Path Following Methods

The calculation of inverse dynamics (ID) solutions is widely used to examine potential injury risks and sources for performance enhancement. The results of these calculations are influenced, among others, by the chosen set of body segment inertia parameters (BSIP). While throwing movements are frequently analyzed via ID and there exists a broad variety of BSIP models, the influence of the BSIP sets on the outcome is not well examined. Therefore, the aim of this study was to clarify the influence of different BSIP sets on the modelling results in javelin throwing. For this purpose the kinematics of ten male javelin throwers were recorded. Six available models were used to estimate the BSIP values of the upper limp for each thrower. The chosen BSIP model had large influence on the derived BSIP parameters which showed variations between 8% and 120%. Also, the maximum net joint moment varied between 6% and 21%. Hence, our study suggests that for modelling joint kinetics in throwing movements the model should be chosen carefully.

We present the petrographical and geochemical study of the Bódvaszilas Sandstone Formation from the Rudabánya iron ore open pit. The work is linked to the current iron and base metal exploration in Rudabánya. According to our examinations the pore-and vein-filling carbonate cement of the Bódvaszilas Sandstone Formation is presumably early diagenetic. According to the concavo-convex grain contacts and the synthaxial quartz overgrowths on monocrystalline quartz grains, the formation underwent deep burial diagenesis. Base metal sulphides dominantly occur in the cover formations of Bódvaszilas Sandstone, appear only in the fracture zones of the Bódvaszilas Sandstone, as the permeability of the formation was low, therefore the metal bringing solutions were able to move mainly along the fractures. The chemical composition and crystal structure related properties of the "acidic sparry iron ore/cream-spar" from Bódvaszilas Sandstone is described for the first time. These carbonates are intermediate solid solutions between siderite and magnesite. The "cream-spar" was formed simultaneously with the metasomatic "sparry iron ore" by the partial or complete Fe replacement of the diagenetic dolomite cement and carbonate layers of the Bódvaszilas Sandstone Formation. Cassiterite has been detected first time in Rudabánya. According to its crystal habit is not detrital, but formed here, and it is present in a "cream-spar" section in the Bódvaszilas Sandstone.

How to initialize an algorithm to solve an optimization problem is of great theoretical and practical importance. In the simplex method for linear programming this issue is resolved by either the two-phase approach or using the so-called big M technique. In the interior point method, there is a more elegant way to deal with the initialization problem, viz. the self-dual embedding technique proposed by Ye, Todd and Mizuno 30]. For linear programming this technique makes it possible to identify an optimal solution or conclude the problem to be infeasible/unbounded by solving its embedded self-dual problem. The embedded self-dual problem has a trivial initial solution and has the same structure as the original problem. Hence, it eliminates the need to consider the initialization problem at all. In this paper, we extend this approach to solve general conic convex programming, including semide nite programming. Since a nonlinear conic convex programming problem may lack the so-called strict complementarity property, it causes di culties in identifying solutions for the original problem, based on solutions for the embedded self-dual system. We provide numerous examples from semide nite programming to illustrate various possibilities which have no analogue in the linear programming case.

In this paper we present the results of a numerical investigation of the dynamics of a model of a suspended railway wheelset in the speed range between 0 and 180 km h -1 . The wheel rolls on a straight and horizontal track unaffected by external torques. A nonlinear relation between the creepage and the creep forces in the ideal wheel rail contact point is used. The effect of flange contact is modelled by a very stiff spring with a dead band. The suspension elements have linear characteristics, and the wheel profile is assumed to be conical. All other parameters than the speed are kept constant. Both symmetric and asymmetric oscillations and chaotic motion are found. The results are presented as bifurcation diagrams, time series and Poincaré section plots. We apply bifurcation and path following routines to obtain the results. In the last chapter we examine one of the chaotic regions with the help of symbolic dynamics.

In recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. To the best of our knowledge, no research to date has been conducted to assist network forensics investigators and cloud service providers in finding an optimal method for investigation of network vulnerabilities found in cloud networks. To this end and in this paper, the state-of-the-art C-NFMs are classified and analyzed based on the cloud network perspective using SWOT analysis. It implies that C-NFMs have a suitable impact on cloud network, which further requires for reformation to ensure its applicability in cloud networks.

With the anticipated explosion of small unmanned aerial vehicles, it is highly likely that operators will be controlling fleets of autonomous vehicles. To fulfill the promise of autonomy, vehicle operators will not be concerned with manual control of the vehicle; instead, they will deal with the overall mission. Furthermore, the one operator to many vehicles is becoming a constant meme with various industries including package delivery, search and rescue, and utility companies. In order for an operator to concurrently control several vehicles, his station must look and behave very differently than the current ground control station instantiations. Furthermore, the vehicle will have to be much more autonomous, especially during non-normal operations, in order to accommodate the knowledge deficit or the information overload of the operator in charge of several vehicles. The expected usage increase of small drones requires presenting the operational information generated by a fleet of heterogeneous autonomous agents to an operator. NASA Langley Research Center's Autonomy Incubator has brought together researchers in various disciplines including controls, trajectory planning, systems engineering, and human factors to develop an integrated system to study autonomy issues. The initial human factors effort is focusing on mission displays that would give an operator the overall status of all autonomous agents involved in the current mission. This paper will discuss the specifics of the mission displays for operators controlling several vehicles.

Causal inference is known to be very challenging when only observational data are available. Randomized experiments are often costly and impractical and in instrumental variable regression the number of instruments has to exceed the number of causal predictors. It was recently shown in Peters, Bühlmann and Meinshausen (2016) (J. R. Stat. Soc. Ser. B. Stat. Methodol. 78 947-1012) that causal inference for the full model is possible when data from distinct observational environments are available, exploiting that the conditional distribution of a response variable is invariant under the correct causal model. Two shortcomings of such an approach are the high computational effort for large-scale data and the assumed absence of hidden confounders. Here, we show that these two shortcomings can be addressed if one is willing to make a more restrictive assumption on the type of interventions that generate different environments. Thereby, we look at a different notion of invariance, namely inner-product invariance. By avoiding a computationally cumbersome reverse-engineering approach such as in Peters, Bühlmann and Meinshausen (2016), it allows for large-scale causal inference in linear structural equation models. We discuss identifiability conditions for the causal parameter and derive asymptotic confidence intervals in the low-dimensional setting. In the case of nonidentifiability, we show that the solution set of causal Dantzig has predictive guarantees under certain interventions. We derive finite-sample bounds in the high-dimensional setting and investigate its performance on simulated datasets.

We characterize the spectral behavior of a primal Schur-complement-based block diagonal preconditioner for saddle point systems, subject to low-rank modifications. This is motivated by a desire to reduce as much as possible the computational cost of matrix-vector products with the (1,1) block, while keeping the eigenvalues of the preconditioned matrix reasonably clustered. The formulation leads to a perturbed hyperbolic quadratic eigenvalue problem. We derive interlacing results, highlighting the differences between this problem and perturbed linear eigenvalue problems. As an example, we consider primal-dual interior point methods for semidefinite programs, and express the eigenvalues of the preconditioned matrix in terms of the centering parameter.

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The use of flight simulation tools to reduce the schedule, risk, and required amount of flight testing for complex aerospace systems is a well-recognized benefit of these approaches. However, some special challenges arise when one attempts to obtain these benefits for the development and operation of a research unmanned aerial vehicle (UAV) system. Research UAV systems are characterized by the need for continual checkout of experimental software and hardware. Also, flight testing can be further leveraged by complementing experimental results with flight-test validated simulation results for the same vehicle system. In this paper, flight simulation architectures for system design, integration, and operation of an experimental helicopter-based UAV are described. The chosen helicopter-based UAV platform (a Yamaha R-Max) is well instrumented: differential GPS, an inertial measurement unit, sonar altimetry, and a three-axis magnetometer. One or two generalpurpose flight processors can be utilized. Research flight test results obtained to date, including those completed in conjunction with the DARPA Software Enabled Control program, are summarized.

