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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.

The structure of a space target, and estimates of its relative pose (position and orientation) and motion are the primary first tasks of any formation flight, including missions to remove space debris. Acquiring these estimates for high-speed tumbling objects is very challenging, due to the lack of prior information about the target’s structure and motion. This paper proposes a method to estimate the relative position, linear and angular velocities, and attitude of space debris, based on vision measurements. The suggested approach employs a stereo camera to track a set of feature points on the target spacecraft, and an Unscented Kalman Filter (UKF) to perform the estimation procedure. The projection of tracked feature points on the two cameras creates an observation model of the filters and the structure of a non-cooperative spacecraft was determined by estimating the feature points’ position. The relative attitude estimation is derived through the Modified Rodrigues Parameters (MRPs). A number of numerical simulations were conducted to validate the accuracy and stability of this solution, and the results indicated acceptable convergence of estimation errors for pose and motion estimation.

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.

Manipulator mounted on an unmanned satellite could be used for performing orbital capture maneuver in order to repair satellites or remove space debris from orbit. Use of manipulators for such purposes presents unique challenges, as high level of autonomy is required and the motion of the manipulator influences the position and orientation of the manipulator-equipped satellite. This paper presents a new control system that consists of two modules: trajectory planning module (based on trajectory optimization algorithm) and Model Predictive Controller. Both modules take into account the free-floating nature of the satellite-manipulator system. Proposed control system was tested in numerical simulations performed for a simplified planar case. In the first set of simulations Nonlinear Model Predictive Control (NMPC) was used to ensure realization of a square reference end-effector trajectory, while in the second set control system was used for optimizing and then ensuring realization of the trajectory that leads to grasping of the rotating target satellite. Simulations were performed with disturbances and with the assumed non-perfect knowledge of parameters of the satellite-manipulator system. Results obtained with NMPC are better than results obtained with the controller based on the Dynamic Jacobian inverse and with the Modified Simple Adaptive Control (MSAC).

Operation of mobile robots in off-road environment requires the attention to the torque saturation problem that occurs in the wheels DC motors while climbing hills. In the present work, off-road conditions are utilized to benefit while avoiding torque saturation. Energy optimization algorithm using predictive control is implemented on a two-DC motor-driven wheels mobile robot while crossing a ditch. The predictive control algorithm is simulated and compared with the PID control and the open-loop control. Predictive control showed more capability to avoid torque saturation and noticeable reduction in the energy consumption. Furthermore, using the wheels motors armature current instead of the supply voltage as control variable in the predictive control showed more efficient speed control. Simulation results showed that in case of known ditch dimensions ahead of time, the developed algorithm is feasible. Experimental examination of the developed energy optimization algorithm is presented. The experimental results showed a good agreement with the simulation results. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot. Curve fitting is applied to the obtained results to address further the effect of the parameters on the energy consumption.

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.

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.

A smart fire detection system is a necessary part of a smart manufacturing facility. Autonomous robots may be deployed to seek out a potential source of fire in the industrial environment, approach, investigate and declare the presence or absence of fire based on several sensor fusion techniques. A novel approach of the above mentioned topic is described in this paper. A new technique, introduced as “Modified Voting Logic” is explained. The robot uses sensor readings comparison to approach the source and a smart obstacle avoidance system to avoid obstacles together with Modified Voting Logic to declare a fire threat.

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.

In this paper an advanced control system for an elastic joint two-link flexible link robot manipulator is considered. The system uses controller with an adaptive fuzzy logic. Nonlinear friction in the joints is modeled by the Hammerstein system. It is identified by the algorithm based on nonparametric nearest neighbor regression estimation. The asymptotically optimal choice of the parameters of the algorithm are investigated.

