Cross-entropy optimization for scaling factors of a fuzzy controller: a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

Discernment of bee pollen loads using computer vision and one-class classification techniques

In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types. Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.

Aerodynamic optimization of the ICE2 high-speed train nose using a genetic algorithm and metamodels

An aerodynamic optimization of the ICE 2 high-speed train nose in term of front wind action sensitivity is carried out in this paper. The nose is parametrically defined by Be?zier Curves, and a three-dimensional representation of the nose is obtained using thirty one design variables. This implies a more complete parametrization, allowing the representation of a real model. In order to perform this study a genetic algorithm (GA) is used. Using a GA involves a large number of evaluations before finding such optimal. Hence it is proposed the use of metamodels or surrogate models to replace Navier-Stokes solver and speed up the optimization process. Adaptive sampling is considered to optimize surrogate model fitting and minimize computational cost when dealing with a very large number of design parameters. The paper introduces the feasi- bility of using GA in combination with metamodels for real high-speed train geometry optimization.

Non invasive moisture measurement in agricultural fiel using a rolling spherical robot

Irrigation management in large crop fields is a very important practice. Since the farm management costs and the crop results are directly connected with the environmental moisture, water control optimization is a critical factor for agricultural practices, as well as for the planet sustainability. Usually, the crop humidity is measured through the water stress index (WSI), using imagery acquired from satellites or airplanes. Nevertheless, these tools have a significant cost, lack from availability, and dependability from the weather. Other alternative is to recover to ground tools, such as ground vehicles and even static base stations. However, they have an outstanding impact in the farming process, since they can damage the cultivation and require more human effort. As a possible solution to these issues, a rolling ground robot have been designed and developed, enabling non-invasive measurements within crop fields. This paper addresses the spherical robot system applied to intra-crop moisture measurements. Furthermore, some experiments were carried out in an early stage corn field in order to build a geo-referenced WSI map.

ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

Automatic generation of models for abdominal aortic aneurysms and intraluminal thrombus based on hexahedral meshes

To propose an automated patient-specific algorithm for the creation of accurate and smooth meshes of the aortic anatomy, to be used for evaluating rupture risk factors of abdominal aortic aneurysms (AAA). Finite element (FE) analyses and simulations require meshes to be smooth and anatomically accurate, capturing both the artery wall and the intraluminal thrombus (ILT). The two main difficulties are the modeling of the arterial bifurcations, and of the ILT, which has an arbitrary shape that is conforming to the aortic wall.

See-and-avoid quadcopter using fuzzy control optimized by cross-entropy

In this work we present an optimized fuzzy visual servoing system for obstacle avoidance using an unmanned aerial vehicle. The cross-entropy theory is used to optimise the gains of our controllers. The optimization process was made using the ROS-Gazebo 3D simulation with purposeful extensions developed for our experiments. Visual servoing is achieved through an image processing front-end that uses the Camshift algorithm to detect and track objects in the scene. Experimental flight trials using a small quadrotor were performed to validate the parameters estimated from simulation. The integration of crossentropy methods is a straightforward way to estimate optimal gains achieving excellent results when tested in real flights.

AR drone identification and navigation control at CVG-UPM

This article presents the proposal of the Computer Vision Group to the first phase of the international competition “Concurso de Ingeniería de Control 2012, Control Aut ́onomo del seguimiento de trayectorias de un vehículo cuatrirrotor”. This phase consists mainly of two parts: identifying a model and designing a trajectory controller for the AR Drone quadrotor. For the identification task, two models are proposed: a simplified model that captures only the main dynamics of the quadrotor, and a second model based on the physical laws underlying the AR Drone behavior. The trajectory controller design is based on the simplified model, whereas the physical model is used to tune the controller to attain a certain level of robust stability to model uncertainties. The controller design is simplified by the hypothesis that accurate positions sensors will be available to implement a feedback controller.

Adaptive control system based on lineal control theory for the path-following problem of a car-like mobile robot

The objective of this paper is to design a path following control system for a car-like mobile robot using classical linear control techniques, so that it adapts on-line to varying conditions during the trajectory following task. The main advantages of the proposed control structure is that well known linear control theory can be applied in calculating the PID controllers to full control requirements, while at the same time it is exible to be applied in non-linear changing conditions of the path following task. For this purpose the Frenet frame kinematic model of the robot is linearised at a varying working point that is calculated as a function of the actual velocity, the path curvature and kinematic parameters of the robot, yielding a transfer function that varies during the trajectory. The proposed controller is formed by a combination of an adaptive PID and a feed-forward controller, which varies accordingly with the working conditions and compensates the non-linearity of the system. The good features and exibility of the proposed control structure have been demonstrated through realistic simulations that include both kinematics and dynamics of the car-like robot.

