Robust navigation in an unknown environment with minimal sensing and representation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A topological approach of path planning for autonomous robot navigation in dynamic environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
On the Topology of Discrete Strategies
International Journal of Robotics Research
Switching control approach for stable navigation of mobile robots in unknown environments
Robotics and Computer-Integrated Manufacturing
Toward simulating realistic pursuit-evasion using a roadmap-based approach
MIG'10 Proceedings of the Third international conference on Motion in games
Robotics and Autonomous Systems
Optimal exploration of terrains with obstacles
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
Sensing and Filtering: A Fresh Perspective Based on Preimages and Information Spaces
Foundations and Trends in Robotics
Analyzing the effect of landmark vectors in homing navigation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Rendezvous in space with minimal sensing and coarse actuation
Automatica (Journal of IFAC)
Worst-case optimal exploration of terrains with obstacles
Information and Computation
Information-Seeking Control Under Visibility-Based Uncertainty
Journal of Mathematical Imaging and Vision
Intensity-based navigation with global guarantees
Autonomous Robots
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This paper considers what can be accomplished using a mobile robot that has limited sensing. For navigation and mapping, the robot has only one sensor, which tracks the directions of depth discontinuities. There are no coordinates, and the robot is given a motion primitive that allows it to move toward discontinuities. The robot is incapable of performing localization or measuring any distances or angles. Nevertheless, when dropped into an unknown planar environment, the robot builds a data structure, called the gap navigation tree, which enables it to navigate optimally in terms of Euclidean distance traveled. In a sense, the robot is able to learn the critical information contained in the classical shortest-path roadmap, although surprisingly it is unable to extract metric information. We prove these results for the case of a point robot placed into a simply connected, piecewise-analytic planar environment. The case of multiply connected environments is also addressed, in which it is shown that further sensing assumptions are needed. Due to the limited sensor given to the robot, globally optimal navigation is impossible; however, our approach achieves locally optimal (within a homotopy class) navigation, which is the best that is theoretically possible under this robot model.