Industrial Internet Robot Collaboration System and Edge Computing Optimization
In a complex environment, for a mobile robot to safely and collision - free avoid all obstacles, it poses high requirements for its intelligence level. Given that the information such as the position and geometric characteristics of obstacles is random, the control parameters of the robot, such as velocity and angular velocity, are also prone to random deviations. To address this issue in the framework of the Industrial Internet Robot Collaboration System, this paper proposes a global path control scheme for mobile robots based on deep learning. First of all, the dynamic equation of the mobile robot is established. According to the linear velocity and angular velocity of the mobile robot, its motion behaviors are divided into obstacle - avoidance behavior, target - turning behavior, and target approaching behavior. Subsequently, the neural network method in deep learning is used to build a global path planning model for the robot. On this basis, a fuzzy controller is designed with the help of a fuzzy control algorithm to correct the deviations that occur during path planning, thereby achieving optimized control of the robot's global path. In addition, considering edge computing optimization, the proposed model can process local data at the edge device, reducing the communication burden between the robot and the central server, and improving the real time performance of path planning. The experimental results show that for the mobile robot controlled by the research method in this paper, the deviation distance of the path angle is within 5 cm, the deviation convergence can be completed within 10 ms, and the planned path is shorter. This indicates that the proposed scheme can effectively improve the global path planning ability of mobile robots in the industrial Internet environment and promote the collaborative operation of robots through edge computing optimization.
Code (0)
등록된 구현이 없습니다.
Tasks
Edge-computingSimilar Papers 제목 키워드 기반
The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edg…
Deep Reinforcement LearningEdge-computingglobal-optimizationSchedulingInHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics
Nowadays, humans and robots are working more closely together. This increases business productivity and product quality, leading to efficiency and growth. However, human and robot collaboration is rather static; robots m…
Action RecognitionTemporal Action LocalizationBuilding Blocks to Empower Cognitive Internet with Hybrid Edge Cloud
As we transition from the mobile internet to the 'Cognitive Internet,' a significant shift occurs in how we engage with technology and intelligence. We contend that the Cognitive Internet goes beyond the Cognitive Intern…
Decision MakingGaze Detection and Analysis for Initiating Joint Activity in Industrial Human-Robot Collaboration
Collaborative robots (cobots) are widely used in industrial applications, yet extensive research is still needed to enhance human-robot collaborations and operator experience. A potential approach to improve the collabor…
Multi-Camera Hand-Eye Calibration for Human-Robot Collaboration in Industrial Robotic Workcells
In industrial scenarios, effective human-robot collaboration relies on multi-camera systems to robustly monitor human operators despite the occlusions that typically show up in a robotic workcell. In this scenario, preci…