In this article, solving and optimizing the problem of forward and inverse kinematics of SCARA is studied. This robot belongs to series robots and it has four degrees of freedom. First, we specify the coordinate axes for each joint and use it to extract the Danavit-Hartenberg parameters. Next, we examineforward kinematics of the robot and obtain the rotation matrices and the homogeneous transformation matrix and calculate the forward kinematics of the robot. Next, the method of solving the inverse kinematics problem of the robot is studied using different algorithms, including Cultural Algorithm, Genetic-Hybrid Algorithm, Gray Wolf Optimization, Firefly Algorithm, Ant Colony Optimization and Particle Swarm Optimization.Them, we optimize the inverse kinematics of the robot using these algorithms in two ways: fixed point and circular path. In the end, the effectiveness of the proposed approaches for solving the inverse kinematics problem of the SCARA robot is evaluated with multiple simulations. Manuscript profile

Cyber-physical systems rely heavily on the functionality of sensors and actuators to operate effectively. Sensors transfer measurements from the physical part to the control and cyber part and while actuators that play the role of applying control signals to the physical part. The placement of sensors and actuators is an important issue for ensuring system’s observability and controllability. This placement should be done in way that the system remains controllable and observable with fewest number of sensors and actuators.. Minimizing the number of sensors and actuators has a significant effect on cost and energy reduction. In this study, a new approach is introduced based on the existing paths and non-existent paths between the states of a system. In the proposed approach, the non-existent paths are defined as infinite paths. The best nodes are selected as the location of sensors and actuators based on the infinite paths and their numbers. The numerical simulations illustrate that the Infinite Value Algorithm has performed better in the placements in this way that it has consumed the unique solution in less time compared the Genetic Algorithms. Manuscript profile