Luigi Spedicato

A New Strategy for Spatial Reconstruction of Orthogonal Planes Using a Rotating Array of Ultrasonic Sensors

In this paper, the authors present a new strategy for accurately reconstructing an L-shaped obstacle such as some wooden panels opportunely connected so as to form a right angle. The mechatronics scanning system consists of four inexpensive ultrasonic sensors moved in three-dimensional (3-D) space by means of a digital motor. The motor rotation is controlled in order to point the sensor array at the target and to obtain distance measurements for each shaft position. Ultrasonic distance sensors propagate large beams and feel the significant effect of multiple reflections. For the sake of excluding all misrepresented distance values at the intersection of the planes, the proposed approach uses powerful mathematical tools together with a physical indicator based on the reflected signal energy. The Fuzzy C-Means (FCM) classification allows partitioning a data set, and the introduced physical indicator is able to select the specific cluster corresponding to the spurious distances. Each remaining cluster permits to calculate the equation of a plane because it is referred to the distance values deriving from a direct reflection. These distances are then transformed considering the sensors directivity and the direction of reflection so as to obtain two sets of 3-D points. Finally, the reconstruction of each plane is achieved by the RANdom SAmple Consensus (RANSAC) in such a way as to better fit these points. The details of this strategy and the experimental tests are shown, demonstrating the applicability and the good results.

3D Reconstruction of L-shaped surfaces using a rotating array of ultrasonic sensors

In this paper, the authors present a novel strategy applied to mechatronics device for reconstructing quite accurately smooth orthogonal-connected surfaces. The novel approach is based on the Fuzzy C-Means (FCM) classification and the RANdom SAmple Consensus (RANSAC) fitting. The system consists of four inexpensive ultrasonic sensors moved in the space by means of a controlled digital motor in order to obtain distance measurements for each shaft position. The automatic use of powerful mathematical tools and the physical analysis of an indicator based on the energy permit the detection of the critical region, where the output data feel the significant effect of a non-specular reflection. Experimental results are discussed demonstrating the applicability and the good results of this new strategy.

3D traversability awareness for rough terrain mobile robots

Purpose – This research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment. Design/methodology/approach – The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity. Findings – The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities....

Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications

In recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach.

Detection Analysis of Small Notches Damages Using a New Tactile Optical Device

In this paper, the experimental application of a new class of optical pressure sensors based on polydimethylsiloxane (PDMS)-Au aimed at detecting and classifying millimetric surface damages of mechanical components is developed. The device consists of a tapered bended optical fiber, where an optical signal goes across, embedded into a PDMS-gold nanocomposite material (GNM). The sensor is automatically moved for the optical scanning of surfaces by means of a high-accuracy servo motor. After moving and positioning the detector, the sensor output data are acquired and processed in such a way as to pinpoint small notches on a beam. Notches of different lengths to within a few millimeters were scanned to test the realized device capability in recognizing and characterizing very small defects. The experimental results are very encouraging; they exhibit high sensitivity and inspire the use of the sensor in multifarious applications of robotics.

Development and validation of a software tool for complex aircraft powertrains

Abstract A simulation software for the assessment of performance, costs and environmental impact of conventional and advanced configuration aircraft has been developed and validated. The software is named PLA.N.E.S. (PLAtform for New Environment-friendly Solutions), and includes a sizing routine and a mission simulator. The simulation is performed with the so-called backward paradigm, i.e. the flight conditions along the mission (altitude and speed versus time) are assumed to be known. Accordingly, the instantaneous power request of the aircraft to meet that flight mission and the corresponding instantaneous fuel consumption are calculated. In the case of advanced powertrains, it is also possible to choose different energy management strategies for the optimal control of the energy flows among engine, secondary equipment and storage systems during the mission. The components currently modeled in PLA.N.E.S. include energy converters (piston and Wankel engines, turboprop, fuel cell, etc.), energy storage systems (batteries, super-capacitors), auxiliaries and secondary power systems. The tool is designed to be integrated with a multi-objective optimization environment. In the present investigation PLA.N.E.S. has been applied to a Medium Altitude Medium Endurance (MAME) Unmanned Aerial Vehicle (UAV) as a case study to compare an experimentally validated Wankel-based powertrain with a proposed turbocharged diesel piston-prop system.