We consider the problem of ignition of propagating waves in one-dimensional bistable or excitable systems by an instantaneous spatially extended stimulus. Earlier we proposed a method (Idris & Biktashev, PRL, vol 101, 2008, 244101) for analytical description of the threshold conditions based on an approximation of the (center-)stable manifold of a certain critical solution. Here we generalize this method to address a wider class of excitable systems, such as multicomponent reaction-diffusion systems and systems with non-self-adjoint linearized operators, including systems with moving critical fronts and pulses. We also explore an extension of this method from a linear to a quadratic approximation of the (center-)stable manifold, resulting in some cases in a significant increase in accuracy. The applicability of the approach is demonstrated on five test problems ranging from archetypal examples such as the Zeldovich-Frank-Kamenetsky equation to near realistic examples such as the Beeler-Reuter model of cardiac excitation. While the method is analytical in nature, it is recognised that essential ingredients of the theory can be calculated explicitly only in exceptional cases, so we also describe methods suitable for calculating these ingredients numerically.

This paper proposes a differential flatness-based method for maneuvering a quadrotor so that its position follows a specified velocity vector field. Existing planning and control algorithms often give a 2D or 3D velocity vector field to be followed by a robot. However, quadrotors have complex nonlinear dynamics that make vector field following difficult, especially in aggressive maneuvering regimes. This paper exploits the differential flatness property of a quadrotor's dynamics to control its position along a given vector field. Differential flatness allows for the analytical derivation of control inputs in order to control the 12D dynamical state of the quadrotor such that the 2D or 3D position of the quadrotor follows the flow specified by a given vector field. The method is derived mathematically, and demonstrated in numerical simulations and in experiments with a quadrotor robot for three different vector fields.

This paper proposes a method for controlling a team of quadrotor micro aerial vehicles to perform agile maneuvers while holding a fixed relative formation, as well as transitioning between a sequence of formations. The objective is to coordinate the quadrotors to fly in intricate interlaced patterns, similarly to an air show demonstration team. The paper proposes a new abstraction, called a Virtual Rigid Body, which allows the quadrotors to hold relative positions while executing agile maneuvers as a group. By planning trajectories for the Virtual Rigid Body in SE(3), trajectories for each quadrotor are obtained in order to maintain the desired formation during the maneuver. The paper also proposes a method for sequencing a series of Virtual Rigid Body formations, and automatically designing collision free transitions between successive formations, while the team simultaneously executes a trajectory in SE(3). The resulting sequence of formations and transitions gives trajectories that weave intricate designs while avoiding collisions. The method is demonstrated experimentally with three KMel K500 quadrotors flying in a motion capture environment.

This paper proposes a method for controlling a team of quadrotor micro aerial vehicles to perform agile maneuvers while holding a fixed relative formation, as well as transitioning between a sequence of formations. The objective is to coordinate the quadrotors to fly in intricate interlaced patterns, similarly to an air show demonstration team. The paper proposes a new abstraction, called a Virtual Rigid Body, which allows the quadrotors to hold relative positions while executing agile maneuvers as a group. By planning trajectories for the Virtual Rigid Body in SE(3), trajectories for each quadrotor are obtained in order to maintain the desired formation during the maneuver. The paper also proposes a method for sequencing a series of Virtual Rigid Body formations, and automatically designing collision free transitions between successive formations, while the team simultaneously executes a trajectory in SE(3). The resulting sequence of formations and transitions gives trajectories that weave intricate designs while avoiding collisions. The method is demonstrated experimentally with three KMel K500 quadrotors flying in a motion capture environment.

This paper presents a path-tracking controller of a bi-steerable cybernetic car with an anti-collision behaviour. The velocity planner and the anti-collision system are fundamental modules in the architecture. The path tracking implementation uses fuzzy logic. The smoothness of the acceleration profile was one of requirements taken into account in the controller design. The anti-collision system based on Laser Range Data consists of estimating the trajectories and behaviour of surrounding objects. Simulation and experimental results are presented showing the effectiveness of the overall navigation control system.

