Robots are still outperforming humans regarding their efficiency and dexterity. On the other hand, however, muscles are more efficient and have a higher power to weight ratio than electric motors. As a consequence, among others, manipulators for human robot interaction in industry or service applications have a low payload to weight ratio and a low energy efficiency. Therefore, we developed a n...

In this chapter, a Recurrent Higher Order Neural Network (RHONN) is used to identify the plant model of discrete time nonlinear systems, under the assumption that all the state is available for measurement. Then the Extended Kalman Filter (EKF) is used to train the RHONN. The applicability of this scheme is illustrated by identification for an electrically driven nonholonomic mobile robot. Trad...

The purpose of this paper is to describe the design, development and simulation of a real time controller for an intelligent, vision guided robot. The use of a creative controller that can select its own tasks is demonstrated. This creative controller uses a task control center and dynamic database. The dynamic database stores both global environmental information and local information includin...

We propose an automatically deployable robust control scheme for modular reconfigurable robot manipulators, which accounts noisy velocity measurements, yet it maximizes the tracking performance while avoiding chattering effects. Our proposed approach adapts to any of possible compositions given sets modules. The stability is guaranteed by exploiting use a recursive Newton-Euler with interval ar...

Fault tolerance is increasingly important in modern industrial robotic manipulators, especially those operated in remote and hazardous environment. Faults in robotic manipulator can cause economic and serious damages. So the robots need the ability to detect as well as tolerate failures, allow effectively coping with internal failures and continue performing designated tasks without the need fo...