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On-orbit servicing, active debris removal or assembling large structures on orbit are only some of the tasks that could be accomplished by space robots. In all these cases, a contact between a space robot and the satellite being serviced, deorbited, or assembled will occur. This contact results in a contact force exerted on the space robot, and therefore momenta of the space robot system are no longer conserved. Most of the papers that are concerned with motion planning problems of a space robot manipulator either consider that no external forces or moments are acting on the space robot system or use additional controllers when the space robot is subjected to external forces and moments. Such a controller minimizes end-effector position and orientation errors caused by the changes in system momenta due to external forces and moments acting on this system. The novelty of this work is that it proposes a new method for planning the motion of dual-arm space robot manipulators when linear and angular momenta of the space robot system are not conserved due to external forces and moments acting on the space robot base or/and manipulators’ end-effectors. In the proposed method the changes in system momenta are considered, but no additional controllers are needed. In this paper, we derive the motion planning equations for dual-arm space robot manipulators, where external forces and moments are acting on both satellite and manipulator end-effectors. The proposed method has been verified by numerical simulations, and the results are presented and discussed.

The system presented in this paper integrates measurements from Deflection-Detection-Vision-System (DDVS) and strain gauge sensors to calculate the dynamic performance of the unmanned aerial vehicle (UAV) fixed-wing. The fusion results are more accurate and reliable than the measurement from the single sensor alone, and this is used to design an advanced control system. Unscented Kalmar Filter (UKF) is the choice of fusion method because it gives better performance in dealing with nonlinear systems and deals with white and color Gaussian noise. The presented novel measurement and control system gives accurate shape and deflection of the fixed-wing, which will be used to calculate the aerodynamic performance. The DDVS was verified in extensive experiments with the system installed in the wind tunnel. The objective of experiments conducted was to determine the wing's deflection in flight parameters' function. The results of the camera and strain gauges integration resulted in accurate measurements in comparison with results obtained from one sensor only.

Through the use of telerobotics, surgeons can perform operations on patients from a distance, via a communication network. However, a major issue to these procedures is the existence and effect of time delays, which are known to negatively affect performance. In addition, the forces that occur due to tool-tissue interaction may also present problems during operations. This paper investigates and compares the use of Proportional-Integral-Derivative (PID) controllers and Model Predictive Controllers (MPCs) used in hybrid control, and their effects on the performance of the da Vinci™Patient Side Manipulator (PSM), a surgical robot, when subjected to different time delays. It was found that hybrid MPCs have better trajectory control, however hybrid PID controllers exhibit greater ability to adapt to undesirable external forces from contact. Further research into implementing a predictor with the PID controllers and comparing results, as well as integrating benefits of both controllers into a single hybrid control would provide great benefits for improving surgical telerobotic performance.

PurposeThis paper aims to present a novel method for identification and classification of rotational motion for uncontrolled satellites. These processes are shown in context of close proximity orbital operations. In particular, it includes a manipulator arm mounted on chaser satellite and used to capture target satellites. In such situations, a precise extrapolation of the target’s docking port position is needed to determine the manipulator arm motion. The outcome of this analysis might be used in future debris removal or servicing space missions.Design/methodology/approachNonlinear, and in some special cases, chaotic nature of satellite rotational motion was considered. Four parameters were defined: range of motion toward docking port, dominant frequencies, fractal dimension of the motion and its time dependencies.FindingsThe qualitative analysis was performed for presented cases of spacecraft rotational motion and for each case the respective parameters were calculated. The analysis shows that it is possible to detect the type of rotational motion.Originality/valueA novel procedure allowing to estimate the type of satellite rotational motion based on fractal approach was proposed.

