When John von Neumann gave his lecture on ‘General and Logical Theory of Automata’ in 1948 his ideas were met with some scepticism. The suggestion that life is a logical process which could result in making a new kind of creature, was the forerunner of all our discussions on whether ‘artificial life’ was at all possible.

A novel composite control scheme for a manipulator with flexible links and joints is presented that uses the singular perturbation technique (SPT) to divide the manipulator dynamics into reduced order slow and fast subsystems. A neural network controller is then applied for the slow subsystem and a state-feedback H∞ controller for the fast subsystem. Results are presented that demonstrate improved performance over an alternative SPT-based controller that uses inverse dynamics and LQR controllers.

This paper presents a study of the application of adaptive and robust control methods to a cooperative manipulation system which is developed for handling an object by three dimensional revolute-jointed manipulators. The adaptive control algorithm supports the parameter adaptive law that provides guaranteed stability for uncertain systems. In designing the robust control structure, contact and friction constraints for grasp and bearing conditions, structural flexibility or such similar factors as various unmodeled dynamics are considered as uncertainties that determine available values of control parameters. The novelty of results in the present paper is to define new control inputs using parametric uncertainties and the Lyapunov based theory of guaranteed stability of uncertain systems for handling objects in a spatial workspace.

Due to the demands from the robotic industry, robot structures have evolved from serial to parallel. The control of parallel robots for high performance and high speed tasks has always been a challenge to control engineers. Following traditional control engineering approaches, it is possible to design advanced algorithms for parallel robot control. These approaches, however, may encounter problems such as heavy computational load and modeling errors, to name it a few. To avoid heavy computation, simplified dynamic models can be obtained by applying approximation techniques, nevertheless, performance accuracy will suffer due to modeling errors. This paper suggests applying an integrated design and control approach, i.e., the Design For Control (DFC) approach, to handle this problem. The underlying idea of the DFC approach can be illustrated as follows: Intuitively, a simple control algorithm can control a structure with a simple dynamic model quite well. Therefore, no matter how sophisticate a desired motion task is, if the mechanical structure is designed such that it results in a simple dynamic model, then, to design a controller for this system will not be a difficult issue. As such, complicated control design can be avoided, on-line computation load can be reduced and better control performance can be achieved. Through out the discussion in the paper, a 2 DOF parallel robot is redesigned based on the DFC concept in order to obtain a simpler dynamic model based on a mass-balancing method. Then a simple PD controller can drive the robot to achieve accurate point-to-point tracking tasks. Theoretical analysis has proven that the simple PD control can guarantee a stable system. Experimental results have successfully demonstrated the effectiveness of this integrated design and control approach.

This paper presents the design of a new low-cost, easy operation hand having three 1-dof anthropomorphic fingers of a human size. Experimental results on human cylindrical grasping have been used for designing a 1-dof anthropomorphic finger of a human size. The mechanical design of this finger has been mainly focused on a suitable articulated mechanism. This mechanism transmits the power to the phalanxes for a human-like grasping motion and it can be embedded in the body of the finger itself. The design of a three finger hand has been presented as based on the designed finger module. Experimental tests on a built hand prototype have been carried out in order to show the feasibility of the proposed design and validate its operation.

In this article, we present a learning model that can control the kinematics motion of a simulated anthropomorphic arm in reaching and grasping tasks of a static prototypic object placed behind an obstacle of varying position and size. The network, composed of two generic neural network modules, learns to combine multi-modal arm-related information (trajectory parameters) as well as obstacle-related information (obstacle size and location). We based our simulation on the Via Point notion, which postulates that the reach motion planning is divided into successive positions of the arm. In order to determine these particular positions, some specific parameters have been extracted from an experimental protocol and constitute the pertinent parameters to be integrated into the model. This net of neural net determines the total path able to reach and grasp the prototypic object while avoiding an obstacle.

In this paper, an adaptive control scheme is proposed for an n-link rigid robot manipulator without using the regressor. The robot is firstly modeled as a set of second-order nonlinear differential equations with the assumption that all of the matrices in that model are unavailable. Since these matrices are time-varying and their variation bounds are not given, traditional adaptive or robust designs do not apply. The function approximation technique (FAT) is used here to represent uncertainties in some finite linear combinations of orthonormal basis. The dynamics of the output tracking can thus be proved to be a stable first order filter driven by function approximation errors. Using the Lyapunov stability theory, a set of update laws is derived to give closed loop stability with proper tracking performance. Experiments are also performed on a 2-D robot to test the efficacy of the proposed scheme.