This paper presents navigation and control of a robot for capturing a moving target using the vision system. A stereo camera is used to calculate the pose of the moving target (position and orientation). An Adaptive Unscented Kalman Filter (AUKF) is used to generate an optimal path to capture the moving object by estimating the state vector (position, orientation, linear and angular velocities) of the target. The Fuzzy Logic Adaptive System (FLAS) has been used to prevent the AUKF from divergence. The FLAS can evaluate the performance of UKF and tuning the factors in the weighted covariance to improve the accuracy of UKF. A new trajectory planning method for the space robot is proposed based on the information acquired from the vision system and estimation the linear and angular velocities of the target by AUKF. The results from simulation experiments were presented and discussed. It was concluded that the Fuzzy Adaptive Unscented Kalman Filter methods give more accurate results rather than the Unscented Kalman filter or Extended Kalman Filter.

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 quasi-steady-state robot model are added to a linear correction term to stabilize the boundary-layer model are proposed. An almost strictly passivity-based approach is adopted to guarantee closed-loop stability of the quasi-steady-state model. Simulation results are included to highlight the performance and robustness of the proposed adaptive composite control methodologies to parametric and dynamics modeling unce...

Mobile robot formations differ in accordance with the mission, environment, and robot abilities. In the case of decentralized control, the ability to achieve the shapes of these formations needs to be built in the controllers of each autonomous robot. In this paper, self-organizing formations control for material transfer is investigated, as an alternative to automatic guided vehicles. Leader–follower approach is applied for controllers design to drive the robots toward the goal. The results confirm the ability of velocity potential approach for motion control of both self-organizing formations.

Mobile robot formations differ in accordance to the mission, environment and robot abilities. In the case of decentralized control, the ability to achieve the shapes of these formations needs to be built in the controllers of each autonomous robot. In this paper is investigated self-organizing formations control for material transfer, as an alternative to Automatic Guided Vehicles (AGVs). Leader-follower approach is applied for controllers design to drive the robots toward the goal. The results confirm the ability of velocity potential approach for motion control of both self-organizing formations.

Kalman filter requires that the process noises to be zero mean white noise; otherwise, the divergence will occur. Adaptive tuning of a Kalman filter via fuzzy logic has been one of the promising strategies to cope with divergence when dealing with non-white noise. The fuzzy logic adaptive controller (FLAC) will continually adjust the noise strengths in the filter's internal model and tune the filter. This paper presents a new INS/GPS sensor fusion scheme based on Fuzzy Adaptive Unscented Kalman Filter (FAUKF). The FAUKF is based on the combination of the unscented Kalman filter and the fuzzy logic controller which performs adaptation task for dynamic characteristics. Results obtained by FAUKF were compared to the Extended Kalman filter (EKF), Unscented Kalman Filter (UKF) and Fuzzy Adaptive Extended Kalman Filter (FAEKF). This comparative study has demonstrated that the FAUKF leads to very promising results as compared the other three Kalman filters.

Mobile robot formations differ in accordance to the mission, environment and robot abilities. In the case of decentralized control, the ability to achieve the shapes of these formations has to be built in the controllers of each autonomous robot. In this paper two types of basic formations are investigated: self-organizing and geometric. Leader follower approach is applied for controllers design to drive the robots toward the goal. Simulations show that that self-organizing formations tend toward platoon formation. Geometric formations approach is also simulated for the case of V-formations passing through a passage. The results confirm the ability of velocity potential approach for motion control of both self-organizing and geometric formations

DOI: 10.2514/1.54083 This paper addresses the problem of adaptive trajectory control of space manipulators that exhibit elastic vibrations in their joints and that are subject to parametric uncertainties and modeling errors. First, it presents a comprehensive study of rigid and linear flexible-joint stiffness models, to propose a dynamic formulation that includes nonlinear effects such as soft-windup and time-varying joint stiffness. Second, it develops an adaptive composite control scheme for tracking the end effector of a two-link flexible-joint manipulator. The control scheme consists of a direct model reference adaptive system designed to stabilize the rigid dynamics and a linear correction termtoimprovedampingofvibrationsatthejoints.Numericalsimulationscomparetheperformanceoftheadaptive controllerwithitsnonadaptiveversioninthecontextofa12:6 12:6 msquaretrajectorytracking.Resultsobtained withtheadaptivecontrolstrategyshowanincreasedrobustnesstomodelingerrorsanduncertaintiesinjointstiffness coefficients, and greatly improved tracking performance, compared with the nonadaptive strategy.

