The closed-loop human-robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot's dynamics and external load compared to the human limb free movement. The human-robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human-robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.
The use of computer controlled manufacturing systems incorporating robots and flexible control is of paramount interest today. A number of economists have noted that strength in high technology industries such as aircraft, missiles, communications, electronic and computers, in contrast to older industries, gives the economy of any country strong long-term growth prospects.
During the past two decades, the concept of a flexible, programmable automation device, which has come to be known as an “industrial robot”, has become a reality. A variety of these automation modules have been developed, offering a wide range of capabilities and application possibilities. Robots currently help weld, cast, form, assemble, machine, transfer, inspect, load and unload parts into and out of a number of differing machines and processes.
The first part of this paper analyzes the significance of FMS in industries today. The second part of the paper attempts to evaluate some of the areas of savings as a result of using robots.
Robots are coming to be regarded as tools to be used rather than as a technology to be mastered. Increasingly they are being bought as a component part in a system. Examples of robotic and flexible manufacturing systems illustrate limitations to planning, implementing and operating practical systems. Software techniques to assist engineers in these tasks are reviewed, namely for event modelling, geometric modelling, programming and planning. Cells are increasingly regarded as the primary building block in flexible automation. As greater functional capability is demanded of systems, the need for practical planning tools — which themselves rely on the availability of adequate ‘world models’ — is highlighted.
In this paper we have endeavored to present the state of the art of Automatic Plan Generation, with special emphasis on its application to Robotics, as well as some personal perspectives on the topics covered.
Thus, we begin by presenting a panoramic view of the first attempts at plan generation, but as yet, not connected with robotics. Further, a survey was made of the main problems to be met when it is intended to plan for a real executor in a real world.
Next, the case of distributed robotic systems and its implications on plan generation is examined. The question of time and its explicit representation is also looked upon.
Finally, a view on learning and specialized plan generators is also given.
The work of the Industrial Robot Design Research Group working at the University of Genoa, Italy, is summarized. The reasons for developing special-purpose CAD codes are noted, together with a discussion of the approach that has been utilized. An example procedure concerning the selection of the controlling strategies is presented, with typical output results. The integration of the robotic devices into manufacturing lines is discussed with an indication of the computational codes presently under development.
Robot manipulators have attracted considerable interest from researchers both in universities and industry during recent years. This interest covers a broad spectrum from task planning, robot language and artificial intelligence to mechanics, sensing and control. This survey paper addresses the area of robot position control and attempts to give an overview of the basic problems involved and some existing solutions.
Robot manipulators have highly nonlinear dynamics. Therefore the control of multi-link robot arms is a challenging and difficult problem. In this paper a nonlinear dynamic model is first presented for an robot arm. Then a linearized model is obtained for it. The linearized model is then considered as an large-scale system. The notion of decentralized stabilization has been introduced and applied to the robot arm. Controllers considered are pd, pid and pd plus a feedforward (inverse dynamics). Numerical simulation for decentralized control of a 3-axis puma arm will also be included.
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