This paper describes a visual servo control scheme for an outdoor autonomous airship in path following tasks. The visual based guidance takes full authority of the robotic airship and it is based on lines as image features, extracted from an on-board camera. An optimal controller is designed using the dynamic model of the system, which tackles the dynamics of the airship and the dynamics of the vision process. To prevent from losing image features during camera motion, to deal with noisy output measurements and modeling inaccuracies, a Kalman estimator was employed. The approach is validated through simulations using the AURORA project underactuaded airship.

Cet article présente les résultats d'études expérimentales effectuées à l'oedomètre avec imposition de succion par la méthode osmotique sur des matériaux gonflants compacté lâche et naturel dense. Plusieurs cycles de séchage/humidification ont été appliqués sur deux matériaux sous trois faibles charges mécaniques constantes. Pendant ces cycles, les éprouvettes manifestent un retrait cumulé pour le sol lâche et un gonflement cumulé pour le sol dense. Les résultats montrent que dans les deux cas, les déformations volumiques convergent vers un état d'équilibre où le sol présente un comportement réversible. A la fin des cycles de succion, un cycle de chargement/déchargement a été effectué sous trois succions différentes. Les valeurs de la pression de préconsolidation p 0 (s), de l'indice de compression vierge λ(s) et de l'indice de compression élastique κ dépendent directement des chemins de contrainte suivis. L'ensemble des résultats expérimentaux permet de déterminer les surfaces de charge : la surface de chargement-effondrement (LC : Loading Collapse) et la surface de comportement saturé (SCS). Les surfaces de charge SCS et LC se superposent à la fin des cycles de succion pour les deux sols.

We highlight an essential difference between path-following and reference-tracking for non-minimum phase systems. It is well-known that in the reference-tracking, for non-minimum phase systems, there exists a fundamental performance limitation in terms of a lower bound on the L 2 -norm of the tracking error, even when the control effort is free. We show that this is not the case for the less stringent path-following problem, where the control objective is to force the output to follow a geometric path without a timing law assigned to it. Furthermore, the same is true even when an additional desired speed assignment is imposed.

This paper introduces a new class of convex functions namely the harmonic inverse sine trigonometric convex (HISTC) functions defined over a real vector space, which exhibits unique refinement characteristics. We provide a detailed mathematical formulation and analyze its algebraic properties. Using this refined class, we establish an identity that serves as a foundation to establish refinements of Bullen-Mercer-type inequalities. The derived results are authenticated through 2D and 3D graphs as well as numerical analysis. Additionally, we demonstrate applications of our findings by their use in analyzing means. By discussing the limiting cases, we prove that the main results are general comparing those in the existing literature.

The advent of autonomous vehicles necessitates a redefinition of road safety regulations, considering a controller can possess better driving skills than an average person. The work presented here partly focuses on a vehicle dynamics model development to help imitate and control vehicle drifting maneuvers. As we see, a professional driver drifting through the traffic while keeping the car safe, it can be utilized to avoid accidents at high speeds, if required. Although drifting can produce higher yaw rates than the regular driving regime, these control capabilities have not yet been exploited in the current automotive control systems. Therefore, this report focuses on developing a vehicle dynamics model to simulate the drifting of an autonomous vehicle that utilizes this high yaw rate property. Drifting control capabilities will increase the vehicle's ability to avoid collision scenarios where higher than typical yaw rates are required.
The other part of the report uses vehicle kinematics equations to generate feasible path planning algorithms for any autonomous vehicle. If we constrain these vehicle kinematics equations for linearly varying curvature of the path, they are called Clothoid curves. This linearly varying curvature is analogous to having a continuous lateral acceleration on the vehicle while cornering, i.e., avoiding jerks. Here, Clothoids are used for the interpolation of waypoints along the path. A mission planner defines waypoints a few meters apart; then, GPS coordinates are used to check whether the vehicle is following these waypoints correctly. Therefore, waypoint interpolation is required for the continuous feed of track coordinates to the controller to make faster corrections for the cross-track error and not to wait every time for a low-rate GPS signal. Also, the Clothoid curves are used in road construction, thus also providing the ability to create a road model along with a vehicle model which can further be used for better state estimation.

