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Robotic telesurgery has gained a lot of interest in the medical and engineering field with the advancement of technology over in Minimum Invasive Surgery (MIS) that uses small incisions to operate resulting in the faster recovery of the patients. In the commercially available surgical systems, the surgeon cannot feel the sense of touch of the environment, unlike open type surgery. The control becomes harder when the robotic surgery is performed remotely in the presence of time delays. The development of the force feedback will help to reduce the tissue damage and end-effector deflection. The primary goal of this work is to develop the force feedback for the surgical arms affected by the time delays. The random behavior of the network delays makes the control realization even harder and can make the response go unstable. A novel approach is used to develop the haptic feedback for the Minimally Invasive Robotic Systems (MIRS) by using Model Predictive Control (MPC) in case of random communication delays.

There is a growing need for space and in-orbit operations that would require use of advance robotic systems. The robotics systems could be used in debris removal from orbits, as well as, in on-orbit servicing activities. This paper is addressing design and control problems related to autonomous spacecraft-manipulator system for space operation. The dynamics equations for rotation floating manipulator were introduced using Lagrange approach with additional states representing the kinetic moment exchange actuators. In this paper, a serial-link manipulator with multi degree of freedoms mounted on the satellite platform was used. For detailed analysis of base motion and manipulability of the end effector, the special indices were introduced. Simulation examples to illustrate kinematic indices were shown with physical parameters for a microsatellite from Myriade series equipped with a robotic arm.

Manipulators are widely used in orbital operations, for example, Mobile Servicing System on the International Space Station and Shuttle Remote Manipulator System are used for several years. Such manipulators are operated by astronauts and mounted on large platforms, thus influence of manipulator motion on the state of the platform is not significant. Application of manipulators for capture maneuvers in unmanned On-Orbit Servicing or Active Debris Removal missions requires reliable control algorithms that take into account the free-floating nature of the manipulator-equipped spacecraft. In this paper the possibility of using Nonlinear Model Predictive Control (NMPC) for controlling such manipulators is presented. Numerical simulations for a simplified planar case show effectiveness of the proposed controller.

This paper presents a novel approach to control of a flexible wing UAV using vision system. The aim of this paper is to develop a new technique to measure and classify the wing shape to control the airspeed, angle of attack (AOA), and the roll angle using a stereo camera. The deflection of the wing was extracted from experimental testing, and Fuzzy Logic was used to classify these shapes of the wing. Two different controllers were used: Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Linear Quadratic Regulator (LQR). The deflections of wing were measured for chosen locations of landmarks on a flexible wing using a stereo camera. The camera was placed at the rear end of the wing structure, and the entire wing was in the view. The stereo camera was used to study the characteristics of the flexible wings in wind tunnel testing at wind speeds ranging from 11 to 31 km/h, angles of attack ranging from −20 to 20 degree and roll angles ranging from −10 to 10 degree. In addition, fuzzy logic control was used to classify the shape of wing based on the deflection values. This algorithm determines the flight characteristics such as the airspeed, angle of attack and roll angle. Furthermore, two modules of a controller were designed for achieving the desired performance. Simulation and experiment results have been presented and verified.

This paper examines accurate image depth determination to achieve accurate navigation. Accurate navigation is critical for Unmanned Aerial Vehicle or UAV aerial refueling and, also, space debris clearance operations. At this point it is not the intention to generate a definitive depth performance study, rather the intention is to gain an understanding of the kind of depth performance which might be possible for a small UAV or space-based robot flying close in and far away, at depths of 2 and 20 m, for example. The theory behind image depth calculation is explained and then synthetic pixel data are manufactured to determine a 95% confidence interval on image depth under various errors conditions. It is shown that without image rectification and no knowledge of camera rotation, accurate depth can only be calculated for stereo cameras pointing without any axial rotations. With image rectification and under the stated conditions, image depth can be calculated with 8.2% or less absolute relative error. Future research is discussed.

This paper present and discusses algorithms suitable for visual navigation for mobile and flying robots. Three different algorithms were used to explore the direct method for the vision system. Those methods are Homography, Iterative Closest Point (ICP), and Horn’s Absolute Orientation. Those algorithms were tested on the camera with moving baseline. The relations between optimal baseline and depth distance were discussed. The camera’s calibration process has been presented and discussed. Several experiments with the different image noise level were performed. The noise levels influence on distance and pose estimation accuracy were discussed. Measurements and estimation errors for both mobile and flying robots were shown and compared with different methods.

The optimal performance of the conventional Kalman filters is not guaranteed, when there is uncertainty in the process and measurement noise covariances. In this paper, in order to reduce the effect of noise covariance uncertainty, the Fuzzy Adaptive Iterated Extended Kalman Filter (FAIEKF) and Fuzzy Adaptive Unscented Kalman Filter (FAUKF) are proposed to overcome this drawback. The proposed FAIEKF and FAUKF have been applied to fuse signals from Global Positioning System (GPS) and Inertial Navigation Systems (INS) for the autonomous vehicles’ navigation. In order to validate the accuracy and convergence of the proposed approaches, results obtained by FAUKF and FAIEKF were compared to the Fuzzy Adaptive Extended Kalman Filter (FAEKF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Iterated Extended Kalman Filter (IEKF). The simulation results illustrate the superior performance of the AKUKF compared to the other filters.

This paper addresses the problem of adaptive trajectory tracking control of space manipulators that exhibit elastic vibrations in their joints, and are subject to parametric uncertainties. The proposed composite control methodology combines a decentralized simple adaptive control-based term to stabilize the rigid dynamics with a linear correction term to improve vibration damping at the joints. In particular, this paper illustrates how the injection of knowledge about the reference model via the use of feedforward terms in the adaptive control structure yields improved tracking results. Simulation results demonstrate reduced overshoots when tracking a square trajectory by the end-effector of a two-link manipulator under parametric uncertainties, in comparison with an existing composite adaptive controller without feedforward terms.

Simultaneous localization and mapping is a situation in which a mobile robot travels through an environment and concurrently makes a momentary map of the environment and uses that map to localize. The simultaneous localization and mapping is currently one of the most challenging problems in the field of autonomous mobile robots and providing a solution to SLAM may open doors to the world of truly autonomous robots. This paper provides a novel approach to Simultaneous Localization and Mapping problem based on estimation approach. The new approach is called Unscented HybridSLAM filter which addresses the linearization process of an autonomous mobile robot utilizing the second order Sterling polynomial interpolation specifically used for Unscented HybridSLAM algorithm. Using computer simulations, Unscented HybridSLAM and the associated theoretical interpolation is examined for a double-loop scenario and the efficacy of the Unscented HybridSLAM is validated.