Learning to Avoid Collisions

Learning to Avoid Collisions: A Functional State Space Approach

A Monte Carlo Simulation of Chain Reaction Rear End Potential Collisions on Freeways

In recent research on modelling road collisions very little attention has been paid  to rear-end chain reaction collisions, which is characterized by more than two vehicles involved in a collision at the same time. The core aim of the present research is to develop a methodology to estimate such potential collision probabilities based on a proactive perspective, where deceleration rate to avoid...

Obstacle Avoidance by Means of an Operant Conditioning Model

This paper describes the Clpplication of a model of operant conditioning to the prohlcrn of obstacle avoidance \Vith a wheeled mobile robot. The main characteristic of the 8pplicd model is that the robot learns to avoid obstacles through a learning-by-doing cycle without external supervision. A series of ultrasonic sensors aetas Conditioned Stimuli (CS), while collisions act as an Unconditioned...

Self-learning neural control of a mobile robot

Reinforcement learning is a promising paradigm for the training of intelligent controllers. The learning capabilities of a neural network based controller architecture are shown by its application to control a mobile robot in an unknown environment. Based on multi-sensor information provided by four infrared sensors, the controller has to learn to avoid collisions, receiving only a nal training...

To appear in: Handbook of Intelligent Control: Neural, Fuzzy and Adaptive Approaches

Whenever an intelligent agent learns to control an unknown environment, two opposing objectives have to be combined. On the one hand, the environment must be su ciently explored in order to identify a (sub-) optimal controller. For instance, a robot facing an unknown environment has to spend time moving around and acquiring knowledge. On the other hand, the environment must also be exploited du...