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Detection of the cracks in beam structures using the fuzzy Gaussian inference technique has been investigated in this paper. The fuzzy-logic controller used in the present investigation consists of six input parameters and two output parameters. The input parameters to the fuzzy controller are the relative divergence of the first three natural frequencies and first three mode shapes in dimensionless forms. The output parameters of the fuzzy controller are the relative crack depth and relative crack location in dimensionless forms. To derive the fuzzy rules for the controller, theoretical expressions have been developed considering four parameters such as natural frequencies, mode shapes, crack depths, and crack locations. The strain-energy release rate has been used for calculating the local stiffnesses of the beam for a mode-I type of the crack. Several boundary conditions are outlined that take into account the crack location. Required fuzzy rules are derived for the fuzzy Gaussian controller. The experimental setup has been fabricated in the laboratory for verifying the robustness of the developed fuzzy controller. The developed fuzzy controller can predict the location and depth of the crack in close proximity with the real results. DOI: 10.2514/1.35927

Humanoid robots, with their overall resemblance to a human body, is modeled for flawless interaction with human-made tools or the environment. In this study, navigation of humanoid robot using hybrid Artificial potential field (APF) and Moth flame optimization (MFO) approach have been performed. The hybrid approach provides the final turning angle (FTA), which is optimum to avoid collision with the hindrances. APF utilizes a negative potential field and a positive potential field to find the location of obstacles and target, respectively. The navigation starts towards the target; when the robot interacts with the obstacle, APF provides an intermediate angle (IA). The IA, along with the position of the obstacle, is fed into MFO as an input. This technique provides the FTA (optimum) to avoid collisions and guide a robot to the target. It is implemented in a single humanoid system and a multi-humanoid system. The presence of multiple humanoids can create the chance of inter-collision. It is dismissed by employing a dining philosopher controller to the proposed technique. Simulations and experiments are accomplished on simulated and real humanoid NAO. The coherency in the behavior of the results evaluated by the simulations and real-time experiments demonstrates the efficiency of the proposed AI technique. Comparisons are performed with a previously used method to validate the robustness of the technique. [ABSTRACT FROM AUTHOR]