This paper presented a new method to guide and control a space robot for capturing an uncooperative target. The dynamic model of a target is unknown and estimated with the help of vision system. This methodology has three different steps. First, the feature points of a space target were extracted using the vision system, then the pose of the target (position and orientation) relative to the space robot was determined based on Homography method. Second, because of an unknown model of the target, the location of the center of mass is calculated using kinematic equations and Iterative Closest Point (ICP) algorithm. This would help tracking moving target. Third, a new Adaptive Unscented Kalman Filter (AUKF) was introduced to estimate the dynamic state vector (position, orientation, linear and angular velocities) of an arbitrary space target. The error in AUKF estimation was prevented from divergence by using Fuzzy Logic Adaptive System (FLAS). Finally, a new trajectory method for planning the end-effector velocities of the space robot arm was implemented based on the measurement information from the vision system and estimation a target state using AUKF. The results from simulation experiments were presented and discussed.

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.

Minimum invasive robotic surgery is one of the most desired applications required with the advancement of medical technologies. Surgeons rely on the visual feedback solely from the laparoscopic camera to get the information about the operation to make further decisions. The control gets harder due to the presence of the latency in the network that can make the system unstable. The synchronization between the hand to eye of the surgeon is affected due to random behavior of the latency. In this paper, a novel approach is proposed by using the model predictive control to develop a predictive imaging technique that will provide the surgeon simplified imaging during the condition when delayed perception could occur. The ability of the model predictive control to predict the future will help to handle the randomness of the latency providing a better hand to eye coordination to the surgeon. A comparison is provided with the current techniques showing the effectiveness of the suggested approach.

This paper examines the effect on achievable depth accuracy of a stereo vision system as the baseline between the two camera sensors changes. This is critical for Unmanned Aerial Vehicle navigation or UAV aerial refueling, and for space debris clearance operations. The theory behind stereo image depth calculation is explained and then synthetic pixel data is manufactured in order to determine a 95% confidence interval on depth under two camera baseline conditions. A Gaussian pixel error is add to simulate Harris corner detection error. A disparity of the order of 10 pixels or less produces more than 1 cm difference between expected and actual depth for the stereo camera bases examined. For a 1-pixel disparity the difference is of the order of 50%. Future research is discussed.

Although concept of fractional calculus was known for centuries, it was not considered in engineering due to the satisfactory performance of integer order models and control. However, recently, engineers and researchers started to investigate the potentially high performance of fractional calculus in various fields including control theory. This article analyzes the relative performance of fractional versus integer order PID for the quadcopter. After introducing fractional calculus and the mathematical tools which have been used, the dynamics of the quadcopter is presented with additional consideration of the ground effect and torque saturation and sensor measurement. The performance of fractional order controller is evaluated by comparing it to an integer order controller.

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 paper compares control strategies for four flexible space robot manipulators including the classical Slotine and Li algorithm, a simple proportional derivative controller, a singular perturbation-based controller and a nonlinear backstepping controller. All strategies are tested and compared in simulations involving endpoint target positioning while tracking a square trajectory by a two-link flexible joint space robot. Simulation results indicate that controlling both nonlinearities and joint flexibility effects improve the closed-loop behavior of the space robot where the control of nonlinearities is of greater importance. The best performance is achieved by the nonlinear backstepping control strategy. We also propose Hammerstein nonlinear joint model and introduce its nonparametric nearest neighbor kernel identification algorithm and study its convergence.

This paper presents a new methodology to guide and control flexible wings UAV based on a stereo vision system with advanced fuzzy logic algorithms. This system allows to detect wing's deflections and shapes by using the novel Deflection-Detection Vision System (DDVS). This methodology has three different steps. First, the shape of the wing was identified by the determinant the deflection points on the wing using the stereo camera. Second, the fuzzy logic algorithm was used to classify the shapes of the wing and to determine the flight parameters such as, speed, angle of attack and roll angle. Finally, an autopilot controller based on intelligent, Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm was designed. Extensive, experimental studies were performed in wind tunnel to determine the characteristics of the flexible wing. The testing were performed for wind speeds ranging from 11 to 31 km/h, angles of attack ranging from −20 deg to +20 deg. The experimental results were extended and verified with simulation experiments for wider ranges of velocities and angle of attack.

