Structural integrity of seawater pipelines in nuclear power plants is a very important issue. In accordance with the operating technical guidelines, the human operators directly enter the pipe and inspect it at every maintenance test. However, in this regard, safety issues such as narrow space and harmful gas are emerging every year.
In response to these needs, a quadruped robot that can inspect underground pipes and assist workers has been developed. The robot has an articulated robotic arm that can receive an impact sound of hammering a pipe wall to test pipe integrity. The state of the pipe was examined using a Convolutional Neural Network algorithm. On the other hand, moving in a plumbing environment requires stable walking ability. To determine the gait sequence, a hierarchical gait controller is proposed. The hybrid controller, which consists of joint impedance and torque control, calculated from Model Predictive Control, can switch the gait modes comparing the reference and the current foot contact condition at each control cycle.
For most industrial robots using position control, self-collision is a headache. Without well-defined constraints at a joint level, self-collision can lead to damage to a robot or even injury to the operator. In this study, a new methodological approach is proposed that is different from the traditional methods of using sensors or analysing kinematics. The proposed method uses a support vector machine to construct a joint boundary map based on the robot's movement in the workspace. To verify its feasibility and performance, simulations are conducted. The proposed method is believed to help reduce the cost and computational burden. It is expected to be utilised even in applications where the existing methods are difficult to use.
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