Aggregate Selection in Evolutionary Robotics

Can the processes of natural evolution be mimicked to create robots or autonomous agents? This question embodies the most fundamental goals of evolutionary robotics (ER). ER is a field of research that explores the use of artificial evolution and evolutionary computing for learning of control in autonomous robots, and in autonomous agents in general. In a typical ER experiment, robots, or more precisely their control systems, are evolved to perform a given task in which they must interact dynamically with their environment. Controllers compete in the environment and are selected and propagated based on their ability (or fitness) to perform the desired task. A key component of this process is the manner in which the fitness of the evolving controllers is measured. In ER, fitness is measured by a fitness function or objective function. This function applies some given criteria to determine which robots or agents are better at performing the task for which they are being evolved. Fitness functions can introduce varying levels of a priori knowledge into evolving populations. Som

Trends In Evolutionary Robotics

A review is given on the use of evolutionary techniques for the automatic design of adaptive robots. The focus is on methods which use neural networks and have been tested on actual physical robots. The chapter also examines the role of simulation and the use of domain knowledge in the evolutionary process. It concludes with some predictions about future directions in robotics. Appeared in Soft Computing for Intelligent Robotic Systems, edited by L.C. Jain and T. Fukuda, PhysicaVerlag, New York, NY, 215--233, 1998. 1 Introduction To be truly useful, robots must be adaptive. They should have a collection of basic abilities that can be brought to bear in tackling a variety of tasks in a wide range of environments. These fundamental abilities might include navigation to a goal location, obstacle avoidance, object recognition, and object manipulation. However, to date, this desired level of adaptability has not been realized. Instead, robots have primarily been successful when deploye..

A Situated Vacuuming Robot

We undertook this project as an opportunity to explore design ideals of the embodied approach. At our disposal was a Pioneer robot and its Saphira software (ActivMedia 1996). Similar to subsumption architecture (Brooks 1986), the Saphira software tackles the dilemma of how to implement layered control design within a system which is inherently centralized. Brooks saw each layer as a simple and almost independent computational entity and, likewise, Saphira allows us to create a hierarchy of behaviors that each have the capacity to function simultaneously and yet asynchronously. Just as Brooks proposed a means by which one level can subsume a lower level by inhibiting its output, so behaviors can each be assigned a priority

REAPER: A Reflexive Architecture For Perceptive Agents

This article describes the winning entries in the 2000 Association for the Advancement of Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication. In the Hors d\u27Oeuvres, Anyone? event, a team of robots entertained the crowd while they interactively served cookies; and in the Urban Search-and-Rescue event, a single robot autonomously explored a section of the test area, identified interesting features, built an annotated map, and exited the test area within the allotted time