Abstract
Single robot hose transport is a limit case of linked multicomponent robotic systems, where one robot moves the tip of a hose to a desired position. The interaction between the passive, flexible hose and the robot introduces highly nonlinear effects in the system's dynamics, requiring innovative control design approaches, such as reinforcement learning. This article improves previous approaches to this problem by introducing a novel reinforcement learning algorithm (TRQ-learning) and a new system state definition for the autonomous derivation of the hose–robot control algorithm. Computational experiments based on accurate geometrically exact dynamic splines hose dynamics simulations show the improvement obtained.
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