Map Building including Qualitative Reasoning for Aibo Robots

摘要

The problem that a robot navigates autonomously through its environment, builds its own map and localizes itself in the map, is still an open problem. It is known as the SLAM (Simultaneous Localization and Map Building) problem. Most of the approaches to solve the SLAM problem divide the space into regions and compute the probability that the robot is in each region. We call them quantitative methods. The drawbacks of quantitative methods are that they have a high computational cost and a low level of abstraction. In order to fulfil these drawbacks qualitative models have been recently used. However, qualitative models are non deterministic. Therefore, the solution recently adopted has been to mix both qualitative and quantitative models to represent the environment and build maps. However, no reasoning process has been used to deal with the information stored in maps up to now, therefore maps are only static storage of landmarks. In this paper we propose a novel method for map building based on hybrid (qualitative + quantitative) representation which includes also a reasoning process. Distinctive landmarks acquisition for map representation is provided by the cognitive vision and infrared modules which compute differences from the expected data according to the current map and the actual information perceived. We will store in the map the relative orientation information of the landmarks which appear in the environment, after a qualitative reasoning process, therefore the map will be independent of the point of view of the robot. The simultaneous localization of the robot will be solved by considering the robot as another landmark inside the map, each time the robot is moving. This map building method has been tested on the Sony AIBO four legged robots on an unknown labyrinth, made of rectangular walls. The path planning strategy has been to explore the environment based on minimizing movements and maximizing the explored areas of the map.