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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.

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.

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.

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.

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.

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.

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 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.

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.

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.

This paper presents an investigation on the performance of Unscented HybridSLAM using two different mapping strategies. The global map estimation using Unscented Kalman Filter is scrutinized for different scenarios, with and without the influence of a data association process. The accuracy of generated global map under different vehicle speed settings and with different process time is demonstrated using computer simulation. Results are discussed in terms of Root Mean Square (RMS) position error, orientation error, and time of navigation process. Results show that depending on the application, and on a desired speed, a compromise has to be done to get the best efficacy.

Autonomous robots can be equipped to detect potential threats of fire and find out the source while avoiding the obstacles during navigation. The proposed system uses Voting Logic Fusion to approach and declare a potential fire source autonomously. The robot follows the increasing gradient of light and heat to identify the threat and approach source.

This paper presents a control system for guidance and navigation of a mobile planetary rover. The control system is composed of three subsystems. The path tracking is accomplished using the LQR algorithm. The position and orientation errors are feedback using optimal gains. The disturbances are feedforward to improve the transient responses of the rover. Position and orientation errors are measured using odometry and inertial navigation system (INS). The collision avoidance is performed using the artificial potential field method. The data for that control loop is acquired using a scanning rangefinder. Dead reckoning is improved by using filtering and fusing signals from INS and odometry. Extensive simulation results and partial experimental results have been shown.

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.

This paper addresses a novel approach to Simultaneous Localization and Mapping problem called Unscented HybridSLAM. Analyzing loop closing as one of the most important aspects of SLAM is the major interest of this study. Closing a loop by an autonomous mobile robot can be tricky even in the presence of a correct data association in the process, especially, when the loop is considerably large. The main challenge is to maintain a track of all correlations among landmarks and the mobile robot using Unscented HybridSLAM. The performance of proposed algorithm is evaluated with a loop closing scenario simulation, then, the results are discussed and compared with other algorithms.

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.

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.

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.

This paper presents a navigation of an autonomous robot using simultaneous localization and mapping (SLAM) in outdoor environments. SLAM is a method in which localization and mapping are done simultaneously in an unknown environment without an access to a priori map. This paper introduces a probabilistic approach to a SLAM problem under Gaussian and non-Gaussian conditions and offers alternative solutions. First, an extended Kalman filter algorithm for the SLAM problem under Gaussian condition will be shown. Also, an alternative way of dealing with SLAM problem with assumption of non-Gaussian and called FastSLAM will be analyzed. FastSLAM is an algorithm that using Rao-Blackwellised method for particle filtering, estimates the path of robot while the landmarks positions which are mutually independent and with no correlation, can be estimated by EKF. This is done using a factorization that fits very well into SLAM problem based on a Bayesian network. In this paper, a real outdoor autonomous robot is presented and several experiments have been performed based on both methods. The experimental results are discussed and compared.

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.

The navigation of an autonomous robotic vehicle is a difficult task. Accurate measurement of robotic vehicle motion is a problem in certain environments. In desert and other terrains, wheel slip affects the accuracy of odometry sensors. Poorly-lit underground environments present problems for passive vision systems. As well, for slow-moving vehicles, the effects of INS drift errors can be large even over short distances. An active triangulation scanning laser camera sensor, which can provide accurate 3D images at distances less than 10m, has the potential to alleviate the problems mentioned above by improving the accuracy of integrated navigation systems for robotic vehicles operating in such environments. Knowledge of the relative position measurement accuracy for scanning laser cameras in various environments will allow navigation system designers to determine whether incorporating these sensors will help to meet their system accuracy requirements. This paper presents an experimental method for determining relative position measurement accuracy of an auto-synchronous triangulation scanning laser camera. 3D images were taken of a simulated desert terrain environment from multiple camera positions and orientations. Registration of overlapping images using an Iterative Closest Point (ICP) based algorithm was performed to determine an estimate of the position and orientation change of the laser camera. Truth data for the position and orientation of the laser camera at each location was determined by using theodolites to measure the location of survey targets mounted on the laser camera. The relative position estimates were then compared to the truth data. In this paper, the experiment design and implementation are detailed, and preliminary experimental results are presented.

Presents a method of sensor fusion based on 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 autonomous mobile vehicles. The presented method has been validated in a 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 have been modified using a fuzzy logic adaptive system and compared with the performance of the regular EKF. It has been demonstrated that the fuzzy adaptive Kalman filter gives better results (more accurate) than the EKF.

This paper presents a set of algorithms for piloting an autonomous planetary rover along a planned path, performing obstacle avoidance, and improving position estimation. Path-tracking is accomplished using feedback-control of position and orientation errors, measured with respect to the planned path trajectory. Obstacle-avoidance is performed through application of an artificial potential field, to data that can be acquired using a scanning rangefinder. Position and velocity estimation is improved by the algorithmic filtering and fusing of odometry and inertial navigation data streams. Results from computer simulation are used to illustrate the path-tracking and obstacle-avoidance capabilities, and experimental data is used to illustrate how sensor fusion mitigates the effects of wheel-slippage and integration-error in position and velocity estimation. >