Exploratory data analysis for robot perception of room environments by means of an in-air sonar scanner.

In this paper, the authors have developed a new method for reconstructing the boundary walls of a room environment by using a mechatronic device consisting of four ultrasonic sensors rotated by a servo modular actuator. This scanning system allows to measure the times of flight in each motor position so as to explore the surrounding space detecting reflections from the boundary walls and from other static obstacles. In addition to undesired reflections, due to non-target obstacles interposed between the sensors and the target surfaces, several spurious times are observed at the corners because of multiple reflections. The Fuzzy C-Means (FCM) algorithm is used for partitioning the obtained dataset in five clusters and some considerations on the output signal energy permit to select the two subsets concerned with multipath echoes. Each remaining cluster is associated to a set of three-dimensional points by considering the directivity of the wide beam propagated. In order to discard the observations that are numerically distant from the confidence data, the three sets are filtered by means of an ellipsoid defined by the Principal Component Analysis (PCA). The best-fit planes are obtained by testing the eigenvalues and relating eigenvectors of the covariance matrix of each filtered set. Several tests are shown and discussed for appreciating the effectiveness of the described approach and they are aimed at making a robot aware of its environment.

Clustering and PCA for Reconstructing Two Perpendicular Planes Using Ultrasonic Sensors

In this paper, the authors make use of sonar transducers to detect the corner of two orthogonal panels and they propose a strategy for accurately reconstructing the surfaces. In order to point a linear array of four sensors at the desired position, the motion of a digital motor is appropriately controlled. When the sensors are directed towards the intersection between the planes, longer times of flight are observed because of multiple reflections. All the concerned distances have to be excluded and that is why an indicator based on the output signal energy is introduced. A clustering technique allows for the partitioning of the dataset in three clusters and the indicator selects the subset containing misrepresented information. The remaining distances are corrected so as to take into consideration the directivity and they permit the plotting of two sets of points in a three-dimensional space. In order to leave out the outliers, each set is filtered by means of a confidence ellipsoid which is defined by the Principal Component Analysis (PCA). The best-fit planes are obtained based on the principal directions and the variances. Experimental tests and results are shown demonstrating the effectiveness of this new approach.

A PCA-based interpretation of ultrasonic data for accurately detecting a corridor wall

This paper introduces a validated model for the ultrasonic detection by processing sensor data and it presents a new approach for evaluating the orientation of a regular wall. The method described is based on the Principal Component Analysis (PCA) which gives the opportunity to make some geometrical considerations on the effectiveness of the scanning results. Several experiments are conducted scanning a regular corridor wall and also propagating ultrasonic waves towards a wall opening. In every instance these tests permit to confirm the applicability of the new proposed strategy.

Three Different Approaches for Localization in a Corridor Environment by Means of an Ultrasonic Wide Beam

In this paper the authors present three methods to detect the position and orientation of an observer, such as a mobile robot, with respect to a corridor wall. They use an inexpensive sensor to spread a wide ultrasonic beam. The sensor is rotated by means of an accurate servomotor in order to propagate ultrasonic waves towards a regular wall. Whatever the wall material may be the scanning surface appears to be an acoustic reflector as a consequence of low air impedance. The realized device is able to give distance information in each motor position and thus permits the derivation of a set of points as a ray trace-scanner. The dataset contains points lying on a circular arc and relating to strong returns. Three different approaches are herein considered to estimate both the slope of the wall and its minimum distance from the sensor. Slope and perpendicular distance are the parameters of a target plane, which may be calculated in each observers position to predict its new location. Experimental tests and simulations are shown and discussed by scanning from different stationary locations. They allow the appreciation of the effectiveness of the proposed approaches.