Estudo de redes neurais artificiais como alternativa ao método do jacobiano para a cinemática inversa

Abstract

The inverse kinematics problem is generally very complex and many traditional solutions are targeted only to robots of certain specific topologies. The iterative method based on the (pseudo)inverse of the Jacobian matrix is a well-known, proven, and reliable general approach that can be applied to a wide variety of manipulators. However, it relies on linearizations that are only valid within a very tight neighborhood around the current pose of the manipulator. This requires the robot to move at very short steps, intensively recalculating its trajectory along the way, making this approach inefficient for certain applications. Neural networks, for their known capacity of modelling highly non-linear systems, appear as an interesting alternative. In this work is demonstrated that neural networks can indeed be successfully trained to map task space displacements into joint angle increments, outperforming the method based on the inverse of the Jacobian when dealing with larger displacement increments. The study is validated showing comparative results for hypothetical 3 joint planar arm, 3 joint 3D arm and the Thormang3 robot.