Jiansheng Peng^1,2,*, Jinsong Guo¹, Fengbo Bao¹, Chengjun Yang², Yong Xu², Yong Qin²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2971-2994, 2023, DOI:10.32604/cmc.2023.041897

Abstract The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019. With the development of sensors and smart devices, factories and enterprises have accumulated a large amount of data in their daily production, which creates extremely favorable conditions for robots to perform machine learning. However, in recent years, people’s awareness of data privacy has been increasing, leading to the inability to circulate data between different enterprises, resulting in the emergence of data silos. The emergence of federated learning provides a feasible solution to this problem, and the combination of federated learning and multi-robot systems… More >

Virginijus Baranauskas^1,*, Žydrūnas Jakas¹, Kastytis Kiprijonas Šarkauskas¹, Stanislovas Bartkevičius², Gintaras Dervinis¹, Alma Dervinienė³, Leonas Balaševičius¹, Vidas Raudonis¹, Renaldas Urniežius¹, Jolanta Repšytė¹

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1343-1356, 2022, DOI:10.32604/iasc.2022.022288

Abstract The research work presents a collision prediction method of mobile robots. The authors of the work use so-called, virtual cocoons to evaluate the collision criteria of two robots. The idea, mathematical representation of the calculations and experimental simulations are presented in the paper work. A virtual model of the industrial process with moving mobile robots was created. Obstacle avoidance was not solved here. The authors of the article were working on collision avoidance problem solving between moving robots. Theoretical approach presents mathematical calculations and dependences of path angles of mobile robots. Experimental simulations, using the software Centaurus CPN, based on… More >