Driving Cars by Means of Genetic Algorithms

Proceedings of: 10th International Conference on Parallel Problem Solving From Nature, PPSN 2008. Dortmund, Germany, September 13-17, 2008The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.Publicad

Evolving a rule system controller for automatic driving in a car racing competition

IEEE Symposium on Computational Intelligence and Games. Perth, Australia, 15-18 December 2008.The techniques and the technologies supporting Automatic Vehicle Guidance are important issues. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the car safety, reduction in emission or fuel consumption or optimization of driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. For this work we have designed an automatic controller that learns rules with a genetic algorithm. This paper is a report of the results obtained by this controller during the car racing competition held in Hong Kong during the IEEE World Congress on Computational Intelligence (WCCI 2008).Publicad

Unmanned Ground Vehicles for Smart Farms

Forecasts of world population increases in the coming decades demand new production processes that are more efficient, safer, and less destructive to the environment. Industries are working to fulfill this mission by developing the smart factory concept. The agriculture world should follow industry leadership and develop approaches to implement the smart farm concept. One of the most vital elements that must be configured to meet the requirements of the new smart farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the characteristics that the UGVs must have to function efficiently in this type of future farm. Two main approaches are discussed: automating conventional vehicles and developing specifically designed mobile platforms. The latter includes both wheeled and wheel-legged robots and an analysis of their adaptability to terrain and crops

Robocart: System Design for the First Generation Autonomous Golf Cart

Inspired by ongoing research and continuous developments in autonomous vehicles, the Robocart MQP focuses on the system development for a first-generation autonomous golf cart vehicle and wireless system server. By creating the foundation for a modular and interdisciplinary system, visualization software and mechanisms can be intuitively integrated. The end result of this project is a better understanding of the efficiency of each subsystems against the real-time challenges required for an autonomous, wireless, and vision-based system. In conclusion of this project, recommendations in mechanical, electrical, and algorithm development were formed to promote further research and enhance rider usability

Robocart Machine Vision Framework

This project documents a framework for an extensible, flexible machine vision software implementation for the Robocart project. It uses a distributed mobile computing framework in order to best leverage the scalability of machine vision. This process aims to improve upon current machine vision implementations in commercial autonomous vehicles, as well as provide a basis for further development of RobocartÂ’s autonomous navigation systems. This framework is tested with the use case of road detection

Using a machine learning algorithm to create a computational artwork: variable

As computational systems have become an integral part of our daily lives, we often see that contemporary art also has adapted itself with the newborn technological changes in diversified dimensions. Machine Learning, which has recently become a remarkable development in science, has also begun to manifest itself in various artistic works. As accordingly, the artwork that has been created by the author of this article named "Variable" stands as an interactive work of art that embraces machine learning algorithms within its compositional structure. The artwork was extensively influenced by the sophisticated discourse of German philosopher Heideggerʼs book "Being and Time." Consequently, Being and Time text has been taught to a machine learning system, and thus the system has been able to automatically generate new original contents when the viewer interacts with the touch of a button. The generative system performs its Machine Learning Markov Chain operations with the implementation of a Python programming language-based library named Markovify. The work constantly redefines its own artistic title and statement with the use of a machine learning framework. In this article, the contribution of machine learning to the production of artworks is being examined while focusing on various implementations

How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV

This work explores the feasibility of steering a drone with a (recurrent) neural network, based on input from a forward looking camera, in the context of a high-level navigation task. We set up a generic framework for training a network to perform navigation tasks based on imitation learning. It can be applied to both aerial and land vehicles. As a proof of concept we apply it to a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a room containing a number of obstacles. So far only feedforward neural networks (FNNs) have been used to train UAV control. To cope with more complex tasks, we propose the use of recurrent neural networks (RNN) instead and successfully train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision based control is a sequential prediction problem, known for its highly correlated input data. The correlation makes training a network hard, especially an RNN. To overcome this issue, we investigate an alternative sampling method during training, namely window-wise truncated backpropagation through time (WW-TBPTT). Further, end-to-end training requires a lot of data which often is not available. Therefore, we compare the performance of retraining only the Fully Connected (FC) and LSTM control layers with networks which are trained end-to-end. Performing the relatively simple task of crossing a room already reveals important guidelines and good practices for training neural control networks. Different visualizations help to explain the behavior learned.Comment: 12 pages, 30 figure