Autonomous feature-based exploration

This paper presents an algorithm for feature-based exploration of a priori unknown environments. We aim to build a robot that, unsupervised, plans its motion such that it continually increases both the spatial extent and detail of its world model - its map. We present a method by which the planned motion at any instant is motivated by the geometric, spatial and stochastic characteristics of the current map. In particular each feature within the map is responsible for determining nearby unexplored areas that if visited are likely to constitute exploration. We assume that the location of the features is uncertain and represented by a set of probability distribution functions (pdfs). These distributions are used in conjunction with the robot path history to determine a robot trajectory suited to exploration. We show results that demonstrate the algorithm providing real-time exploration of a mobile robot in an unknown environment.

Authors

• Paul Newman 0001
• Michael Bosse
• John J. Leonard

Keywords

• Navigation
• Simultaneous localization and mapping
• Mobile robots
• Paper technology
• Stochastic processes
• Probability distribution
• History
• Indoor environments
• Working environment noise
• Computational geometry
• Local Features
• Mobile Robot
• Unknown Environment
• Local Area
• Free Space
• Current Position
• Line-of-sight
• Radius Of Curvature
• Local Vector
• Global Search
• Position Uncertainty
• Global Goals
• Obstacle Avoidance
• Clear Path
• Geometry Features
• Robot Localization
• Fringe Visibility
• Exploration Algorithm
• Exploratory Focus