Robots mounted on an unmanned chaser satellite could be used for performing rendezvous and grasping manoeuvres in order to repair satellites or remove space debris from orbit. Use of manipulators for such purposes is challenging, since the performed task need to be done autonomously, accurately and with high level of robustness. During manoeuvres high disturbances might appear e.g. due to contact between the manipulator arm end-effector and the target spacecraft. In this paper an approach for validation of the robotic subsystem during chaser rendezvous and grasping manoeuvre has been shown. Two type of testbed systems were used: planar air-bearing microgravity simulators and a test-bed system with industrial robots. The proposed approach took advantage of possible replacement of particular subsystem in reference model or reference hardware in specified test-bed system. The list of tests performed are included in the paper.

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.

Operational problems with robots in space relate to several factors. One of the most important factors is the elastic vibrations in the joints. In this paper, control strategies for endpoint tracki...

This paper demonstrates a haptic device for interaction with a virtual environment. The force control is added by visual feedback that makes the system more responsive and accurate. There are two popular control methods widely used in haptic controller design. First, is impedance control when user motion input is measured, and then, the reaction force is fed back to the operator. The alternative method is admittance control, when forces exerted by user are measured and motion is fed back to the user. Both, impedance and admittance control are also basic ways for interacting with a virtual environment. In this paper, several experiments were performed to evaluate the suitability of force-impedance control for haptic interface development. The difference between conventional application of impedance control in robot motion control and its application in haptic interface development is investigated. Open loop impedance control methodology is implemented for static case and a general-purpose robot under open loop impedance control was developed as a haptic device, while a closed loop model based impedance control was used for haptic controller design in both static and dynamic case. The factors that could affect to the performance of a haptic interface are also investigated experimentally using parametric studies. Experimental results for 1 DOF rotational motion and 2 DOF planar translational motion systems are presented. The results show that the impedance control aided by visual feedback broaden the applicability of the haptic device and makes the system more responsive and accurate.

In this paper, a vision-based relative navigation strategy applicable for an unknown and spinning target object in space is presented. Specifically, the relative navigation system uses a calibrated stereo monochrome camera, and is developed for estimating the relative position, linear velocity and angular velocity between the inspector spacecraft and the unknown target object, while minimizing the computational requirements. Experimental results in a dark room with a scaled model of a spacecraft spinning about its major axis of rotation are reported to demonstrate the performance and applicability of the navigation system for small inspection spacecraft.

Autonomous robots are required to avoid the obstacles during navigation. For this purpose unknown and unexpected obstacles have to be detected during motion. The proposed approach uses particle filters to process sensors data and estimate relative position of the robot with regard to the obstacles and to the goal. These relative position estimations are inputs to the velocity potential field approach for obtaining time varying velocity commands for the robot to avoid all obstacles and reach the goal.

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.

Tracking of a square trajectory 12.6 m x 12.6 m by a two-link flexible robot manipulator is performed repetitively for both inverse dynamics control (IDC) and fuzzy logic control (FLC). Repetitive learning inverse dynamics control (RLIDC) achieves no improvement in tracking but repetitive learning fuzzy logic control (RLFLC) achieves greater precision where cyclic tracking enables the fuzzy inference system to self-adapt and further reduce tracking errors.

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.

The aim of this investigation is to nd a control for spacecraft rendezvous maneuver in the known elliptical orbit. Model Predictive Controller is used to calculate control for nonlinear system described by equations of spacecraft relative motion. For comparison purpose, model based on Gaussian Variational Equations is introduced. It gives possibility to assess, which type of model is more suitable for rendezvous problems with highly elliptical orbits and relatively large separation between spacecrafts. Variable-mass deputy vehicle is considered. MPC problem includes constraints on control signals, nal state and amount of used propellant.

