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Based on the recent development of sufficient conditions that allow nonstationary systems to become stable and strictly passive via static or dynamic output feedback, this paper presents a new class of direct adaptive output feedback controllers. The proposed control methodology uses a decentralized adaptation law mechanism based upon the Simple Adaptive Control technique. Using Lyapunov direct method and Lasalle's invariance principle for nonautonomous systems, the formal proof of stability is established. The applicability of the proposed adaptive control approach to second-order Euler-Lagrange systems, whose nonlinear dynamics is expressed in the operational space, is presented.

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.

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 robot. In this paper two types of basic formations are investigated, platoon and V-shape. Leader follower approach is applied for controllers design to drive each robot toward achieving the desired geometric shape of the formation.

This paper presents a modified version of HybridSLAM (HS) method using unscented Kalman filter to solve simultaneous localization and mapping problem. Instead of applying extended Kalman filter for SLAM (EKF-SLAM) to build the map of the environment, an unscented Kalman filter (UKF) was added to the HS algorithm. This would allow including higher order of non-linearity of the motion. The new method called Unscented HybridSLAM (UHS) is constructing the global map more accurately compare to the original HS and by employing the same constraint of local sub-map fusion technique, a more reliable solution to SLAM problem is achieved. The unscented Kalman filter takes advantage of both statistical and analytical linearization techniques to estimate the global map. The local map in the vicinity of the robot is estimated using FastSLAM and the local map is fused to the map using constrained local sub-map fusion technique. Unscented HybridSLAM uses a minimal set of chosen samples to approximate the posterior mean and covariance for a nonlinear system. The unscented HybridSLAM performance is compared to the original HybridSLAM and FastSLAM algorithms and it is shown that in case of severe nonlinearity, the proposed unscented HybridSLAM is outperforming current filters in terms of estimation of the path and map building.

Vehicle path tracking control problem is investigated using optimal control technique. Steering angle is subject to two limitations — both its magnitude and rate are bounded. Although, it is the most natural settings from the application point of view — it has been rarely considered. An approach described in this paper utilizes the shortest-time solution for a 3-rd order linear system known for long time and revisited in the nineties to improve its robustness. Main generalization proposed here concerns state limitations resulting from the bounds over steering magnitude. Linear settings is sufficient if the offsets from the desired path are small. The validity of the ”linear”' solution is verified by applying the obtained optimal bang-bang control to a nonlinear system. In case of large lateral and angular offsets from the path the nonlinear analysis is needed. We show that in spite of the low dimension of the system (third) the nonlinear problem presents a variety of challenges, namely, singular optimal trajectory and a Fuller-type phenomena consisting of ‘chattering’ near the junction of non-singular and singular portions of optimal trajectories. Finally, the controller designed using linear technique is tested with nonlinear model in simulation and experimentally.

This paper presents a novel direct model reference adaptive control scheme using a decentralized update law for nonsquare Euler-Lagrange systems. The proposed decentralized adaptation law is based on the simple adaptive control (SAC) methodology in which the control gain matrices are updated by the reference model input and state signals, and by the errors between the nonlinear system and a reference model. Numerical simulation results are presented to illustrate the trajectory tracking performance of the proposed approach in comparison with both a decentralized modified simple adaptive control (DMSAC) and a fuzzy logic-based direct adaptive control strategy. Results demonstrate that the designed adaptive control approach achieves improved tracking results compared to the DMSAC and fuzzy logic control methodologies.

Most advanced trajectory tracking control laws for robot manipulators require a knowledge of all state variables. For lightweight flexible-joint space manipulators this objective is difficult to achieve since link positions are typically not measured. In this paper, an extended Kalman filter (EKF) observer to estimate all state variables of a manipulator system modeled with a nonlinear stiffness dynamics model is presented. In addition, it is shown that the observer can be modified in order to calibrate in real-time the sensor biases. The state variable estimates are coupled to a flexible joint adaptive controller to provide a complete closed-loop trajectory tracking solution. Simulation results show that the proposed solution provides satisfying tracking performance in a 12.6 ×12.6 m trajectory tracking scenario.

