Lucas de Paula Veronese

Differential evolution algorithm on the GPU with C-CUDA

Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform. In case of Evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of the Differential Evolution (DE) algorithm in C-CUDA. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced using C-CUDA. As far as we know, this is the first implementation of DE algorithm in C-CUDA.

Swarm's flight: Accelerating the particles using C-CUDA

With the development of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform, several areas of knowledge are being benefited with the reduction of the computing time. Our goal is to show how optimization algorithms inspired by Swarm Intelligence can take profit from this technology. In this paper, we provide an implementation of the Particle Swarm Optimization (PSO) algorithm in C-CUDA. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C and Matlab. Results demonstrate that the computing time can significantly be reduced using C-CUDA. As far as we know, this is the first implementation of PSO in C-CUDA.

Automated multi-label text categorization with VG-RAM weightless neural networks

In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. In this paper, we examine virtual generalizing random access memory weightless neural networks (VG-RAM WNN), an effective machine learning technique which offers simple implementation and fast training and test, as a tool for building automatic multi-label text categorization systems. We evaluated the performance of VG-RAM WNN on two real-world problems:, (i) categorization of free-text descriptions of economic activities and (ii) categorization of Web pages, and compared our results with that of the multi-label lazy learning approach (Multi-Label K-Nearest Neighbors, ML-KNN). Our experimental comparative analysis showed that, on average, VG-RAM WNN either outperforms ML-KNN or show similar categorization performance.

Large-scale mapping in complex field scenarios using an autonomous car

We present a mapping system for large-scale environments with changing features.We describe in a high level of detail a mapping algorithm for 3D-LiDAR.G-ICP was used for loop closure displacement calculation in GraphSLAM.Experiments were made with an autonomous vehicle in 3 real world environments. In this paper, we present an end-to-end framework for precise large-scale mapping with applications in autonomous driving. In special, the problem of mapping complex environments, with features changing from tree-lined streets to urban areas with dense traffic, is studied. The robotic car is equipped with an odometry sensor, a 3D LiDAR Velodyne HDL-32E, a IMU, and a low cost GPS, and the data generated by these sensors are integrated in a pose-based GraphSLAM estimator. A new strategy for identification and correction of odometry data using evolutionary algorithms is presented. This new strategy makes odometry data significantly more consistent with GPS. Loop closures are detected using GPS data, and GICP, a 3D point cloud registration algorithm, is used to estimate the displacement between the different travels over the same region. After path estimation, 3D LiDAR data is used to build an occupancy grid mapping of the environment. A detailed mathematical description of how occupancy evidence can be calculated from the point clouds is given, and a submapping strategy to handle memory limitations is presented as well. The proposed framework is tested in three real world environments with different sizes, and features: a parking lot, a university beltway, and a city neighborhood. In all cases, satisfactory maps were built, with precise loop closures even when the vehicle traveled long distances between them.

Traffic sign recognition with VG-RAM Weightless Neural Networks

Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. In this paper, we present a new approach for traffic sign recognition based on VG-RAM WNN. We evaluate its performance using the German Traffic Sign Recognition Benchmark (GTSRB), a large multi-class classification benchmark. Our experimental results showed that our VG-RAM WNN architecture for traffic sign recognition was able to rank at 4th position in the GTSRB evaluation system, with a recognition rate of 98.73%, and was overcome by only one automatic approach.

Image-based global localization using VG-RAM Weightless Neural Networks

Mapping and localization are fundamental problems in autonomous robotics. Autonomous robots need to know where they are in their area of operation to navigate through it and to perform activities of interest. In this paper, we propose an Image-Based Global Localization (VibGL) system that uses Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN). For mapping, we employ a VG-RAM WNN that learns the world positions associated with the images captured along a trajectory. During the localization, new images from the trajectory are presented to the VG-RAM WNN, which outputs their positions in the world. We performed experiments with our VibGL system applied to the problem of localizing an autonomous car. Our experimental results show that the system is able to learn large maps (several kilometers in length) of real world environments and perform global localization with median pose precision of about 3m. Considering a tolerance of 10m VibGL is able to localize the car 95% of the time.

A Model-Predictive Motion Planner for the IARA autonomous car

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARAs MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of occasional obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

Re-emission and satellite aerial maps applied to vehicle localization on urban environments

Vehicle localization in large-scale urban environments has been commonly addressed as a map-matching problem in the literature. Generally, the maps are 2D images of the world where each pixel covers a part of it. However, building maps for large-scale urban environments requires driving the vehicle along the desired path at least once. In order to simplify this task, in this work, we propose a new localization system that uses satellite aerial map-images available on the Internet to localize a vehicle in a complex urban environment. Satellite aerial map-images are compared against re-emission maps built from the infrared reflectance information of the vehicles LiDAR. Normalized Mutual Information (NMI) is used to compare re-emission and aerial map images. A Particle Filter Localization strategy is applied for vehicles localization. As a result, the system has an accuracy of 0.89m in a test course with 6.5km. Our system can be used continuously without losing track, and it works even in dark and partially occluded areas.

A simple yet effective obstacle avoider for the IARA autonomous car

We present a simple yet effective obstacle avoider for the Intelligent and Autonomous Robotic Automobile (IARA). At each or several motion planning cycles, the IARAs obstacle avoider firstly receives as input an updated map of the environment around the car, the current cars state relative to the map, and a trajectory from the current cars state to the next goal state. Secondly, the obstacle avoider simulates the trajectory. Finally, if the trajectory crashes into an obstacle, then the obstacle avoider decreases the linear velocity commands of the trajectory to prevent the accident. To evaluate the performance of the obstacle avoider, we executed experiments with the IARAs simulator using a real-world sensor data log, which was acquired in the campus of the Universidade Federal do Espírito Santo (UFES). We also carried out experiments with IARA itself, which was driven autonomously on a parking lot of the UFES. Experimental results showed that the obstacle avoider, together with the motion planner, allows the IARA to go from objective to objective safely. In fact, in all the experiments executed with the IARA for about one year, the obstacle avoider operated successfully.

A Computational Geometry Approach for Localization and Tracking in GPS-denied Environments*

Localization and tracking of vehicles is still an important issue in GPS-denied environments both indoors and outdoors, where accurate motion is required. In this work, a localization system based on the random disposition of LiDAR sensors which share a partially common field of view and on the use of the Hausdorff distance is addressed. The proposed system uses the Hausdorff distance to estimate both the position of the LiDAR sensors and the pose of the vehicle as it drives within the environment. Our approach is not restricted to the number of LiDAR sensors the estimation procedure is asynchronous, the number of vehicles it is a multidimensional approach, or the nature of the environment. However, it is implemented in open spaces, limited by the range of the LiDAR sensors and the geometry of the vehicle. An empirical analysis of the presented approach is also included here, showing that the error in the localization estimation remains bounded in approximately 50 cm. Real-time experimentation as validation of the proposed localization and tracking techniques as well as the pros and cons of our proposal are also shown in this work.