Formations required to move to given destinations can be controlled to arrive to a predefined geometric shape or to self-organize. For a large number of autonomous mobile robots, geometric formations pose too many constraints to achieving inter-robot positions. Self-organizing formations are more suitable for a large number of robots and generate formation shapes while avoiding robots collisions. In this paper are investigated approaches for self-organizing formations of large number of robots. The approach, inspired by hydrodynamic flow, results in an artificial velocity field approach that avoids local minima due to a combination of normal and tangential velocity commands associated to obstacles. These commands, for the formation of a large number of robots that are all subject to a velocity vector command to move to the goal, avoid inter robots collisions. In this paper is investigated the efficiency of this control approach in generating a formation shape and maintaining it during motion while moving toward a goal. Experimental and simulation results confirm the applicability of this control approach.

Growing research interest in space robotic systems capable of accurately performing autonomous manipulation tasks within an acceptable execution time has led to an increased demand for lightweight materials and mechanisms. As a result, joint flexibility effects become important, and represent the main limitation to achieving satisfactory trajectory tracking performance. This paper addresses the nonlinear adaptive output feedback control problem for flexible-joint space manipulators. Composite control schemes in which decentralized simple adaptive control-based adaptation mechanisms to control the quasisteady state robot subsystem are added to a linear correction term to stabilize the boundary layer subsystem, are proposed. An almost strictly passivity-based approach is adopted to guarantee closed-loop stability of the quasi-steady state subsystem. Simulation results are included to highlight the performance and robustness to parametric uncertainties and modeling errors of the proposed composite control methodologies.

In this paper, the translational accuracy performance of two algorithms that allow the pose of an Unmanned Aerial Vehicle (UAV) to be estimated is reported, based on preliminary data. The two algorithms are the 2D Homography and the 3D Iterative Closest Point (ICP) algorithm. Performance is measured against real image data taken with a digital Nikon D70 camera, with lens set at 3 different focal lengths. It is shown that best accuracy is achieved with the 3D Iterative Closest Point algorithm and that focal length itself has little or no bearing on this performance. Calibration errors may impact accuracy.

The use of harmonic drive gear boxes in space robotic applications offer several attractive properties, such as high reduction ratio, compact size, low mass, and coaxial assembly. However, the flexibility effects of this type of gear mechanism in the joints of robotic manipulators are significant enough to make real-time control challenging. This paper addresses the problem of fuzzy adaptive trajectory control of space manipulators that exhibit elastic vibrations in their joints and that are subject to parametric uncertainties and dynamics modeling errors. The developed control scheme uses a fuzzy adaptation mechanism that varies in real-time the gains of a slow control term designed to stabilize the rigid dynamics, and a linear correction term to further reduce the elastic vibrations at the joints. The proposed controller is validated in numerical simulations in a trajectory tracking scenario by a flexible-joint manipulator. Simulation results suggest that the controller is robust to uncertainties in joint stiffness coefficients and to modeling errors, and yields improved tracking performance compared with conventional flexible-joint control strategies.

The research presented in this paper ultimately aims at accurate Unmanned Aerial Vehicle (UAV) navigation using camera(s) to augment inertial navigation unit data while flying through an urban environment. Accurate position and depth determination requires precise image feature location and matching. This paper investigates accurate feature matching enabling determination of image depth. The paper offers two unique contributions to the field. First, it is shown how to improve feature matching accuracy when a good position estimate is available. Secondly, it is shown how to increase the number of matched features. In this way, there is more data and it may be possible, in future research, to identify the depth plane a feature belongs to and so increase the accuracy of position determination. Preliminary results are reported.

This paper compares two commonly used algorithms to solve Simultaneous Localization and Mapping (SLAM) problem in order to safely navigate an outdoor autonomous robot in an unknown location and without any access to a priori map. EKF-SLAM is considered as a classical method to solve SLAM problem. This method, however, suffers from two major issues; the quadratic computational complexity and single hypothesis data association. Large number of landmarks in the environment, especially, nearby landmarks, causes extensive error accumulation when the robot is traveling along a desired path. The multi-hypothesis data association property and the linear computational complexity are essential features in FastSLAM method. Those features make this method an alternative to overcome mentioned issues. The FastSLAM algorithm uses Rao-Blackwellised particle filtering to estimate the path of the robot and EKF-SLAM method to estimate locations of landmarks. In case of FastSLAM applications, however, observation noise needs to be reconsidered if the motion measurements are noisy while the range sensor is noiseless. This study suggests optimization of a specific situation of FastSLAM algorithm in case of noise discrepancy.

