S. Palani Murugan^1,*, M. Chinnadurai¹, S. Manikandan²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 181-194, 2022, DOI:10.32604/cmc.2022.016152 - 07 September 2021

Abstract The development of intelligent algorithms for controlling autonom- ous mobile robots in real-time activities has increased dramatically in recent years. However, conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories. The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time. The proposed system is implemented using the AlphaBot platform, and the performance of the system is evaluated according to the obstacle prediction More >

Yung-Hsiang Chen¹, Yung-Yue Chen², Shi-Jer Lou³, Chiou-Jye Huang^4,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 357-372, 2022, DOI:10.32604/iasc.2022.018663 - 03 September 2021

Abstract This research presents an adaptive energy-saving H₂ closed-form control approach to solve the nonlinear trajectory tracking problem of autonomous mobile robots (AMRs). The main contributions of this proposed design are as follows: closed-form approach, simple structure of the control law, easy implementation, and energy savings through trajectory tracking design of the controlled AMRs. It is difficult to mathematically obtained this adaptive H₂ closed-form solution of AMRs. Therefore, through a series of mathematical analyses of the trajectory tracking error dynamics of the controlled AMRs, the trajectory tracking problem of AMRs can be transformed directly into a solvable More >