Many researchers have been doing their best in the last decades to improve such important characteristics of robots as their positioning and orientation accuracy. However, constantly increasing industry demand of achieving high positioning accuracy still requires further research and improvements in this area. Have you ever tried to build the highest one-column tower out of domino blocks? If you imagine that the height of your domino tower is equivalent to the robot end-effector positioning accuracy, then the errors in robot joints can be equivalent to the inaccuracy of placing the domino blocks on the top of your tower. Hence, if the position of the following domino block in the tower can compensate previous mistakes made with other domino blocks, then one can expect that joint errors in robots can also compensate each other if one is able to find appropriate configurations for robot joints. The latter problem was targeted in this book. The general-purpose algorithms and sim ulation frameworks to find optimal configurations of various types of industrial robots are presented. The method for robotic manipulator positioning accuracy improvement using joint error maximum mutual compensation was developed to help engineers to design and implement their robotic systems with the maximum possible positioning and orientation accuracy. Our approach can be widely used on the stages of robot end-effector trajectory planning. In most practical cases, one can observe the improvement of robot end-effector positioning accuracy by ten - fifteen percent and, in extreme cases, by two times and more. Surprisingly, this can be achieved without any additional hardware or measurement equipment.