This paper presents a general overview of the technological fields of mechatronic, robotic, components for automation and control. Five technical areas are considered: component and instruments, mechatronic, robotics, cost oriented automation and human-machine systems. The paper addresses their current key problems and the recent major accomplishments. At last the most promising forecasted development and applications are considered.

Unmanned Aerial Vehicles (UAVs) became a technology that have attracted considerable interest in the commercial markets for the military and civilian uses, such as surveillance and reconnaissance, aerial surveys for natural sources, traffic monitoring, early forest fire detection etc. This paper deals with Nonlinear and Model Predictive Control (MPC) of Unmanned Aerial Vehicles (UAV) flying in formation. Although, UAVs present numerous advantages over the manned aircrafts, they face challenges in various aspects of control in autonomous mode, and even more, in formation flying. An advanced control system is demonstrated as a possible solution to improve and increase the level of the autonomous mode and flying capabilities of UAVs. This paper deals with advanced control system of Unmanned Aerial Vehicles (UAV) flying in formation.

Inverse flexible dynamics control (IFDC) and fuzzy logic system adaptive control (FLSAC) strategies are used to track the end effector of a flexible space robot with sensors collocated at the joints, noncollocated at the end effector and 0.5m from the elbow joint on link 2. Collocated joint sensors satisfy hyperstability conditions but fail to capture nonminimum phase (NMP) response that generates time-delays causing asynchronous control action. Noncollocated sensors capture NMP response but require time delay compensation to achieve synchronous control action. Results for IFDC are insignificant compared to those achieved with FLSAC.

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.

Control of Unmanned Aerial Vehicles is a difficult task. One of the most challenging problems are nonlinear nature of the vehicle dynamics and the second is related to the nonminimum phase system. There are several methods to design nonlinear controller. One of them is so-called feedback linearization. Even, after successful design of nonlinear controller several important issues like nonminimum phase problem remain. The system with nonminimum phase dynamics needs to have outputs redefined. The output redefinition technique is used in a way such that the resulting system to be inverted is a minimum phase system. The complex calculation related to the system dynamics redefinition make a real-time computation very difficult. The real-time control system needs to be fast and reliable. In order to make a real-time control possible, a neural networks method has been developed and presented. The NARMA-L2 Neural Network is trained off-line to identify the forward dynamics of the UAV model with the redefined output, which is subsequently inverted to force the real output to approximately track a command input. Simulation results show that the proposed neural network method has overall good performance.

This paper presents the problem of navigation, gate recognition, and gate crossing for a real size outdoor mobile robot. The vehicle localization and general navigation is based on Global Positioning System (GPS) and Inertial Navigation System (INS). These sensors are aided by a Laser Measuring System (LMS). The laser scanner sensor gives information about the gate exact location, its shape, and vehicle attitude with respect to the gate. This necessitates the transition from GPS/INS based guidance in the open field to the range based, guidance system in proximity of the gate. A recognition procedure based on the concept of the gate signature has been developed and shown. It is followed by novel map-matching and localization procedures respectively. The developed navigation controller makes use of the polar coordinate representation and discontinuous feedback adapted for real time applications.

There are two popular control methods widely used in haptic controller design. First, is impedance control – the operator motion input is measured, and then, the reaction force is fed back to the operator. The alternative method is admittance control-forces exerted by operator are measured and positions are fed back to the operator. Both, impedance and admittance control are also two basic methods for interacting with a virtual environment. In this paper, several experiments were performed to evaluate the suitability of impedance control with force feedback 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. The experiments were carried out using an experimental setup. Experimental results for 1 DOF rotational motion and 2 DOF planar translational motion systems are presented. Other experimental results are also shown and discussed.