In the paper we address a problem of controlling an oscilmotion with a robot. As the object we have selected a yo-yo. First we have measured and analysed the motion of different yo-yos. We have developed a simplified model of a yo-yo which has one degree-of-freedom, and the behaviour at the end of the string is modelled as an impact. Next, we discuss the control strategy. Our results show, that for playing a yo-yo it is important to start the upward motion before the yo-yo reaches the bottom position and the acceleration has to be reversed after the bottom impact. We present two control strategies: one based on predefined hand motion pattern and and the other generating the hand motion on-line. Both allow playing the yo-yo at a selected top height. The theoretical results have been proven by experiments on a real robot system.

In this article, impedance control of a two link flexible link manipulators is addressed. The concept of impedance control of flexible link robots is rather new and is being addressed for the first time by the authors. Impedance Control provides a universal approach to the control of flexible robots, in both constrained and unconstrained maneuvers. The initial part of the paper concerns the use of Hamilton's principle to derive the mathematical equations governing the dynamics of joint angles, vibration of the flexible links and the constraining forces. The approximate elastic deformations are then derived by means of the Assumed-Mode-Method (AMM). Using the singular perturbation method, the dynamic of the manipulator is decomposed into fast and slow subsystems. The slow dynamic corresponds to the rigid manipulator and the fast dynamic is due to vibrations of flexible links. The sliding mode control (SMC) theory has been used as the means to achieve the 2nd order target impedance for the slow dynamics. A controller based on state feedback is also designed to stabilize the fast dynamics. The composite controller is constructed by using the slow and fast controllers. Simulation results for a 2-DOF robot in which only the 2nd link is flexible confirm that the controller performs remarkably well under various simulation conditions.

This paper discusses the modeling and control of a robotic manipulator with a new deburring tool, which integrates two pneumatic actuators to take advantage of a double cutting action. A coordination control method is developed by decomposing the robotic deburring system into two subsystems; the arm and the deburring tool. A decentralized control approach is pursued, in which suitable controllers were designed for the two subsystems in the coordination scheme. In simulation, three different tool configurations are considered: rigid, single pneumatic and integrated pneumatic tools. A comparative study is performed to investigate the deburring performance of the deburring arm with the different tools. Simulation results show that the developed robotic deburring system significantly improves the accuracy of the deburring operation.

In this paper, an optimal kinematic design method of a three translational DoFs parallel manipulator is presented. The design is based on the concept of performance chart, which can show the relationship between a criterion and design parameters graphically and globally. The normalization on the design parameters of the studied manipulator makes it possible that the design space, which is made up of the normalized parameters, is limited. The design space includes of all possible basic similarity manipulators (BSMs). As any one of the BSMs represents all of its similarity manipulators (SMs) in terms of performances, if one BSM is optimal, its SMs are optimized as well. The said optimal BSM is from the optimum region, which is the intersecting result of involved performance charts. In this paper, the related performance criteria are good-conditioning workspace (GCW), global conditioning index (GCI) and global stiffness index (GSI). As an applying example, a design result of the parallel manipulator with a desired task workspace is presented. The results of the paper are very useful for the design and application of a parallel manipulator.

This paper presents a recursive approach for solving kinematic and dynamic problems in snake-like robots using Kane's equations. An n-link model with n-nonholonomic constraints is used as the snake robot model in our analysis. The proposed algorithm which is used to derive kinematic and dynamic equations recursively, enhances the computational efficiency of our analysis. Using this method we can determine the number of additions and multiplications as a function of n. The proposed method is compared with the Lagrange and Newton-Euler's method in three different aspects: Number of operations, CPU time and error in the computational procedures.

Our goal is to develop biped humanoid robots capable of working stably in a human living and working space, with a focus on their physical construction and motion control. At the first stage, we have developed a human-like biped robot, WABIAN (WAseda BIped humANoid), which has a thirty-five mechanical degrees of freedom. Its height is 1.66 [m] and its weight 107.4 [kg]. In this paper, a moment compensation method is described for stability, which is based on the motion of its head, legs and arms. Also, a follow walking method is proposed which is based on a pattern switching technique. By a combination of both methods, the biped robot is able to perform dynamic stamping, walking forward and backward in a continuous time while someone is pushing or pulling its hand in such a way. Using WABIAN, human-fellow walking experiments are conducted, and the effectiveness of the methods are verified.

This paper studies crab gaits and turning gaits of a hexapod robot with a locked joint failure. Due to the reduced workspace of a failed leg, fault-tolerant gaits have limitations in their mobility. Based on the principles of fault-tolerant gait planning, periodic crab gaits and turning gaits are proposed in which a hexapod robot carries out tripod walking after a locked joint failure, having a reasonable stride length and stability margin.