Skillful adaptation of a 7-DOF manipulator to avoid moving obstacles in a teleoperated force control task

This paper presents a method to exploit the redundancy of an industrial manipulator with 7 degrees of freedom (DOF), called PA-10 to adapt its configuration to avoid moving obstacles in a teleoperated force control task. This self adaptive skill on the slave manipulator side is very important, because teleoperation is often performed in dangerous or partially unknown environments, where unexpected changes such as moving obstacles can well be expected. In such situations, the control ability of the master side is very limited due to the practical limitations of vision sensors to capture a comprehensive view of the environment and the limitations of the master side degrees of freedom. The proposed method relies on two modules of an intelligent controller on the slave side. The first is an on-line fuzzy neural network (FNN) for intelligent force control, and the second is a configuration controller optimized using an evolutionary algorithm that works in harmony with the first to exploit redundancy to react to avoid moving obstacles, without inhibiting the progress of the former. The second controller generates joint velocity commands in the null space of the hand Jacobian, so that its activation does not affect the force controller. Here we show that the proposed method can skillfully avoid a moving obstacle without stopping the force control task. This skillful adaptation ability can significantly improve the efficiency and safety of teleoperated force control tasks with less burden on the master side. The paper presents some promising experimental results to demonstrate the effectiveness of the proposed method