Navigation of mobile robot by using D++ algorithm

The navigation of mobile robots is a vital aspect of technology in robotics. We applied the D++ algorithm, which is a novel and improved path-planning algorithm, to the navigation of mobile robots. The D++ algorithm combines Dijkstra’s algorithm with the idea of a sensor-based method, such that Dijkstra’s algorithm is adapted to local search, and the robot can determine its next move in real-time. Although the D++ algorithm frequently runs local search with limited ranges, it can compute optimum paths by expanding the size of the searching range to avoid local minima. In addition, we verified the performance of the D++ algorithm by applying it to a real robot in a number of environments. The use of the D++ algorithm enables robots to navigate efficiently in unknown, large, complex and dynamic environments.

[1]  K. K. Bharadwaj,et al.  Hybrid Genetic-fuzzy approach to Autonomous Mobile Robot , 2009, 2009 IEEE International Conference on Technologies for Practical Robot Applications.

[2]  Yoram Koren,et al.  Potential field methods and their inherent limitations for mobile robot navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[3]  Nak Young Chong,et al.  Fusion of direction sensing RFID and sonar for mobile robot docking , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[4]  Chia-Ju Wu,et al.  Fuzzy Motion Planning of Mobile Robots in Unknown Environments , 2003, J. Intell. Robotic Syst..

[5]  M. Meng,et al.  Mobile robot navigation using neural networks and nonmetrical environmental models , 1993, IEEE Control Systems.

[6]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[7]  J. Pan,et al.  FUZZY NAV A Vision Based Robot Navigation Architecture using Fuzzy Inference for Uncertainty Reasoning , 1995 .

[8]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[9]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[10]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.

[11]  Max Q.-H. Meng,et al.  Neural network approaches to dynamic collision-free trajectory generation , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Anthony Stentz Optimal and efficient path planning for partially-known environments , 1994 .

[13]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[14]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[15]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[16]  Jesfis Peral,et al.  Heuristics -- intelligent search strategies for computer problem solving , 1984 .

[17]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[18]  Pi-Ying Cheng,et al.  The D++ algorithm: Real-Time and collision-free path-planning for mobile robot , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Marwan Bikdash,et al.  Comparison of geometrical, kinematic, and dynamic performance of several potential field methods , 2009, IEEE Southeastcon 2009.

[20]  David Furcy,et al.  Lifelong Planning A , 2004, Artif. Intell..