Human-robot interactions carry several challenges, the most important being the risk of injury to the human. In industrial robotic systems, robots are mostly caged and isolated from humans in a safety guard environment. However, as time has passed, the use of domestic robots has emerged, leading to a high need in research on robot safety in domestic settings. Human-Robot collaboration is still in an initial stage; thus, safety assessments in domestic environments are critical in the field of collaborative robots or cobots, with simulations being the first stage of research. In this study, a preliminary investigation on the simulation of human’s safety throughout human-robot interactions in home surroundings with no safety fence is presented. A simulation model is designed and developed with Gazebo in the Robot Operating System, ROS-based, to simulate the human-robot interaction. In the robot trajectory, safety interaction can be simulated. In one example, the robot’s speed can be reduced before a collision with a human about to happen, and it can be minimized the risk of the collision or reduce the damage of the risk. After the successful simulation, this can be applied to the real robot in a domestic working environment.
Several secondary path, acoustic noise cancellation modelling causes the problems to increase the complexity of ANC implementation, reduction of performance caused by modelling error and requirement of auxiliary noise for secondary path modelling. The acoustic noise generated is further compounded by using secondary path identification which makes the system complex. There are several available ANC algorithms that do not require secondary path estimation for modifying the FxLMS algorithms. Due to drawbacks such as slow convergence speed and, complexity of the phase shift mechanism, a novel approach with no secondary path modelling is adopted, in which the adaptation stability is guaranteed by switching the sign of the step size. It is combined with the online tuneable delay of the reference signal to significantly improve the adaptation convergence properties of the algorithm.
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