Underwater robotic operations using a decentralized adaptive neurocontroller

Abstract

The authors explored whether neural networks can improve telerobotic performance. The neural network design is based upon an innovative three-tier distributed control architecture. The neurocontroller was tested on two simulated two-joint robot arms which had different dynamics. The tests compared the performance of five controller configurations. The findings indicate that a decentralized adaptive neurocontroller performed as well as or better than standard adaptive and nonadaptive controllers. This approach to autonomous control and path planning circumvents the tradeoff between speed and desired levels of accuracy, stability, and robustness by generating optimal trajectories without sacrificing computational speed or robustness.<>