Solving the inverse kinematics problem for sequential robot manipulators based on fuzzy numerical methods

Abstract

Nowadays the introduction of robotic systems is one of the most common forms of the technological operations automation in various spheres of human activity. Among the robotic systems a special place is occupied by sequential multi-link robotic manipulators (SRM). SRM have become widespread due to relatively small dimensions and high maneuverability, which makes their use indispensable to solve various tasks. In practice, the effectiveness of the functioning of the SRM can be influenced by various types of external environment fuzzy factors. Among the external factors there is a group affecting the ability to determine the exact target position. Such factors often affect technical vision systems. This problem is especially relevant for special purpose mobile robots operating in aggressive environmental conditions. A situation similar to the described one also occurs when a medical robot manipulator is used for minimally invasive surgery, when the role of the control and monitoring system is assumed by an operator. In this regard, the organization of effective control taking into account influence of the external fuzzy factors, that prevent the correct recognition of the target position of the SRM instrument, is an urgent problem. The authors consider the solution of the inverse kinematics problem for SRM based on the use of fuzzy numerical methods, taking into account the possible occurrence of singular configurations in the process of solving.

Using neural-fuzzy inference to control the functioning of a human-machine system in the form of an exoskeleton with links of variable length, taking into account the effects of lag and synchronization of electric drives

“…The creation of hybrid neural-fuzzy control systems [6][7][8][9][10][11][12][13][14], which combine the advantages of neural networks: training and adaptation with control models based on fuzzy logic that a person uses, will effectively control the electric drives of exoskeletons to achieve the most comfortable movement of the user in it. The block diagram of the learning algorithm for the neural-fuzzy control module of an exoskeleton with electric drives is represented in the form (Figure 2).…”

“…When solving the problem of controlling an exoskeleton with electric drives, it is possible to use artificial neural networks of the following types [6][7][8][9][10][11][12][13][14]: networks of radial-basis structures, multilayer perceptron, adaptive fuzzy inference network. Fuzzy inference networks combine the advantages of a neural network model and fuzzy reasoning logic, which are all closer to the logical reasoning of an exoskeleton user.…”