I feel you: Towards affect-sensitive domotic Spoken Conversational Agents

We describe the work on infusion of emotion into limitedtask autonomous spoken conversational agents (SCAs) situated in the domestic environment, using a Need-inspired task-independentEmotion model (NEMO). In order to demonstrate the generation of a?ect through the use of the model, we describe the work of integrating it with a naturallanguage mixed-initiative HiFi-control SCA. NEMO and the host system communicates externally, removing the need for the Dialog Manager to be modi?ed as done in most existing dialog systems, in order to be adaptive. We also summarize the work on automatic a?ect prediction, namely frustration and contentment from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach.

Effects of different soil management practices on soil mechanic resistance and seed depth placement in a maize crop in Alentejo, Portugal Mediterranean region.

No tillage, minimum tillage and conventional tillage practices are commonly used in maize crops in Alentejo, affecting soil physic conditions and determining seeders performance. Seeders distribution can be evaluated in the longitudinal and vertical planes. Vertical plane is specified by seeding depth (Karayel et al., 2008). If, in one hand seeding depth uniformity is a goal for all crop establishment , in the other hand, seeders furrow openers depth control is never constant depending on soil conditions. Seed depth uniformity affects crop emergence, Liu et al. (2004) showed an higher correlation between crop productivity and emergence uniformity than with longitudinal plants distribution. Neto et al. (2007) evaluating seed depth placement by measuring maize mesocotyl length under no tillage conditions in 38 farms concluded that 20% of coefficient of variation suggests the need of improvement seeders depth control mechanisms. The objective of this study was to evaluate casual relationships and create spatial variability maps between soil mechanic resistance and vertical distribution under three different soil practices to improve seed depth uniformity.

Experimental results on multicarrier SIMO HF communications

Achieving reliable communication over HF channels is known to be challenging due to the particularly hostile propagation medium. To address this problem, diversity techniques were shown to be promising. In this paper, we demonstrate through experimental results the benefits of different diversity strategies when applied to multi-input-multi-output (MIMO) multicarrier systems. The performance gains of polarisation, space and frequency diversities are quantified using different measurement campaigns.

Lifetime maximization for beamforming applications in wireless sensor networks

Energy efficiency is a major design issue in the context of Wireless Sensor Networks (WSN). If data is to be sent to a far-away base station, collaborative beamforming by the sensors may help to dis- tribute the load among the nodes and reduce fast battery depletion. However, collaborative beamforming techniques are far from opti- mality and in many cases may be wasting more power than required. In this contribution we consider the issue of energy efficiency in beamforming applications. Using a convex optimization framework, we propose the design of a virtual beamformer that maximizes the network's lifetime while satisfying a pre-specified Quality of Service (QoS) requirement. A distributed consensus-based algorithm for the computation of the optimal beamformer is also provided

Distributed primary user identification from imprecise location information

We study a cognitive radio scenario in which the network of sec- ondary users wishes to identify which primary user, if any, is trans- mitting. To achieve this, the nodes will rely on some form of location information. In our previous work we proposed two fully distributed algorithms for this task, with and without a pre-detection step, using propagation parameters as the only source of location information. In a real distributed deployment, each node must estimate its own po- sition and/or propagation parameters. Hence, in this work we study the effect of uncertainty, or error in these estimates on the proposed distributed identification algorithms. We show that the pre-detection step significantly increases robustness against uncertainty in nodes' locations.

Location-aided distributed primary user identification in a cognitive radio scenario

We address a cognitive radio scenario, where a number of secondary users performs identification of which primary user, if any, is trans- mitting, in a distributed way and using limited location information. We propose two fully distributed algorithms: the first is a direct iden- tification scheme, and in the other a distributed sub-optimal detection based on a simplified Neyman-Pearson energy detector precedes the identification scheme. Both algorithms are studied analytically in a realistic transmission scenario, and the advantage obtained by detec- tion pre-processing is also verified via simulation. Finally, we give details of their fully distributed implementation via consensus aver- aging algorithms.