An adaptive fuzzy logic control scheme is developed for tracking a square trajectory by the endpoint of a two-link rigid joint space robot. The control strategy is based on a direct adaptive control scheme in which the controller gains are adapted in real-time according to fuzzy logic systems such that the tracking errors between a reference model and the actual robot system outputs are brought to zero.

Lightweight space robotic systems are known to exhibit elastic effects in their gearboxes, which contribute greatly to the motion of their end-effector. When accurate end-effector positioning is required, such flexible joint vibrations must be taken into account in both the modeling and control of space manipulators. This paper presents an analysis and a comparative numerical simulation study of previous studies on advanced control schemes for flexible joint robots. More specifically, a linear PD control, a rigid-based indirect adaptive control, an indirect adaptive composite control, a nonlinear backstepping control, a transpose Jacobian composite control, and a direct adaptive composite control methodologies are considered. The performance of the controllers is illustrated in a 12.6 × 12.6 m square trajectory tracking scenario by a two-link flexible joint space manipulator.

This research was motivated by a need for accurate UAV navigation using camera(s) to augment inertial navigation unit data while flying over, or through an urban environment. The process of position determination using cameras follows a sequence of well defined steps. First features must be found in consecutive-in-time images and then matched across time. The underlying camera motion, as defined by camera rotation and translation, may then be calculated as a homography, for example. Accurate homography determination requires use of a set of at least 4 feature pairs that are not collinear and are accurately matched. In this paper it is shown that collinearity of features in urban images is quite likely to occur. Thus, collinear features must be removed. This paper reports an easy way to do so and examines, as well, the issue of accurate matching. Preliminary results are reported.

The work presented in this paper has the goal to derive equations that permit experimental design of active non-destructive infrared testing. The approach consists of the predetermination of the required frequency for the input thermal signal given the size of a variety of possible defects in materials and the time when steady state response can be recorded. Infrared testing is known for the ability to give thermal images that are influenced by inner defects. For this reason, in the proposed approach the model used for the experimental design of non-destructive infrared active testing of materials is reduced from full three-dimensional (3D) to one-dimensional (1D). It is considered that, in some specific conditions, relevant results can be obtained before reflected thermal waves from more remote boundaries arrive back to the point under investigation. The analysis starts with the formulation and solution of the direct problem for three-dimensional (3D) heat conduction problem, followed by detailed development for the one-dimensional (1D) problem and ends with simulation results that illustrate the usefulness of the pre-determination of the frequency of the input thermal signal needed for the investigation of possible defects in materials.

Autonomous navigation in an outdoor environment can be effectively performed by small robots but remains a challenging task as soon as real scale ground vehicles are concerned. An intelligent mobile robot which uses a real armor ground vehicle as a platform is currently being developed for security applications (Fig. 30.1(a)). The robot tasks needed to be addressed are patrolling, surveillance, and tracking suspicious objects in a limited area. Executing those tasks a security robot often faces the situation when many objects in the environment have similar geometrical characteristics. This situation is typical for navigating within the outdoor warehouse [1] as illustrated in Fig. 30.1(b). The cargo containers (the squares with pattern) of the same size are piled inside the fenced area. A robot is used to carry out the patrol along a given trajectory (dotted line) around the area. Being equipped with the basic positioning system using GPS receiver and odometry sensor, a robot is able to follow the given path if measurement no errors are negligible. In practice, due to various errors (misalignments, drifts, slippage) basic GPS/odometry-based system fails to provide reliable pose estimates required in physically restricted environments. Measurements of relative distances are necessary and they are provided in our case by an additionally installed laser range sensor (LRS) known for its accuracy and reliability.

