Can an Engineering Competition Catalyze Curriculum Innovation?

This article describes the ongoing efforts of a multidisciplinary group of faculty at an undergraduate institution to form a team and compete in the IBM AI XPRIZE competition. We describe the advantages and disadvantages of faculty participation in major engineering competitions over more traditional professional activities at undergraduate engineering institutions. Our discussion is focused on the benefits to three major groups: undergraduate students, faculty, and academic institutions. We use examples from our one year of experience in the competition and from the literature to illustrate these benefits. Already observed benefits from the competition include increased student engagement, development and introduction of a new minor in cognitive science, the purchase of a state-of-the-art robot and a deep learning server, enhanced multidisciplinary collaboration among faculty, and heightened awareness among administrators of the growing importance of artificial intelligence (AI) technologies. Results of a student survey regarding their involvement in with the team are presented

Diseño de algoritmo realimentado para detección de objetos en entornos continuos

En el presente artículo se describe el Mask Feedback Algorithm (MFA) desarrollado en el Laboratorio de Sistemas Distribuidos (LSD) de la Facultad de Ingeniería de la Universidad Nacional de Asunción (FIUNA), con el propósito de detectar objetos presentes en superficies acuáticas desarrollado como una de las componentes fundamentales del proyecto “Surface Drone for the Study of Water Quality” también perteneciente al LSD, sin embargo MFA puede ser adaptado a un mayor número de situaciones en las cuales está presente un entorno continuo y se pretende extraer información referente a objetos sumergidos parcialmente en dicho entorno.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time

Adaptive tactical behaviour planner for autonomous ground vehicle

Success of autonomous vehicle to effectively replace a human driver depends on its ability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through scenarios such as intersections that demand interactive behavior for successful navigation. The many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state of the art in path planning solutions. They have been found to result in inefficient and sometime unsafe behaviours when tackling interactively demanding scenarios. In this paper we review the current state of the art of path planning solutions, the individual planners and the associated methods for each planner. We then establish a gap in the path planning solutions by reviewing the methods against the objectives for successful path planning. A new adaptive tactical behaviour planner framework is then proposed to fill this gap. The behaviour planning framework is motivated by how expert human drivers plan their behaviours in interactive scenarios. Individual modules of the behaviour planner is then described with the description how it fits in the overall framework. Finally we discuss how this planner is expected to generate safe and efficient behaviors in complex dynamic traffic scenarios by considering a case of an un-signalised roundabout

Artificial Intelligence for the Advancement of Lunar and Planetary Science and Exploration

AI-driven methods have potential to minimise manual labour during planetary data processing and aid ongoing missions with real-time data analysis. This white paper focuses on key areas of AI-driven research, the need for open source training data, and the importance of collaboration between academia and industries to advance AI-driven research

VELOCITY PERCEPTION USING AVERAGE OF DISTANCE BETWEEN THE OBJECTS THERE WAS IN ENVIRONMENT

In ecological psychology, it is thought that animals and insects use visual information in exchange for distance information. In this paper, we focus attention on the mechanism of animals into consideration and address the method used to estimate the velocity of a vehicle by employing only one camera. Simulations are conducted and their accuracy is discussed

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability

Obstacle detection through sensor fusion is unmanned surface vehicles

In our diploma thesis we issue a navigation problem of autonomous surface vehicles. Presume we have a vessel appointed with sensors, such as GPS, IMU, compass, a stereo sistem of two cameras and a standalone processing unit that combines sensor data and performs segmentation and planning in real time. We begin by introducing the algorithm of segmentation, that along with help of advanced computer vision methods extracts useful visual information, therefore avoids obstacles in its path (boats, swimmers, buoys). Then, we focus on horizon estimation by taking into account data of innertial measurement unit, with whom we improve the estimation of a sea border. To improve localization of obstacles in front of the vessel, we calculate depth of every pixel in the image. Image pairs are first rectified as we simplify the correspondence search to single dimension. Furthermore, we present, implement and evaluate our methods. We conclude by discussing further work that can be done