robot collaborative automation decision-making technology

  4.1 Autonomous pision of labor method

The autonomous pision of labor method is an important component of multi robot collaborative automation decision-making technology. It achieves autonomous pision of labor and collaborative execution of tasks by decomposing the total task into several subtasks and assigning them to different robots for execution. The autonomous pision of labor method needs to consider factors such as robot performance, task difficulty, and environment to achieve reasonable and efficient pision of labor. The key issues that need to be addressed in the autonomous pision of labor method are how to decompose tasks and allocate robot tasks, as well as how to ensure the overall completion effect of tasks. The commonly used autonomous pision of labor methods include rule-based methods, optimization algorithm based methods, and learning based methods. These methods can be selected and optimized according to different application scenarios and requirements.

  4.2 Collaborative Path Planning

Collaborative path planning is another important component of multi robot collaborative automation decision-making technology. It formulates the optimal execution path for each robot by considering the interrelationships and environmental factors among multiple robots, efficiently completing the overall task. Collaborative path planning needs to consider factors such as robot motion constraints, task requirements, and environmental changes, and optimize the path based on this. Common collaborative path planning methods include graph based methods, search based methods, and optimization algorithm based methods. Corresponding methods can be selected and optimized based on different application scenarios and requirements to achieve efficient path planning and task execution. Collaborative path planning also needs to consider real-time and dynamic characteristics to adapt to environmental changes and changes in task requirements.

  4.3 Dynamic Task Allocation

Dynamic task allocation is another key aspect of multi robot collaborative automation decision-making technology. It assigns tasks to each robot in real-time based on task requirements and robot status to achieve efficient completion of the overall task. Dynamic task allocation requires consideration of factors such as task requirements, robot status, and environmental characteristics, and based on this, task allocation and adjustment should be carried out. The commonly used dynamic task allocation methods include rule-based methods, optimization algorithm based methods, and learning based methods. These methods can be selected according to different application scenarios and requirements to achieve efficient dynamic task allocation. Dynamic task allocation also needs to consider real-time and dynamic characteristics to adapt to changes in task requirements and environmental changes.

  5 Collaborative automation action control of robots

  5.1 Formation control technology

Formation control is an important technology that supports multi robot collaborative automation action control. It controls the position and posture of multiple robots to arrange them in a predetermined formation, in order to achieve the completion of the overall task. Formation control needs to consider factors such as robot performance, task requirements, and environmental factors to achieve reasonable formation design and control. The commonly used formation control methods include rule-based methods, optimization algorithm based methods, and learning based methods. These methods can be selected and optimized according to different application scenarios and requirements to achieve efficient formation control and task completion. Formation control also needs to consider real-time and dynamic characteristics to adapt to changes in task requirements and environmental changes.

  5.2 Collaborative Navigation Methods

Collaborative navigation is one of the important technologies in multi robot collaborative automation action control. It achieves overall task navigation and path planning through collaborative cooperation among multiple robots. Collaborative navigation requires considering factors such as robot motion constraints, task requirements, and environmental changes, and based on this, path planning and navigation control are carried out. The following are the main contents of collaborative navigation methods. Firstly, establish a cloud platform. Establishing a cloud platform is the first step in collaborative navigation methods. Cloud platforms can collect various data, including robot status, task requirements, environmental information, etc., and provide computing and storage resources for robots. Secondly, collect data. Robots collect various data through sensors and communication devices, including their own status, environmental information, task requirements, etc. These data are transmitted to the cloud platform for subsequent processing. Finally, data processing. The cloud platform processes and analyzes the collected data, extracting useful information such as the robot's position, speed, direction, etc.

  5.3 Collision avoidance technology

Collision avoidance technology is one of the key technologies in multi robot collaborative automation action control. It controls the movement and distance between multiple robots to avoid collisions and conflicts between them, ensuring the safe and stable completion of the overall task. Collision avoidance technology needs to consider factors such as robot motion constraints, task requirements, and environmental changes, and on this basis, carry out path planning and collision avoidance control. Common collision avoidance techniques include rule-based methods, optimization algorithm based methods, and learning based methods, which can be selected and optimized according to different application scenarios and requirements to achieve efficient collision avoidance control and task completion.

  6 Conclusion

Multi robot collaborative automation control technology is an important support for multi robot collaborative automation control. By utilizing technologies such as collaborative perception, decision-making, and action control, it is possible to achieve organic cooperation and efficient collaborative work among multiple robots, thereby improving the efficiency and quality of task completion. However, there are still some challenges that need further improvement, such as data transmission and processing, real-time and dynamic performance. With the continuous development of artificial intelligence and robotics technology, multi robot collaborative automation control technology will be applied in a wider range of fields.

References

[1] Wang Yaonan, Jiang Yiming, Jiang Jiao, etc Key Technologies of Robot Perception and Control and Their Applications in Intelligent Manufacturing [J] Journal of Automation, 2023, 49 (3): 494-513

[2] Hu Kai, Chen Xu, Yang Pinghua, et al A review of research on multi robot collaborative formation based on sliding mode variable structure control [J] Journal of Nanjing University of Information Science&Technology (Natural Science Edition)/Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban), 2022, 14 (2)