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

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.

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

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

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.

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

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

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.

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.

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

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

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.

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 into controllers of each robot. In this paper the use of velocity potential approach for controlling formations of robots in crowded environments is investigated. Several physical experiments and extensive simulations tests were performed and discussed.

As the demand for field mobile robots in off-road operations increased, the need to investigate the 3D motion for mobile robots became important. One of the main difficulties in the 3D motion of a mobile robot is the torque saturation of the DC motors of the wheels that occurs while climbing hills. In the present work, off-road conditions are utilized to benefit by 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 developed algorithm is simulated and compared with the PID control and the open-loop control. The 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 robust speed control. Simulation results showed that in case of knowing the ditch dimensions ahead of time, the developed algorithm is feasible.

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.