Control of the flexible arm space based robots in general is a difficult problem. Low damping and significant flexibility cause vibration and non-linear phenomenon. This paper presents a control system for a flexible arm using piezofilm sensors and piezoceramic actuators. LQR method, based on a full state feedback, has been used to control the flexible arm. The control system has been studied both analytically and experimentally. Results are compared and discussed. The paper shows that piezoelements can be efficient components of control systems especially in case where several dynamic modes have to be compensated.

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.

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.

In this paper, an extended Kalman filter (EKF) strategy to estimate state variables from noisy measurements in flexible joint space manipulators is presented. First, an EKF that estimates the link and motor positions/velocities using only measurements from motor sensors is developed for space robots modeled with a classical linear joint dynamics model. Second, an extension for a novel nonlinear joint dynamics formulation is provided. The state estimates are coupled to a flexible joint adaptive controller in order to provide a complete closed-loop solution for real-time estimation and control. In numerical simulations, the EKF-adaptive controller combination demonstrates, for both dynamics representations, good performance when tracking a 12.6 × 12.6 m square trajectory.

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.

Flexible effects in the joints of large space robots make their real-time operation a challenging task, especially when accurate endpoint positioning is required. The problem is further aggravated when the flexible joint stiffness matrix is not well known. This paper discusses the application of a model reference adaptive control (MRAC) composite system for tracking the endpoint of a flexible joint space robotic manipulator. The composite control scheme consists in a flexible control term designed to damp the joint vibrations plus a Transpose Jacobian rigid control term for which the control gains are adapted using a novel direct MRAC adaptation law. Numerical simulations show that the adaptive composite controller can maintain stability and good tracking performance despite significant uncertainties in the joint stiffness coefficients. Nomenclature ) , ( q q C & r = rigid centrifugal and Coriolis matrix x e , y e = model reference endpoint position error, ref x – x and ref y – y i I = inertia of link i , n i , , 1 K = m J = motor inertia matrix i m J = inertia of motor i , n

An adaptive control scheme is proposed for tracking a 12.6 − 12.6 m square trajectory by the endpoint of a two-link rigid joint space robot. The adaptive controller is based on the classical Transpose Jacobian control law where the controller gains are adapted using a modified version of the Simple Adaptive Control (SAC) adaptation law. The formal Lyapunov proof of stability for the adaptive control system is derived. Simulation results show that the proposed adaptive control methodology is a promising concept when applied to space robots and yields improved tracking results compared to a nonadaptive control strategy.

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.

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.

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.

This paper presents a model of force interaction between the two-arm robot and a load handled by the robot. The model is applied to dual force-position control of a two-arm robot. Since position control and force control are closely interrelated, the design and implementation of such a hybrid (force + position) control system is a comlex task. When two-arm manipulators are used to handle objects, a force control system has to be employed in order to protect the body from slippage out of grasp. The force control works simultaneously with a position control. A multi-level adaptive control system based on a reference model is presented in this paper. A simulation study has been performed to evaluate the performance of the proposed control scheme.

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].

The end effector of a free-flying flexible space robot is tracked along a planar trajectory using inverse flexible dynamics control (IFDC) and fuzzy logic system adaptive control (FLSAC) strategies for comparison. The robot is required to track its end effector between two points in two-dimensional space while maintaining the orbiting rigid spacecraft at a fixed position in space. Simulation results show the IFDC strategy produces a trajectory that fails to reach its commanded final position by a large margin in both coordinates. The FLSAC strategy produces a more accurate trajectory closer to the commanded final position in both coordinates but at a greater computational time burden. Sporadic high magnitude driving torque “spikes” are experienced similar to the “bursting phenomenon” encountered with adaptive control systems. Spacecraft reactive translation and attitude disturbance is minimal in each case but slightly greater for IFDC. Notation

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.

