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

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.

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.

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

This paper presents a comparative study of four control strategies for a flexible joint space manipulator. The considered control strategies consist in the classical Slotine and Li algorithm, a simple proportional derivative controller, a singular perturbation-based controller and a nonlinear backstepping controller. All control methodologies are assessed in numerical simulations for endpoint positioning while tracking a 12.6 × 12.6 m square trajectory by a two-link flexible joint space robot, thus providing a common framework to compare the respective performance of the considered control schemes. Simulation results indicate that controlling both nonlinearities and joint flexibility effects improve the closed-loop behavior of the space robot where the control of nonlinearities is of greater importance in the sense that it provides the most significant improvements. While each control scheme tracks the trajectory with a different degree of accuracy, the best performance for this particular application is obtained with the nonlinear backstepping control 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 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 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.

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 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.

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.

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 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.

This paper describes the application of Model Reference Adaptive Control (MRAC) to a conventional two-link manipulator arm2. This scheme includes an algorithm for compensating a non-linear term in the dynamic equations and for decoupling the dynamic interaction between the links. In the first part of this paper, this theory is briefly introduced, and its efficacy in controlling manipulators is tested. The second part deals with the development of a mathematical model for a flexible two-link manipulator, and then the application of the above MRAC strategy to this flexible manipulator arm. The performance of this model was compared with that of the rigid model, as a means of evaluating the suitability of MRAC for flexible manipulators. Both schemes were tested using a computer simulation method.The simulation results indicate that Adaptive Control holds promise for the control of flexible manipulators.