Many authors suggest that is necessary to include the most important episodes from physics history in teaching and learning in order to give students some ideas about the nature of science. The pendulum-related aspects are considered as very adequate for that purpose. Unfortunately, when some physics textbook authors present historical information, it is frequently incomplete and even false. In the first part of this article, we present and comment important results of Huygens' analysis of two problems related to pendular and circular motions. These results were published in his work about centrifugal force "De vi centrifuga". In the second part, we consider the destiny and different treatments of his results in today physics teaching and learning, noting that Huygens' contribution is never mentioned. These treatments go from (1) testing students (and teachers') ideas about tension force in pendular motion to (2) mathematical derivation of Huygens' results or posing his problems to students. Finally, we briefly suggest how original results might be used to give students an idea about development of mathematical tools in physics and to explore students' creative experimental thinking.

The Karush-Kuhn-Tucker (KKT) equations provide both necessary and sufficient conditions for optimality of convex linearly-constrained quadratic programming problems. These equations consist of both linear equations (the primal and dual feasibility constraints) and nonlinear equations (the complementary slackness conditions, specifying that the product of each complementary pair of variables is zero). While for many years complementary pivoting algorithms to solve the KKT equations were the state of the art for quadratic programming problems, more recently there has been much progress in the development of interior-point methods. Path-following interior-point algorithms proceed by relaxing the complementarity constraints of the KKT equations, specifying that each complementary product equals a positive parameter µ , which is successively reduced at each iteration. The solutions of the relaxed KKT equations constitute the "central path", parameterized by µ , converging to the optimum as µ approaches zero. For each µ , these equations are typically "solved" by one iteration of Newton's method, in which the nonlinear (complementarity) equations are approximated by linear equations and the resulting system of linear equations are then solved. The resulting sequence of points satisfy the primal and dual feasibility constraints exactly, but the (relaxed) complementarity conditions only approximately. An alternate approach, the so-called "monomial method", treats the complementarity constraints directly, and approximates the linear equations by monomial equations in order to obtain a system of equations which, after a logarithmic transformation, yields a linear system of equations. The sequence of points which results from solving these log-linear equations therefore satisfy the (relaxed) complementarity equations exactly, but the primal and dual feasibility constraints only approximately. This paper discusses this new path-following algorithm for quadratic programming, and evaluates its performance by presenting the results of some numerical experiments.

This paper proposes the use of Extended Kalman Filter (EKF) state estimator to improve the vehicle features and diagnostic tuning. The EKF algorithm is used to estimate the vehicle state variables such as position, velocity, and acceleration. This information is then used to enhance the vehicle's features and diagnostic tuning, leading to improved performance and safety.

We present a multistage model for allocation of financial resources to bond indices in different currencies. The model was tested on historical data of interest and exchange rates. We compare a two-stage and a three-stage stochastic programming model from a financial performance point of view. For solving two-stage and three-stage stochastic programs the interior point method (IPM) in the frame of the primal-dual path following formulation is used. An application of the Birge and Qi factorization to the IPM allows decomposition of large linear system to smaller blocks allowing thus to solve it in parallel. The parallel code is written in the Fortran programming language, using the Message Passing Interface (MPI) for communication. Parallel and financial performance is illustrated on experiments executed on the IBM 1350 Linux cluster.