Tracking the end effector of a two-link flexible robot is simulated using control strategies with an inverse dynamics robot model and Jacobian transpose control law. Results are presented for linear quadratic Gaussian (LQG) dynamic regulator with extended Kalman filter (EKF); LQG with fuzzy logic adaptive EKF (FLAEKF); LQG with EKF and FLAEKF combined with fuzzy logic system (FLS) vibration suppression. In general, FLS vibration suppression overrides noise filtering in achieving tracking accuracy. In comparison with classical PID control or even with more advanced adaptive control strategies FLS vibration suppression gives better trajectory tracking while execution time remains acceptable.

Tracking a flexible robot is simulated using nonadaptive and fuzzy logic system (FLS) adaptive control strategies with noncollocated joint rotation sensors then with a position sensor located 0.5m, 2.25m and 4.0m from the end effector. Collocated joint rotation sensors satisfy hyperstability conditions but fail to capture nonminimum phase (NMP) response that causes control action delay. A position sensor noncollocated at the end effector captures NMP response. Results are poor for nonadaptive control with a collocated rotation sensor and a noncollocated end effector position sensor but very good results are achieved for FLS adaptive control. For noncollocated sensors at 0.5m, 2.25m and 4.0m from the end effector, tracking control results for both strategies are similar but drastically deviate from the commanded trajectory as the sensor gets closer to the elbow joint. FLS adaptive control is most effective for a sensor located either at the elbow joint or end effector.

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.

End effector tracking of a two-link flexible robot is simulated using a linear quadratic Gaussian (LQG) dynamic regulator with an extended Kalman filter (EKF), a LQG with fuzzy logic adaptive EKF (FLAEKF), LQG with an EKF and a FLAEKF combined with time delays in the feedback loop to model nonminimum phase (NMP) response for a sensor noncollocated at the end effector and in the feed forward loop for corrective control action. A fuzzy logic system (FLS) vibration suppression control strategy is simulated for comparison. Results demonstrate FLS adaptive vibration suppression produces greater tracking accuracy than an EKF, FLAEKF or corrective time delays. In comparison with classical FID control or even with more advanced adaptive control strategies FLS vibration suppression gives better tracking control while execution time remains acceptable.

†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, for position control. This paper presents a comparison of fuzzy logic system (FLS) controllers to determine an optimal design for tracking the endpoint of a two-link flexible robot. The dominant assumed mode of vibration for an EulerBernoulli cantilever beam is coupled with the nonlinear dynamics for rigid links to form an Euler-Lagrange inverse flexible dynamics robot model. A Jacobian transpose control law is adapted by a FLS with link deformation inputs and a single variable output. Simulation results for three, five, seven and nine triangular and Gaussian membership functions are compared to identify an optimal design for best tracking control and least computation time. Results show the optimal choice of FLS is three triangular membership functions at 14 min 51 sec tracking time.

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. The methodology and experiments presented in this paper has been developed and tested for the autonomous Unmanned Aerial Vehicle (UAV).

rigid Euler-Lagrange dynamics model but are extended here to a flexible dynamics model based on the first assumed mode calculated using Euler-Bernoulli pinnedpinned beam theory. A square trajectory presents an ideal case for studying intense vibration effects and their control at the four orthogonal direction switches. 7 Operational problems with robots in space relate to several factors. One most important is the structural flexibility problem of a robot manipulator and subsequently significant difficulties with its control systems, especially, position control. Typically, small elastic vibrations of the links coupled with their large rigid link rotations and non-linear dynamics primarily cause these difficulties. This paper presents a control scheme for positioning the endpoint of a two-link robot manipulator modeled with assumed modes flexible dynamics while tracking a square trajectory. The dominant assumed mode vibration is derived for an Euler-Bernoulli pinned-pinned beam to model link flexibility then, coupled with nonlinear dynamics for large rotations of rigid links to formulate an EulerLagrange inverse dynamics robot model. A control law derived using the Jacobian transpose provides joint actuation for both rigid and flexible dynamics. Comparison of output trajectories shows some differences in control performance between the rigid and flexible dynamics models with the transient response characteristics demonstrating a reduction in maximum overshoot for the flexible dynamics over the rigid dynamics models but a lengthier settling time with a consistent albeit slight oscillation about the entire desired steady-state ttrajectory .