Application of Active Force Control and Iterative Learning in a 5-Link Biped Robot

This paper investigates the efficacy of the implementation of the conventional Proportional-Derivative (PD) controller and different Active Force Control (AFC) strategies to a 5-link biped robot through a series of simulation studies. The performance of the biped system is evaluated by making the biped walk on a horizontal flat surface, in which the locomotion is constrained within the sagittal plane. Initially, a classical PD controller has been used to control the biped robot. Then, a disturbance elimination method called Active Force Control (AFC) schemes has been incorporated. The effectiveness and robustness of the AFC as disturbance rejecter has been examined when a conventional crude approximation (AFCCA), and an intelligent active force control scheme, which is known as Active Force Control and Iterative Learning (AFCAIL) are employed. It is found that for both of the AFC control schemes proposed, the system is robust and stable even under the influence of disturbances. An attractive feature of the AFCAIL scheme is that inertia matrix tuning becomes much easier and automatic without any degradation in the performance.     

Decentralized adaptive controller design of large-scale uncertain robotic systems

In this paper, we develop a decentralized neural network control design for robotic systems. Using this design, it is not necessary to derive the robotic dynamical system (robotic model) for the control of each of the robotic components, as in traditional robot control. The advantage of the proposed neural network controller is that, under a mild assumption, unknown nonlinear dynamics such as inertia matrix and Coriolis/centripetal matrix and friction, as well as interconnections with arbitrary nonlinear bounds can be accommodated with on-line learning.     

A Gesture Based Interface for Human-Robot Interaction

Service robotics is currently a highly active research area in robotics, with enormous societal potential. Since service robots directly interact with people, finding natural and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such as navigation and manipulation, relatively few robotic systems are equipped with flexible user interfaces that permit controlling the robot by natural means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through office environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures defined through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned and instructs the robot to pick up trash.     

Visual grasping with long delay time of a free floating object in orbit

In the US-German Spacelab mission D2 (April/May 1993) a new level of automation capabilities has been achieved with the ROTEX-freeflyer-experiment of DLR. For the first time, a combined human/robotic task force on the ground succeeded in visually controlled tele-grasping of a free floating object on board the Space Shuttle Columbia within a working cell of the German Spacelab by a remotely controlled robot arm. The contributions of UniBwM in the fields of monocular motion-stereo vision, state prediction and fully automatic grasping under long delay times (5 to 7 seconds) are discussed in detail.The successful team jointly consisted of scientists from DLR and UniBwM for decision taking and remote manual control as well as a network of computers representing a robot specialist for visual motion interpretation and prediction including the expertise for delayed feedback, which is hard for humans.     

Preliminary Design and Feasibility Study of a 6-Degree of Freedom Robot for Excavation of Unexploded Landmine

At present, the Sensing and Access Control R&D for Humanitarian Mine Action project has been carrying out by the Japan Science and Technology Agency (JST). Chiba Universitys group join to this project. Our effort is to develop a small vehicle for mine detection and clearance. Specifically we are concerned with the development of a 6 degree-of-freedom robot arm for exposing buried anti-personnel mines, with plans of field-testing in Afghanistan in 2005. This paper describes the current state of development in the anti-personnel mine exposure and clearance system, focusing on the robot arm.     

Human direct teaching of industrial articulated robot arms based on force-free control

Force-free control produces motion in a robot arm as if it were under conditions with no gravity and no friction. In this study, a method of force-free control is proposed for industrial articulated robot arms. The force-free control proposed was applied to the direct teaching of industrial articulated robot arms in that the robot arm was moved by direct human force. Generally, the teaching of industrial articulated robot arms is carried out using operational equipment called a teach-pendant. Smooth teaching can be achieved if direct teaching is applicable. The force-free control proposed enables humans to teach industrial articulated robot arms directly. The effectiveness of force-free control was confirmed by experimental work on an articulated robot arm with two degrees of freedom. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Scheduling in Reentrant Robotic Cells: Algorithms and Complexity

We study the scheduling of m-machine reentrant robotic cells, in which parts need to reenter machines several times before they are finished. The problem is to find the sequence of 1-unit robot move cycles and the part processing sequence which jointly minimize the cycle time or the makespan. When m = 2, we show that both the cycle time and the makespan minimization problems are polynomially solvable. When m = 3, we examine a special class of reentrant robotic cells with the cycle time objective. We show that in a three-machine loop-reentrant robotic cell, the part sequencing problem under three out of the four possible robot move cycles for producing one unit is strongly -hard. The part sequencing problem under the remaining robot move cycle can be solved easily. Finally, we prove that the general problem, without restriction to any robot move cycle, is also intractable.     

Robust Backstepping Control of Robotic Systems Using Neural Networks

Neural network (NN) controllers for the robust back stepping control of robotic systems in both continuous and discrete-time are presented. Control action is employed to achieve tracking performance for unknown nonlinear system. Tuning methods are derived for the NN based on delta rule. Novel weight tuning algorithms for the NN are obtained that are similar to -modification in the case of continuous-time adaptive control. Uniform ultimate boundedness of the tracking error and the weight estimates are presented without using the persistency of excitation (PE) condition. Certainty equivalence is not used and regression matrix is not computed. No learning phase is needed for the NN and initialization of the network weights is straightforward. Simulation results justify the theoretical conclusions.     

Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.     

Controlling the Execution of a Visual Servoing Task

This paper presents the application of the Visual Servoing approach to a mobile robot which must execute coordinate motions in a known indoor environment. In this work, we are interested in the execution and control of basic motions like Go to an object by using the mobile robot H^il are2Bis. We use a diagonal matrix for the gain to improve the visual servoing behaviour and the potential field formalism to avoid obstacles. Namely, the robot is controlled according to the position of some features in an image. Such a path will be executed by a nonholonomic mobile robot, which has only two degrees of freedom (two wheels), and three configuration parameters (X Y ); a camera is mounted on the robot close to the end effector of an arm, controlled to add at least a new degree of freedom (_pl).