An approach to adaptive map building of mobile robots in unknown environment

Map building in unknown environment is the prerequisite to perform other tasks for the mobile robots. An effective approach based on the frontier points is proposed in this paper. Firstly, an adaptive clustering algorithm which integrates subtractive clustering and k-means clustering is introduced to partition the frontier points into different groups. Secondly, the centers of the groups serve as candidate destination points and they are evaluated using multi-step-ahead prediction. The optimal one will be selected to guide the robot to explore the environment with high accuracy. Finally, simulation results are provided for validation which show that the proposed strategy can generate accurate and complete (or nearly complete) maps of the unknown environments.

[1]  Gamini Dissanayake,et al.  Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[2]  Nicholas Roy,et al.  Global A-Optimal Robot Exploration in SLAM , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[3]  H. Durrant-Whyte,et al.  A sub-optimal algorithm for automatic map building , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[4]  Yalou Huang,et al.  Motion Planning for SLAM Based on Frontier Exploration , 2007, 2007 International Conference on Mechatronics and Automation.

[5]  Fan Xiaoping.  Mobile Robot Trajectory Planning in Simultaneous Localization and Mapping Problem , 2006 .

[6]  Yan Shi,et al.  Study on combining subtractive clustering with fuzzy c-means clustering , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[7]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[8]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[9]  Stefan B. Williams.  Efficient Solutions to Autonomous Mapping and Navigation Problems , 2009 .

[10]  Alexei Makarenko,et al.  Information based adaptive robotic exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[12]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[13]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..

[14]  Randall Smith,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.