This paper presents the path tracking and following for the Autonomous LHD (Load-Haul-Dump) articulated vehicle. A fuzzy logic control system for the articulated vehicle was developed to control longitudinal and lateral motion. The lateral control keeps the vehicle along the planned path and the longitudinal control makes the vehicle running at the required speed. Fuzzy logic control method is compared with classical methods to evaluate its performance and several simulation experiments were performed to develop the path following control system with 3-D motion animation. Different type of paths were tested and analyzed.

This paper presents model predictive control of an autonomous vehicle. Simulation and experimental results have been shown and compared with input–output linearization method. The results obtained show that the MPC is an efficient method that allows for accurate control and navigation of an autonomous vehicle. Model based predictive control is tested in simulations for motion on an inclined plane. This is done to prepare future work regarding the avoidance of the violation of the smoothness condition for exact linearization, while at the same time by modifying the input commands the geometric path planning results are conserved. The approach is presented for the wheel-ground slippage and tip-over avoidance of the three-wheeled vehicle for inclined plane motion. A complete three-dimensional dynamic model using Newtonian dynamics is also presented. Simulation results using a three-wheeled vehicle built in our laboratory illustrate the performance of the proposed controller.

Operational problems with robot manipulators in space relate to several factors, most importantly, structural flexibility and subsequent difficulties with their position control. In this paper we present control methods for endpoint tracking of a 12.6 × 12.6m2 trajectory by a two-link robot manipulator. Initially, a manipulator with rigid links is modeled using inverse dynamics, a linear quadratic regulator and fuzzy logic schemes actuated by a Jacobian transpose control law computed using dominant cantilever and pinned-pinned assumed mode frequencies. The inverse dynamics model is pursued further to study a manipulator with flexible links where nonlinear rigid-link dynamics are coupled with dominant assumed modes for cantilever and pinned-pinned beams. A time delay in the feedback control loop represents elastic wave travel time along the links to generate non-minimum phase response. A time delay acting on control commands ameliorates non-minimum phase response. Finally, a fuzzy logic system outputs a variable to adapt the control law in response to elastic deformation inputs. Results show greater endpoint position control accuracy using a flexible inverse dynamics robot model combined with a fuzzy logic adapted control law and time delays than could be obtained for the rigid dynamics models.

This paper presents the model, simulation and experimental results of model predictive control (MPC) for motion control of an autonomous vehicle. The simulation and experiments were designed for mining application. In mining industry robotic vehicles have to navigate in tunnels and often have to overcome inclined and rough surfaces. In this case, the MPC was applied using a three-dimensional dynamic model of the robot vehicle. The purpose is to achieve autonomous vehicle motion with the avoidance of wheel-ground slippage or loss of contact while, at the same time, conserving the geometric path planning results by modifying the input commands. Tests were carried out for a vehicle moving on an inclined plane. Results from testing MPC plus feedback linearization controllers are presented and compared with results for controllers using only feedback linearization. Simulation results are presented for a three-wheeled vehicle moving in conditions that permit to illustrate the performance of the proposed controller. The results obtained show that the MPC is an efficient method that contributes to accurate control and navigation of an autonomous vehicle. The results were used to evaluate the performance and operational capabilities of an autonomous unmanned mining vehicle.

This paper presents the position and velocity determination by using INS and GPS. The measurement results from INS and GPS sensors are fused by using Kalman filter. Dilution of Precision (DOP) technique is used to select a combination of satellites to be used as measurement data. Two implementations of Kalman filter, feedforward and feedback are used. The experiment shows that the selection of the satellites affects the measurements. For biased and correlated data an adaptive technique, developed earlier was used. This paper is a demonstration of known techniques rather than an introduction of new ones.

