2024 Vol.7 Jun.N03

• Title: Optimization of Artificial Intelligence Technology in Service Robot Control Systems
• Name: Lina zhang
• Company: zhengzhou business university, college of information, mechanical and electrical engineering, zhengzhou 451200
• Abstract:

  As the robot may be subject to different aspects of environmental interference during the motion process submission, which leads to a large deviation of the control effect from the target control route. In this regard, the optimization of the application of artificial intelligence technology in the control system of service robots is proposed. Firstly, by combining the kinematic mechanism and the structure of the robot, the kinematic modeling of the service robot is realized by defining the joint states of the robot. Then two incremental PID controllers are designed to control the position of the robot at the same time, which are the lateral position controller and the longitudinal position controller, and the controller structure is designed. Finally, the robot's motion space is mapped to the state space of reinforcement learning according to the kinematic model, and the control results are output in combination with the optimization of the reward function. Finally, the proposed practical approach is validated in terms of robot control effectiveness by constructing an experimental comparison session. By visualizing and analyzing the results, it can be clarified that under the control method, the error between the robot's motion trajectory and the target trajectory is low, which has a more ideal control effect.

  
  

• Keyword: artificial intelligence; service robots; control algorithms; reinforcement learning;
• DOI: 10.12250/jpciams2024090614
• Citation form: Lina zhang.Optimization of Artificial Intelligence Technology in Service Robot Control Systems[J]. Computer Informatization and Mechanical System,2024,Vol.7,pp.61-65

bibliography

[1] Shimba K , Kotani K , Jimbo Y .Evaluating Axon Conduction Characteristics of Cultured Sensory Neurons Toward Soft Robot Control[J].J. Robotics Mechatronics, 2022, 34:263-265.

[2] Dubay S , Melek W , Nielsen C .Path Following Control for Human-Robot Collaborative Tasks[J].2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2022:931-936.

[3] Qin Y , Ouyang W .Application of Hybrid Algorithm in Robot Intelligent Control[J].2023 2nd International Conference for Innovation in Technology ( INOCON), 2023:1-5.

[4] Zhang S , Chen Y , Zhang L ,et al. Study on Robot Grasping System of SSVEP-BCI Based on Augmented Reality Stimulus[J]. 2023, 28(2):322-329.

[5] Makino K , Hirai T , Murase T .Mobility Control of Avoiding Interference for Autonomous Mobile Robot Ad Hoc Networks[J].2023 IEEE International Conference on Consumer Electronics (ICCE), 2023:1-6.

[6] Sawadwuthikul G , Tothong T , Lodkaew T ,et al. Visual Goal Human-Robot Communication Framework with Few-Shot Learning: a Case Study in Robot Waiter System[J].IEEE Transactions on Industrial Informatics, 2022, 18(3):1883-1891.

[7] Ren C , Li C , Hu L ,et al. Adaptive model predictive control for an omnidirectional mobile robot with friction compensation and incremental input constraints:[J].Transactions of the Institute of Measurement and Control, 2022, 44(4):835-847.

[8] Liu X , Luo Y .Analytical Design of Optimal Fractional Order PID Control for Industrial Robot based on Digital Twin[J].2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI), 2022:1-6.

[9] Yang C , Xie H , Xu H ,et al. Rat-Robot Autonomous Navigation System Based on Wearable Sensors[J].IEEE Sensors Journal, 2023, 23:11007-11015.

[10] Braun C , Ramesh A , Rothfuss S ,et al. Model Predictive Control of the Degree of Automation Optimizing Robot Health[J].2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2023:000381-000386.

[11] Xin J , Qu Y , Zhang F ,et al. Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning[J].Complex System Modeling and Simulation, 2022, 2(4):273-287.

[12] Yang Z , Bi L , Chi W ,et al. Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control[J].Complex System Modeling and Simulation, 2022, 2(4):307-321.

[13] Zhen S , Zhang M , Liu X ,et al. Model-based robust control design and experimental validation of collaborative industrial robot system with uncertainty[J].Asian Journal of Control, 2022, 25:1663 - 1674.

[14] Wu Y , Qi J , Hussain S .Application of Image Processing Variation Model Based on Network Control Robot Image Transmission and Processing System in Multimedia Enhancement Technology[J].J. Robotics, 2022, 2022:6991983:1-6991983:10.

[15] Wang X , Bai W , Zheng H ,et al. Design of Mobile Robot Control System Based on EMG Signal[J].2022 5th International Conference on Intelligent Autonomous Systems (ICoIAS), 2022:202-208.