This paper presents a platform-independent framework for autonomous navigation of an intelligent vehicle. The framework consists of three integrated modules, namely; waypoint navigation, obstacle localization and path planning. Each module has been individually validated based on experiments with a real intelligent vehicle. For waypoint navigation, we propose the use of Google Earth for generation of reference waypoints and a simple bias subtraction method for GPS calibration. Based on the autonomous navigation experiments, this method yields a more stable navigation path compared to the use of GPS-generated waypoints and translational error can be efficiently eliminated. For obstacle localization, we develop a feature-based approach for obstacle detection and map generation based on the use of compactness measure and perspective projections. With an integrated use of a camera, digital compass, and GPS, static obstacles of a known dimension, along with their positions and orientations on the road can be calculated in real-time while the vehicle is travelling. Based on the derived information, an overhead-view obstacle map is generated to provide an internal representation of the road. The experiment on an unmarked road shows that the estimation of an obstacle can be achieved with maximum errors of 1.4 degree, 15 cm and 12 cm in angle, depth and lateral positions, respectively. Based on the obstacle map and the Google Earth waypoints, artificial potential field is adopted for collision-free path generation.

We describe the secondary xylem anatomy of three types of roots produced by Laguncularia racemosa (cable roots, pneumatophores and pneumathodes). Formation of lateral roots (pneumathodes) by pneumatophores causes structural modifications in their secondary xylem, like circular vessels, which may have consequences for water transport. These modifications are not found in the secondary xylem of cable roots. The path followed by water in the secondary xylem of cable roots and pneumatophores was established by infusion with safranin and by mold-casting with polystyrene. Hydraulic (k h ) and specific (k s ) conductivity of the different root types was also determined. We found that the circular vessels at the junctions did not impair k h or k s values under well-watered conditions, but perhaps the junctions limit water loss when the air-exposed pneumatophores dry out.

Abstract. The paper demonstrates the use of self-oscillation identification method for heading controller tuning of the autonomous unmanned surface vehicle (USV) Charlie. In short, the theory behind self-oscillation identification method is addressed and a model based controller design is described. Two controllers are implemented on the vehicle: controller with Euler backward differentiator for yaw rate calculation, Kalman filter based yaw rate estimator. The Kalman filter is also tuned on the basis of the identified model. The ...

This article describes a navigation algorithm for a mobile robot, endowed with odometry and a range sensor, moving in a structured indoor environment, without previous knowledge of its map. The objective is to discover the exit and obtain the shortest path from the initial point. The navigation algorithm adapts to changes in the environment. Results obtained with a mobile robot, endowed with a laser scanner, are presented.

We consider a family of linear optimization problems over the ndimensional Klee-Minty cube and show that the central path may visit all of its vertices in the same order as simplex methods do. This is achieved by carefully adding an exponential number of redundant constraints that forces the central path to take at least 2 n -2 sharp turns. This fact suggests that any feasible path-following interior-point method will take at least O(2 n ) iterations to solve this problem, while in practice typically only a few iterations, e.g., 50, suffices to obtain a high quality solution. Thus, the construction potentially exhibits the worst-case iterationcomplexity known to date which almost matches the theoretical iteration-complexity bound for this type of methods. In addition, this construction gives a counterexample to a conjecture that the total central path curvature is O(n).

This paper presents the design of a controller that allows a four-rotor helicopter to track a desired trajectory in the 3D space. To this aim, a dynamic model obtained from Euler-Lagrange equations is used to describe the robot. Such model is represented by numerical methods and, from this approach, the control actions for the operation of the system are obtained. The proposed controller is easy implementation and presents a good performance. Keywords— Trajectory Tracking; Quadrotor; Numerical Methods; Linear Algebra. Resumo— Este artigo apresenta o projeto de um controlador que permite a um helicóptero de quatro rotores rastrear uma trajetória desejada no espaço 3D. Para este objetivo, um modelo dinâmico obtido a partir das equações de Euler-Lagrange é usado para descrever o robô. Esse modelo é representado por meio de métodos numéricos e, a partir desta abordagem, as acções de controle para a operação do sistema, são obtidos. O controlador proposto é simples e apresenta um bom des...

The paper is dedicated to the study of strong duality for a problem of linear copositive programming. Based on the recently introduced concept of the set of normalized immobile indices, an extended dual problem is deduced. The dual problem satisfies the strong duality relations and does not require any additional regularity assumptions such as constraint qualifications. The main difference with the previously obtained results consists in the fact that now the extended dual problem uses neither the immobile indices themselves nor the explicit information about the convex hull of these indices. The strong duality formulations presented in the paper have similar structure and properties as that proposed in the works of M. Ramana, L. Tuncel, and H. Wolkovicz, for semidefinite programming, but are obtained using different techniques.

