A topological coverage algorithm for mobile robots

In applications such as vacuum cleaning, painting, demining and foraging, a mobile robot must cover an unknown surface. The efficiency and completeness of coverage is improved via the construction of a map of covered regions while the robot covers the surface. Existing methods generally use grid maps, which are susceptible to odometry error and may require considerable memory and computation. This paper proposes a topological map and presents a coverage algorithm in which natural landmarks are added as nodes in a partial map. The completeness of the algorithm is argued. Simulation tests show over 99% of the surface is covered; 85% for real (Khepera) robot tests. The path length is about 10% worse than optimal in simulation tests, and about 20% worse than optimal for the real robot, which are within theoretical upper bounds for approximates solutions to traveling salesman based coverage problems. The proposed algorithm generates shorter paths and covers a wider variety of environments than topological coverage based on Morse decompositions.

[1]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[2]  George Wolberg,et al.  Digital image warping , 1990 .

[3]  Elon Rimon,et al.  Spiral-STC: an on-line coverage algorithm of grid environments by a mobile robot , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[4]  J. O'Keefe,et al.  The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. , 1971, Brain research.

[5]  Sylvia C. Wong,et al.  Natural Landmark Recognition using Neural Networks for Autonomous Vacuuming Robots , 2000 .

[6]  Uwe R. Zimmer,et al.  Robust world-modelling and navigation in a real world , 1996, Neurocomputing.

[7]  Howie Choset,et al.  Sensor-based Coverage of Unknown Environments: Incremental Construction of Morse Decompositions , 2002, Int. J. Robotics Res..

[8]  Esther M. Arkin,et al.  Angewandte Mathematik Und Informatik Universit at Zu K Oln Approximation Algorithms for Lawn Mowing and Milling Ss Andor P.fekete Center for Parallel Computing Universitt at Zu Kk Oln D{50923 Kk Oln Germany Approximation Algorithms for Lawn Mowing and Milling , 2022 .

[9]  Viii Supervisor Sonar-Based Real-World Mapping and Navigation , 2001 .

[10]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[11]  Chee-Keng Yap,et al.  Algorithmic and geometric aspects of robotics , 1987 .

[12]  Bernhard Nebel,et al.  Dynamic decentralized area partitioning for cooperating cleaning robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[13]  Sylvia C. Wong,et al.  Performance Metrics for Robot Coverage Tasks , 2002 .

[14]  Francesco Mondada,et al.  Autonomous vacuum cleaner , 1997, Robotics Auton. Syst..

[15]  Benjamin Kuipers,et al.  A Robust, Qualitative Method for Robot Spatial Learning , 1988, AAAI.