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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are two popular control methods widely used in haptic controller design. First, is impedance control – the operator motion input is measured, and then, the reaction force is fed back to the operator. The alternative method is admittance control-forces exerted by operator are measured and positions are fed back to the operator. Both, impedance and admittance control are also two basic methods for interacting with a virtual environment. In this paper, several experiments were performed to evaluate the suitability of impedance control with force feedback for haptic interface development. The difference between conventional application of impedance control in robot motion control and its application in haptic interface development is investigated. Open loop impedance control methodology is implemented for static case and a general-purpose robot under open loop impedance control was developed as a haptic device, while a closed loop model based impedance control was used for haptic controller design in both static and dynamic case. The factors that could affect to the performance of a haptic interface are also investigated experimentally using parametric studies. The experiments were carried out using an experimental setup. Experimental results for 1 DOF rotational motion and 2 DOF planar translational motion systems are presented. Other experimental results are also shown and discussed.

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

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

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

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

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

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

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.

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.

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.

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.

Torque saturation of DC motors of the wheels of mobile robots is one of the main difficulties during climbing hills. A two-DC motor-driven wheels mobile robot is used in the present work to attempt crossing a ditch-like hindrance using predictive control. The proposed predictive control algorithm is compared with the PID control and the open-loop control. Experimental examination of energy optimization algorithm for mobile robots is presented. The experimental results showed a good agreement with the simulation results confirming the capability of the predictive control to avoid torque saturation and indicating a noticeable reduction in the energy consumption. Additionally, a theoretical parametric study of the predictive control is presented. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot.

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.

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.

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.

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

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.

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.

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.

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