This paper proposes a Particle-Filter approach and a set of motion strategies to cooperatively localize a team of three robots. The allocated mission consists on the path following of a closed trajectory and obstacle avoidance in isolated and unstructured scenarios. The localization methodology required for the correct path following relies on distance and orientation measurements among the robots and the robots and a fixed active beacon. Simulation results are presented.

This paper describes a navigation architecture for mobile robots, structured as a set of nested control loops whose depth is related to their knowledge of the environment and the ability to drive the actuator, and involving as well competing behaviors that will ultimately generate the robot motion. The architecture has been successfully used on a mobile platform to support three-dimensional environment reconstruction tasks. The architecture may be classified as belonging to the hybrid type but made up of hybrid elements as well, allowing virtually any level of input awareness and ranging from high-level task planning to direct motion commands issued by external user or applications. A monitorized data path ensures the construction of the most adequate and safe motion, as well as an unlimited set of behaviors depending on the already known and perceived environment. Added concepts of path recovering and assisted navigation fulfill the demands for the three-dimensional acquisition sch...

This paper proposes a Particle-Filter approach and a set of motion strategies to cooperatively localize a team of three robots. The allocated mission consists on the path following of a closed trajectory and obstacle avoidance in isolated and unstructured scenarios. The localization methodology required for the correct path following relies on distance and orientation measurements among the robots and the robots and a fixed active beacon. Simulation results are presented.

The capacity of re-using previously acquired skills can greatly enhance robots' learning speed and behavioral complexity. 'Intrinsically Motivated Reinforcement Learning (IMRL)' is a framework that exploits this idea and proposes to build agents capable of solving several specific tasks by assembling general-purpose building-block behaviors ('skills') previously acquired on the basis of 'intrinsic motivations'. This paper proposes a novel neural-network hierarchical reinforcement-learning architecture which exploits 'evolutionary robotics (ER)' techniques that not only allow tackling important limits of IMRL, as shown in previous papers, but they also allow investigating two other important issues, namely: (1) the optimization of the parameters that regulate the architecture's learning processes; (2) the optimization of the time the architecture dedicates to the acquisition of the skills' repertoire. These two issues are investigated here through a simulated robot engaged in solving compositional path-following navigation tasks. The results obtained indicate that the proposed approach allows obtaining a remarkable improvement of performance of the architecture, while at the same time decreasing the time the system needs to learn the skills, with respect to cases where hand-tuned parameters are used.

In this paper we present an advanced method of obstacle avoidance for a laser based intelligent wheelchair using optimized Bayesian neural networks. Three neural networks are designed for three separate sub-tasks: passing through a door way, corridor and wall following and general obstacle avoidance. The accurate usable accessible space is determined by including the actual wheelchair dimensions in a real-time map used as inputs to each networks. Data acquisitions are performed separately to collect the patterns required for specified sub-tasks. Bayesian frame work is used to determine the optimal neural network structure in each case. Then these networks are trained under the supervision of Bayesian rule. Experiment results showed that compare to the VFH algorithm our neural networks navigated a smoother path following a near optimum trajectory.

This thesis is a research for localization problem of an autonomous vineyard spraying robot. The project is motivated by the lack of manpower to perform the vineyard spraying task and the exposure to hazardous pesticides during the spraying. Localization is a fundamental problem in the field of autonomous mobile robotics. In order to allow basic autonomous functionality of a robot, Position and Path-Planning control loops are needed. Both loops require knowledge of the accurate state (position orientation and velocity) of the robot This research focus in modeling the kinematic model of the robot. The model will be later use for controller design and model based state estimation (filtering). Second, we research how to filter a noisy raw sensor data and the integration of data from all sensors (DGPS, IMU and Vision). The research focus on statistical and probability theory such as Bayesian estimator and Kalman filter for filtering and data fusion. The simulations of Data Fusion presen...