Surface temperature control of a thin aluminium plate were investigated using closed loop control approach implemented using inverse problem. The one-dimensional model with periodic boundary condition was solved using the Laplace transform and both direct problem and inverse problem transfer functions were obtained. The resulting transfer functions were expanded using Zero-Pole expansion to obtain a finite order polynomial transfer function. Simulation results for closed loop control using fractional controllers (FOPIλ, FOPDμ, and FOPIDμ) were evaluated.

Robots mounted on an unmanned chaser satellite could be used for performing rendezvous and grasping manoeuvres in order to repair satellites or remove space debris from orbit. Use of manipulators for such purposes is challenging, since the performed task need to be done autonomously, accurately and with high level of robustness. During manoeuvres high disturbances might appear e.g. due to contact between the manipulator arm end-effector and the target spacecraft. In this paper an approach for validation of the robotic subsystem during chaser rendezvous and grasping manoeuvre has been shown. Two type of testbed systems were used: planar air-bearing microgravity simulators and a test-bed system with industrial robots. The proposed approach took advantage of possible replacement of particular subsystem in reference model or reference hardware in specified test-bed system. The list of tests performed are included in the paper.

In this paper the temperature at one side of a plate is used to control in closed loop the temperature on the opposite side of the plate. To solve this problem, Laplace transform is used to obtain the quadrupole model of the direct heat equation and the analytical solution for the transfer function for the inverse problem. The resulting hyperbolic functions are approximated by Taylor expansions to facilitate the real-time closed loop temperature control formulation. Simulation results illustrate the advantages and permit to identify the limitations of using inverse problem to closed loop control temperature of a plate.

Planned active debris removal and on-orbit servicing missions require capabilities for capturing objects on Earth’s orbit, e.g., by the use of a manipulator. In this paper we demonstrate the application of a trajectory optimization algorithm for free-floating satellite-manipulator systems in two cases: a planar system with 2 degrees of freedom manipulator and a spatial system with a manipulator having four degrees of freedom. For the case with planar system, results of experiments performed on an air-bearing microgravity simulator are shown. Quadratic norm connected with the power consumption of manipulator motors has been used as a cost functional that is minimized. Optimal trajectories are compared with straight-line trajectories and it is shown that the optimization allows reduction of the power use of manipulator motors (for the planar system 30 trajectories based on randomly selected initial and final end-effector positions were analysed and the cost functional was, on average, reduced by 49.4%). The presented method could be modified by using cost functional that would, e.g., minimize disturbance on the satellite.

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.

Autonomous robots are required to avoid the obstacles during navigation. For this purpose unknown and unexpected obstacles have to be detected during motion. The proposed approach uses particle filters to process sensors data and estimate relative position of the robot with regard to the obstacles and to the goal. These relative position estimations are inputs to the velocity potential field approach for obtaining time varying velocity commands for the robot to avoid all obstacles and reach the goal.

This paper presents 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 the position and velocity determination by using INS and GPS. The measurement results from INS and GPS sensors are fused by using Kalman filter. Dilution of Precision (DOP) technique is used to select a combination of satellites to be used as measurement data. Two implementations of Kalman filter, feedforward and feedback are used. The experiment shows that the selection of the satellites affects the measurements. The methodology and experiments presented in this paper has been developed and tested for the autonomous Unmanned Aerial Vehicle (UAV).

Presents a method of sensor fusion based on adaptive fuzzy Kalman filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and inertial navigation system (INS) for autonomous mobile vehicles. The presented method has been validated in a 3-D environment and is of particular importance for guidance, navigation, and control of flying vehicles. The extended Kalman filter (EKF) and the noise characteristic have been modified using a fuzzy logic adaptive system and compared with the performance of the regular EKF. It has been demonstrated that the fuzzy adaptive Kalman filter gives better results (more accurate) than the EKF.

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

This paper presents the algorithm for on-line estimation of the optimal gain of the Kalman filter applied to a tactile sensor signals when the structure of the signal model is known exactly, but the signal to noise ratio is unknown. A first order spectrum of a pure signal and white Gaussian measurement noise have been assumed. The proposed adaptation algorithm has been examined for various spectra of the signal and for various signal to noise ratios. The effect of the length of an adaptation step on the convergence properties of the algorithm and on errors of the pure signal estimation has also been tested. The presented considerations might be helpful for designers who synthesize optimal linear digital filters of sensor's signals in the case of unknown signal to noise ratio. Although that particular algorithm has been applied for stationary signals, it can also be used successfully for time variant sensor's signals when the signal to noise ratio varies very slowly in comparison to the length of adaptation step. The method for the best choice of the adaptation step for the time variant sensor's signals has been proposed.