Mauro Bellone

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.

A new approach for terrain analysis in mobile robot applications

This paper presents a novel approach to detect traversable and non-traversable regions of the environment from a depth image that could enhance mobility and safety of mobile robots through integration with localization, control and planning methods. The proposed system is based on Principal Component Analysis (PCA). PCA theory provides a powerful means to analyze 3D surfaces widely used in computer vision. It can be successfully applied, as well, to increase the degree of perception in autonomous vehicles, as new generations of 3D imaging sensors, including stereo and RGB-D-cameras, are increasingly introduced. The approach described in this paper is based on the estimation of the normal vector to a local surface leading to the definition of a novel, so-called, Unevenness Point Descriptor. Experimental results, obtained from indoor and outdoor environments, are presented to validate the system. It is demonstrated that the proposed approach can be effectively used for scene segmentation and it can efficiently handle difficult scenarios, including the presence of terrain slopes.

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.

TP-Space RRT – Kinematic Path Planning of Non-Holonomic Any-Shape Vehicles

The autonomous navigation of vehicles typically combines two kinds of methods: a path is first planned, and then the robot is driven by a local obstacle-avoidance controller. The present work, which focuses on path planning, proposes an extension to the well-known rapidly-exploring random tree (RRT) algorithm to allow its integration with a trajectory parameter-space (TP-space) as an efficient method to detect collision-free, kinematically-feasible paths for arbitrarily-shaped vehicles. In contrast to original RRT, this proposal generates navigation trees, with poses as nodes, whose edges are all kinematically-feasible paths, suitable to being accurately followed by vehicles driven by pure reactive algorithms. Initial experiments demonstrate the suitability of the method with an Ackermann-steering vehicle model whose severe kinematic constraints cannot be obviated. An important result that sets this work apart from previous research is the finding that employing several families of potential trajectories to expand the tree, which can be done efficiently under the TP-space formalism, improves the optimality of the planned trajectories. A reference C++ implementation has been released as open-source.

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.

Watch Your Step! Terrain Traversability for Robot Control

Watch your step! Or perhaps, watch your wheels. Whatever the robot is, if it puts its feet, tracks, or wheels in the wrong place, it might get hurt; and as robots are quickly going from structured and completely known environments towards uncertain and unknown terrain, the surface assessment becomes an essential requirement. As a result, future mobile robots cannot neglect the evaluation of terrain’s structure, according to their driving capabilities. With the objective of filling this gap, the focus of this study was laid on terrain analysis methods, which can be used for robot control with particular reference to autonomous vehicles and mobile robots. Giving an overview of theory related to this topic, the investigation not only covers hardware, such as visual sensors or laser scanners, but also space descriptions, such as digital elevation models and point descriptors, introducing new aspects and characterization of terrain assessment. During the discussion, a wide number of examples and methodologies are exposed according to different tools and sensors, including the description of a recent method of terrain assessment using normal vectors analysis. Indeed, normal vectors has demonstrated great potentialities in the field of terrain irregularity assessment in both on‐road and off‐road environments.

A novel 3D user interface for the immersive design review

This work describes a novel hardware/software platform dedicated to design review process simplification, using immersive reality technologies. The proposed platform allows designers to interface with CAD engines and visualize different data types simultaneously into an immersive and stereoscopic multi-view visualization system. This research focuses on the development of a novel immersive user interface, a smart 3D disk with a set of widgets. During the immersive sessions, the user can activate functionalities using cost-effective pointing devices and the conceived 3dUIs projected into the virtual environment. In this way, it is possible to easily manipulate virtual objects, perform basic operations such as rotations and translations but also more complex CAD functionalities such as surfaces shape modification. Each feature can be selected inside the virtual world using the smart 3D disk. Users evaluations show that the use of a virtual environment may enhance the perception of designers ideas during the design process and the use of smart 3D interfaces simplifies the interaction among user and virtual objects.

A Proposed Software Framework Aimed at Energy-Efficient Autonomous Driving of Electric Vehicles

This paper describes the development of an electric car prototype, aimed at autonomous, energy-efficient driving. Starting with an urban electric car, we describe the mechanical and mechatronics add-ons required to automate its driving. In addition, a variety of exteroceptive and proprioceptive sensors have been installed in order to obtain accurate measurements for datasets aimed at characterizing dynamic models of the vehicle, including the complex problem of wheel-soil slippage. Current and voltage are also monitored at key points of the electric power circuits in order to obtain an accurate model of power consumption, with the goal of allowing predictive path planners to trace routes as a trade-off between path length and overall power consumption. In order to handle the required variety of sensors involved in the vehicle, a MOOS-based software architecture has been developed based on distributed nodes that communicate over an onboard local area network. We provide experimental results describing the current stage of development of this platform, where a number of datasets have been already grabbed successfully and initial work on dynamics modeling is being carried on.

Laser based driving assistance for smart robotic wheelchairs

This paper is presenting the ongoing work toward a novel driving assistance system of a robotic wheelchair, for people paralyzed from down the neck. The users head posture is tracked, to accordingly project a colored spot on the ground ahead, with a pan-tilt mounted laser. The laser dot on the ground represents a potential close range destination the operator wants to reach autonomously. The wheelchair is equipped with a low cost depth-camera (Kinect sensor) that models a traversability map in order to define if the designated destination is reachable or not by the chair. If reachable, the red laser dot turns green, and the operator can validate the wheelchair destination via an Electromyogram (EMG) device, detecting a specific group of muscles contraction. This validating action triggers the calculation of a path toward the laser pointed target, based on the traversability map. The wheelchair is then controlled to follow this path autonomously. In the future, the stream of 3D point cloud acquired during the process will be used to map and self localize the wheelchair in the environment, to be able to correct the estimate of the pose derived from the wheels encoders.