Synchronization control for physics-based collaborative virtual environments with shared haptics

In this paper, we propose a synchronization scheme to achieve a high level of consistency in peer-to-peer-based shared virtual environments (SVEs), as well as to display natural and realistic motions of virtual objects, in collaborative haptic tasks. The synchronization scheme utilizes an advanced feedback controller to compensate for the state error between geographically separated sites with a significant amount of time delay. It is designed using the mathematical model of a two-user SVE manipulating a freely moving object represented as a mass with damping resistance, with a haptic interface. Thanks to feedback control theory of time delay systems, the controller is shown to result in closed-loop stability and is be robust to perturbations in the time delay. Together with the synchronization control, a recovery filter is also designed and integrated so as to preserve the natural behavior of the synchronized object, which is, otherwise, affected by the feedback control action. In addition to verifying the theoretical results, two experiments using real Internet and local area network communications are carried out. These tests clearly support the validity of the analyses and demonstrate the applicability of the synchronization scheme.     

Sonar-based guidance of unmanned underwater vehicles

This paper addresses the problem of the design and coordination of guidance and sonarbased motion estimation algorithms for unmanned underwater vehicles. In the framework of a two-layered hierarchical architecture uncoupling the system's dynamics and kinematics, a couple of guidance laws for approaching a target with the desired orientation and following an environmental feature have been designed with Lyapunov-based techniques. Suitable acoustic-based estimators of the corresponding operational variables have been designed and integrated with the guidance and control system. A finite state machine combined with a suitable interface for event generation allows the coordinated execution of basic guidance and motion estimation tasks to carry out more complex functions. Experimental results of pool trials of a prototype unmanned underwater vehicle executing free-space maneuvering, wall-following tasks and the more complex mission of following the perimeter of the trial pool are reported and discussed.     

Design of robot assistance for arm movement therapy following stroke

This paper describes the mechanical and control design of a robotic device for providing therapeutic assistance to arm movement following stroke. The device uses a single motor and a passively oriented linear constraint to allow patients to reach across their workspace. Experimental evaluation of two controllers for assisting in reaching is presented.     

High-speed visual servoing of a 6-d.o.f. manipulator using multivariable predictive control

This paper presents a new approach to model and control high-speed 6-d.o.f. visual servo loops. The modeling and control strategy take into account the dynamics of the velocity-controlled 6-d.o.f. manipulator as well as a simplified model of the camera and acquisition system in order to significantly increase the bandwidth of the servo loop. Multi-input multi-output generalized predictive control (GPC) is used to optimally control the visual loop with respect to the proposed dynamical model. The predictive feature of the GPC is used for optimal trajectory following in Cartesian space. Experimental results on a 6-d.o.f. industrial robot are presented that validate the proposed model. The visual sensor used in the experiments is a high-speed camera that acquires 120 non-interlaced images. Using this camera, a sampling rate of 120 Hz is achieved for the visual loop. Furthermore, a precise synchronization method is used to reduce the delays due to image transfer and processing. The experiments show a drastic improvement of the loop performance with respect to more classical control strategies for 6-d.o.f. visual servo loops.     

Combined direct and indirect adaptive control of robot manipulators using multiple models

A novel methodology is proposed for the adaptive control of rigid robotic manipulators. The proposed method utilizes multiple adaptive models for the identification and control of the manipulator. The present study is an extension of our previous work which utilized an indirect adaptive control approach with multiple models for better transient performance. The proposed scheme uses a composite approach where both prediction and tracking errors are used in a combined direct and indirect adaptive control framework. Simulation results are given to demonstrate the efficient use of the methodology.     

Stability analysis and robust control for fixed-scale teleoperation

In this article we present a new theorem that reduces the stability problem of teleoperators from a structured singular value problem to a maximum singular value problem. A control scheme realizing an ideal response for fixed-scale teleoperation is proposed and its stability is proven analytically. Next, the ideal control scheme is extended to control schemes with a higher stability robustness. Also, for these more complicated schemes, stability analyses are performed making use of the new theorem and stability conditions are derived. Experiments on a 1-d.o.f. setup are conducted to confirm the validity of the proposed control schemes.     

Unified nonsingular tracking and stabilization controller design for unicycle-type wheeled mobile robots

A unified, singularity-avoidant controller which enables simultaneous trajectory tracking and posture stabilization of unicycle-type wheeled mobile robots is proposed. The design scheme is based on phase portrait analysis, dynamic feedback linearization and sliding mode control. Path planning via phase portrait analysis plays a key role in choosing the control parameters and the initial value of the extended state in avoiding any singularity. Simulation results on posture stabilization as well as an eight-shaped trajectory tracking are presented to demonstrate the performance of the proposed controller.     

An Optimal Control-Based Formulation to Determine Natural Locomotor Paths for Humanoid Robots

In this paper we explore the underlying principles of natural locomotion path generation of human beings. The knowledge of these principles is useful to implement biologically inspired path planning algorithms on a humanoid robot. By 'locomotion path' we denote the motion of the robot as a whole in the plane. The key to our approach is to formulate the path planning problem as an optimal control problem. We propose a single dynamic model valid for all situations, which includes both non-holonomic and holonomic modes of locomotion, as well as an appropriately designed unified objective function. The choice between holonomic and non-holonomic behavior is not accomplished by a switching model, but it appears in a smooth way, along with the optimal path, as a result of the optimization by efficient numerical techniques. The proposed model and objective function are successfully tested in six different locomotion scenarios. The resulting paths are implemented on the HRP-2 robot in the simulation environment OpenHRP as well as in the experiment on the real robot.     

Adaptive controller for non-holonomic mobile robots with matched uncertainties

This paper deals with the backstepping approach for the design of adaptive discontinuous time-invariant controllers for the point-stabilization of mobile robots with matched uncertainties. First of all, we derive a control law in the disturbance-free case guaranteeing exponential convergence for a unicycle-like mobile robot. Furthermore, an adaptive version of the previous control law is proposed when the mobile robot is subjected to input disturbances. Finally, simulation results are presented.