Guiding an autonomous ground vehicle through the gate based on the range data from the laser scanner is considered. This kind of operation is important for security mobile robots. Information about the gate shape is supposed to be more accurate than the geographical location of the gate. This necessitates the transition from GPS-based guidance in the open field to range-based guidance in the proximity of the gate. Recognition procedure based on the proposed concept of the gate signature is followed by Map-matching and Localization. Navigation controller makes use of the polar coordinate representation and discontinuous feedback adapted for real time applications.

Operational problems with space robots relate to several factors, one most importantly being structural flexibility and subsequently significant difficulties with position control. Elastic link vibrations with inherent nonminimum phase response coupled with large rotations create a complex nonlinear dynamic system for control. The ability of the flexible robot to follow a prescribe trajectory is an important issue. In case of more complex operation, good tracking control could potentially save much time and money. This paper compares the performance of control strategies with noise filtering, then combined with nonminimum phase and corrective control action modeled by time delays for a stationary spacecraft mounted flexible robot tracking a 12.6 m × l2.6 m square trajectory. Finally a fuzzy logic system (FLS) vibration suppression strategy demonstrates its effectiveness in providing intelligent and autonomous precision control. The inclusion of this fuzzy logic approach to vibration suppression is motivated by the prospect of substituting for the control of space robotic systems typically teleoperated by astronauts. Tracking results are obtained initially without vibration suppression or noise filtering, then with EKF and FLAEKF state estimators to determine tracking accuracy for robot process and measurement noise. Time delays are included in the feedback and control action loops to simulate nonminimum phase response and corrective control. Finally, a FLS is used to adapt the control law and suppress residual vibrations. Good tracking results were previously achieved using an input shaping method to reduce residual vibrations coupled with an inverse kinematics control strategy and recursive order-n algorithm to model a two-link flexible robot tracking a square trajectory [1].

Navigation of autonomous vehicles and robots can be divided into two categories: indoor navigation and outdoor navigation. In general the outdoor navigation is more difficult and complex task because often the environment does not have characteristic points that can be identified and with respect to which robots could relate their position. If environment is unstructured (e.g. off-road environment), problems related to autonomous navigation are very difficult. Problems related to navigation in the unstructured environment could be divided into mapping, localization, collision avoidance and trajectory tracking.

Aerial robots can be considered as the natural evolution of the Unmanned Aerial Vehicles (UAVs) from the point of view of the intelligence. Traditional industrial robots evolved from the repetition of sequences of previously defined way points, with minimal perception and decision capabilities, toward robots interacting with the environment, mapping unknown environments and localising the robot in these maps. Thus, Simultaneous Localisation and Mapping (SLAM), has played an important role in the exploration of unknown environments. In the same way, Unmanned Aerial Vehicles will also evolve from the current commercial UAVs that mainly move by following a list of way points, to aerial robots capable of perceiving the environment adapting their behaviour to dynamic events. In addition, current UAV navigation strongly depends on satellite positioning systems, and then is sensitive to GPS transient failures, which are common in many outdoor environments. Furthermore, the navigation in GPS denied environments requires positioning devices that could be very costly and/or difficult to be implemented. Therefore, positioning with respect to the perceived environment is a need to increase the reliability or even to make possible UAV navigation. Moreover, environment mapping is also a very desirable feature for many UAV applications. Consequently, simultaneous

This paper presents the current state of the technology in space robotics. Some historical developments and the experience gained during those projects have been discussed. The challenge of space exploration and related danger requires even larger use of robotics devices. Many tasks and jobs are being automated. The exploration of the Moon and Mars requires the autonomous, mobile robots to gather various information, measurements and data. An introduction of Space Shuttle and related activities required an efficient robotic manipulator for in orbit operations. The Space Station Mir (in orbit) and especially Space Station Freedom (planned for nearest future) require advanced robots and manipulators. The Space Station Remote Manipulator System (SSRMS) and Special Purpose Dextrous Manipulator (SPDM) have been shown and briefly reviewed. Those and other robots for future missions are discussed in this paper.