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A Digital Artificial Brain Architecture for Mobile Autonomous Robots (1999)
| Content Provider | CiteSeerX |
|---|---|
| Author | Pérez-Uribe, Andrés Sanchez, Eduardo |
| Description | An autonomous robot need not be given all the details of the environment in which it is going to act: it can acquire them by direct interaction. One approach to learn by interaction is reinforcement learning, though, the robot has also to be able to autonomously categorize the input data it receives from the environment, deal with the stability-plasticity dilemma, and learn very rapidly. In this paper we present a digital artificial brain architecture capable of dealing with such problems. Furthermore, we present its use for controlling a mobile autonomous robot in an obstacle avoidance task in a real arena. Keywords. Artificial neural networks, mobile autonomous robots, neurocontrol. 1 Introduction Programming an autonomous robot so that it reliably acts in an unknown or a dynamic environment is a difficult thing to do. This is due to missing information during programming, the dynamic nature of the environment and the inherent noise in the robot's sensors and actuators [1]. One com... |
| File Format | |
| Language | English |
| Publisher Date | 1999-01-01 |
| Publisher Institution | Proceedings of the Fourth International Symposium on Artificial Life and Robotics AROB'99 |
| Access Restriction | Open |
| Subject Keyword | Mobile Autonomous Robot Autonomous Robot Direct Interaction Inherent Noise Dynamic Nature Reinforcement Learning Input Data Obstacle Avoidance Task Digital Artificial Brain Architecture Artificial Neural Network Dynamic Environment Real Arena Difficult Thing Stability-plasticity Dilemma |
| Content Type | Text |
| Resource Type | Article |
