Abstract
The study of articulated robots in higher education necessarily goes through the development of their kinematic models. The inverse kinematic model is usually described algebraically, although this representation is often difficult to obtain. Thus, the use of genetic algorithms in teaching robotics can be very attractive, since they allow students to easily develop models and predict the behavior of robots before their formal development. This way, the results of this work present a relatively fast way to simulate the inverse kinematic model, allowing the designer to have a broader view of the structure of a robot, coming to identify points that must be corrected or that can be optimized. It can be concluded that the use of genetic algorithms in robotics teaching is viable, having as main advantages their easy computational implementation and precision in the representation of kinematic models. .
Highlights
The motion described by a manipulator robot can be represented by its direct and inverse kinematic models, as described in Craig (2017)
Obtaining the direct kinematic model is relatively simple, since it is defined by a set of transformations among the reference systems of the degrees of freedom (DOF) or joints
Obtaining the inverse kinematic model, tends to be more complex than obtaining the direct kinematic model, since it involves the solution of a system of non-linear equations that can admit more than one solution
Summary
The motion described by a manipulator robot can be represented by its direct and inverse kinematic models, as described in Craig (2017). Obtaining the direct kinematic model is relatively simple, since it is defined by a set of transformations among the reference systems of the degrees of freedom (DOF) or joints. Through this model it is possible to determine the position of the tool on the free end of the robot being known the positions of its joints. The inverse kinematic model, in turn, allows the determination of the state of the joints of a robot according to the desired position for its tool In this way, when a trajectory for the tool is defined, it is possible to determine the set of joint positions that will allow the robot to describe the desired motion (MILLER, 2017). Even in relatively simple cases, as for the two DOF planar robot described below, the definition of the inverse kinematic model is not trivial
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