Jonay Toledo

Using Kinect on an Autonomous Vehicle for Outdoors Obstacle Detection

An accurate method to detect obstacles and dangerous areas is the key to the safe performance of autonomous robots. Time of flight sensors can report their existence through the emission, reflection, and measurement of wave patterns, but large wavelength light projection is often unreliable in outdoors environments, due to solar radiation contamination. In this paper, a specific Microsoft Kinect arrangement on a robotic vehicle is proposed, such that outdoors detection is possible. The main contribution of this paper is the description of a sequence of filtering techniques, which translate the depth image provided by the sensor into definite obstacle projections in the navigability map used by the vehicle. A series of experiments proves that the Kinect device is more accurate at detecting obstacles using this procedure than a camera pair using two different stereovision techniques.

Real-time adaptive obstacle detection based on an image database

Abstract In this paper, an innovative method for the detection and avoidance of obstacles is presented. This is based on image registration techniques. The aim of this method is the detection of the possible obstacles that could be in the route where VERDINO (an autonomous electrical vehicle which is going to work in the surroundings of a bioclimatic urbanization as part of the SAGENIA project) navigates. The obstacle detection is one of the most critical parts of the prototype. It is responsible for the detection and later avoidance of the pedestrians, cars, etc. that can damage it or be damaged by the prototype. The algorithm is able to work in real time, with good detection rates and a fast response. It also includes a dynamical database that will allow the vehicle to learn adaptively the current state of the environment, rejecting old images. With this, some of the typical problems related to the image registration techniques are removed. Some examples and results are provided, corroborating the good behavior of the algorithm, which has been tested in simulated and real conditions. The method is also applicable to other tasks, like surveillance for non-stationery cameras or other applications over very different kinds of images.

Generating automatic road network definition files for unstructured areas using a multiclass support vector machine

Smooth and safe paths.Tolerance to the sensor measurement errors.First method in using Multiclass SVM for path planning.Better results in smoothness than other related SVM path planning methods.Similar results in terms of safety than other related SVM path planning methods. In this paper, an innovative methodology for the generation of a Road Network Definition File (RNDF) using only an obstacle map as input is presented. This RNDF, which relies on a Multiclass Support Vector Machine(MSVM)-based trajectory generation method, will be used by an autonomous vehicle for transporting people in closed, unstructured areas for which no previous information is available, such as residential areas or industrial parks. The advantages of using this technique are the generation of a safe and smooth trajectory graph (making the trip more comfortable for riders by having trajectories pass as far away as possible from obstacles). Moreover, although there exist other previous Support Vector Machine (SVM) path planning methods, this is the first to use a MSVM. The advantages of doing so are that by obtaining a decision boundary for each object in the scene, all possible trajectories are computed and joined to form a graph. This is done through a combination of a Nearest-Neighbor Graph (NNG) and a Relative Neighborhood Graph (RNG). The method was tested with real data and in real conditions, yielding good results. At the end of the paper, results for two kinds of studies are presented. The first set of tests is intended to determine the best parameter values for the proposed methodology. In the second set of evaluations, the approach is compared with other state-of-the-art SVM-based methods, as well as with a classical approach, demonstrating that the method outperforms them in some aspects. Furthermore, the source code of the method is available for testing, as are some videos in which the output of the method is shown, including a comparison with previous methods.

Applying an Ant Colony Optimization Algorithm to an Artificial Vision Problem in a Robotic Vehicle

In this paper, a problem of artificial vision in a robotic autonomous vehicle consisting of the real time detection and tracking of non-structured roads is addressed by applying an Ant Colony Optimization (ACO) algorithm. The solution adopted tries to find some properties describing the probability that a pixel belongs to the boundaries of the road, and then formalize the road detection problem as an optimization one.

MCL with sensor fusion based on a weighting mechanism versus a particle generation approach

The combined action of several sensing systems, so that they are able to compensate the technical flaws of each other, is common in robotics. Monte Carlo Localization (MCL) is a popular technique used to estimate the pose of a mobile robot, which allows the fusion of heterogeneous sensor data. Several sensor fusion schemes have been proposed which include sensors like GPS to improve the performance of this algorithm. In this paper, an Adaptive MCL algorithm is used to combine data from wheel odometry, an inertial measurement unit, a global positioning system and laser scanning. A particle weighting model which integrates GPS measurements is proposed, and its performance is compared with a particle generation approach. Experiments were conducted on a real robotic car within an urban environment.

Brief paper: A neuro-fuzzy method applied to the motors of a stereovision system

In this paper, a new approach for steering a binocular head is presented. This approach is based on extracting the experts knowledge in order to improve the behaviour of the classical control strategies. This is carried out without inserting new elements in the system. Neuro-fuzzy techniques have been chosen in order to reach this target. As a result, a more friendly robotic system is achieved.

Path planning using a Multiclass Support Vector Machine

Graphical abstractDisplay Omitted HighlightsGeneration of a smooth path based on the hyperplane generated by the SVM training stage.The margin maximization constraint of the SVM ensures that the generated path will be as far as possible from the obstacles in the environment.Robustness against uncertainty and the noise generated by sensors.Generation of a decision graph that allows using higher level methods to select the final path.The method is compared with a previous approach and in real world conditions. In this paper, a new path planning algorithm for unstructured environments based on a Multiclass Support Vector Machine (MSVM) is presented. Our method uses as its input an aerial image or an unfiltered auto-generated map of the area in which the robot will be moving. Given this, the algorithm is able to generate a graph showing all of the safe paths that a robot can follow. To do so, our algorithm takes advantage of the training stage of a MSVM, making it possible to obtain the set of paths that maximize the distance to the obstacles while minimizing the effect of measurement errors, yielding paths even when the input data are not sufficiently clear. The method also ensures that it is able to find a path, if it exists, and it is fully adaptable to map changes over time. The functionality of these features was assessed using tests, divided into simulated results and real-world tests. For the latter, four different scenarios were evaluated involving 500 tests each. From these tests, we concluded that the method presented is able to perform the tasks for which it was designed.

An artificial intelligence approach to forward kinematics of Stewart Platforms

The Stewart Platform, one of the most successful and popular parallel robots, has attracted the attention of many researchers in recent decades. The solution of the forward kinematics problem in real-time is one of the key aspects that continues to garner interest. In this paper we propose a new approach for solving this particular case using Support Vector Machines, a popular Machine Learning method for classification and regression. The algorithm involves a data generation and preprocessing off-line phase, and a fast on-line evaluation. The experiments show that this method is very accurate and suitable for use in real-time.

Object detection in non-stationary video surveillance for an autonomous vehicle

In this paper, a new method for the automated video surveillance of wide areas is described. Using the images obtained from a set of cameras installed on an autonomous vehicle, a video surveillance tool has been developed, based on the comparison between images that have been taken in the same place but at different times. The vehicle drives around the watched area, looking for intruders. The method described in this paper is the image comparison system used for this task, and it is based on image registration and change detection techniques. The system has been fully tested, obtaining promising results. The validation process shows the good performance of the methods selected to develop the application. It is also able to be executed in real time with good detection rates.

MNEME: A memory hierarchy simulator for an engineering computer architecture course

As in other fields of Engineering, simulators are widely used to teach Memory hierarchy topics. In this paper, a simulator called MNEME (due to the muse of the memory in Greek mythology), which includes a complete vision of memory hierarchy topics, is presented. This simulator has been validated and improved using feedback from students during two academic years. This way, the students have taken part significantly in the MNEME design process.