Operational problems with robot manipulators in space relate to several factors, one most importantly being structural flexibility and subsequently significant difficulties with the control systems, especially, position control. A control strategy is devised for positioning the endpoint of a two-link robot manipulator modeled with assumed modes flexible dynamics repetitively tracking a square trajectory. The dominant assumed modes of vibration are determined for Euler-Bernoulli cantilever beam boundary conditions then, coupled with the nonlinear dynamics for rigid links to fonn an Euler-Lagrange inverse flexible dynamics robot model. A Jacobian transpose control law actuates the robot links. While repetitive tracking alone achieves no improvement in control precision, adapting the control law by a fuzzy logic system achieves consistent tracking precision.

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 an ongoing research on a novel approach to Simultaneous Localization and Mapping problem called Unscented HybridSLAM. The main contribution of this paper is to develop the map update formulas along with proof results in order to investigate the validation of the map evolution. The investigation is presented using the help of simulations in terms of robustness, map fusion, and the update process. Results clearly show that as the vehicle travels along the path and the map evolves, the Unscented HybridSLAM algorithm avoids the overestimation of landmarks.

This paper presents the sensor fusion for dead-reckoning mobile robot navigation. Odometry and sonar measurement signals are fused together using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). Two methods of adaptation scheme are used, the first one uses Q and R , the second one only uses Q . The first method gives faster result than the second one. The fused signal is more accurate than any of the original signals considered separately. The enhanced, more accurate signal is used to guide and navigate the robot.

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.

This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The Extended Kalman Filter (EKF) and the noise characteristic has been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF.

The paper presents a solution to dynamic calibration of sensors for precise remote monitoring and control for teleoperation. The solution is based on the inverse dynamics of sensors and signal transmission system. The method is formulated theoretically, and then is exemplified for multi-sensor acceleration measurements. In the first example, robot arm control using joint acceleration feedback is analyzed for the case without and with dynamic calibration of sensors using inverse dynamics. In the second example, pure delay compensation is discussed for the case of INS and GPS signal fusion for flight control.

In positioning and localization problems, two or more different sensors are often used to obtain the best estimation data for control system. Extended Kalman Filter (EKF) is widely used to fuse those data to obtain one optimal result. One consideration when using EKF is the signal used during navigation is a white noise signals. This consideration is hardly found in real application. This paper presents the sensor fusion for dead-reckoning mobile robot navigation. Odometry and sonar signals are fused using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). The AFLS was used to adapt the gain and therefore prevent the Kalman filter divergence. The fused signal is more accurate than any of the original signals considered separately. The enhanced, more accurate signal is used to guide and navigate the robot.

A direct proportional derivative (PD) control scheme is used to obtain a square trajectory 12.6 m × 12.6 m tracked by a two-link flexible robot manipulator incorporating nonlinear dynamics equations. An alternative approach uses a fuzzy logic control (FLC) scheme to track the trajectory by substituting the nonlinear dynamics equations with two fuzzy controllers, each dedicated to a link of the manipulator. The links are coupled by feeding acceleration output from the FLC for Link 1 into the FLC for Link 2 to simulate interactions between links across the elbow joint.

In this paper, a method based on Adaptive Fuzzy Kalman Filtering has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the navigation of autonomous mobile vehicles. The presented method is of particular importance for guidance, navigation, and control of flying vehicles and has been validated in 3-D environments. The Extended Kalman Filter (EKF) and the noise characteristic are modified using Fuzzy Logic Adaptive System and compared with the performance of a regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results in terms of accuracy than the EKF.

To solve the problems in guidance, navigation, and control for an autonomous robot, its accurate positioning and localization are needed. Two or more different sensors are often used to obtain reliable data useful for control system. Extended Kalman Filter (EKF) is widely used to fuse those data to obtain one optimal result. The signals used during navigation cannot be always considered as white noise signals. On the other hand, colored signals will cause the EKF to diverge. This paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). The AFLS was used to adapt the gain and therefore prevent the Kalman filter divergence. The fused signal is more accurate than any of the original signals considered separately. The enhanced, more accurate signal is used to guide and navigate the robot.