Cave Exploration of Mobile Robots using Soft Computing Algorithms

Cave Exploration is main task for most speleologist. There are cave humans can't enter or even survive. Technology can help them to explore cave with modern tiny robots. Mobile robot starts its job in an unknown cave environment and faces lot of obstacles and avoids it using fuzzy logic. A mobile robot records each and every position of traveling environment using Monte Carlo localization. Mobile robots also using camera's to capture each and every grid location and store it in a robot's internal memory. For robot's path planning we introduced a new methodology so that robot can map the whole cave environment in a efficient manner. This type of mobile robots will useful in an environment where people not able to travel and also this mobile robot will helpful for those who are doing researchers about cave.

[1]  Hongling Han,et al.  Path Planning of an Indoor Mobile Robot Navigated by Infrared , 2010, 2010 International Conference on E-Product E-Service and E-Entertainment.

[2]  A. Francy Golda,et al.  Algorithmic agent for effective mobile robot navigation in an unknown environment , 2009, 2009 International Conference on Intelligent Agent & Multi-Agent Systems.

[3]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[4]  Kevin Wedeward,et al.  A Navigation and Obstacle Avoidance Algorithm for Mobile Robots Operating in Unknown, Maze-Type Environments , 2004 .

[5]  L. Wang,et al.  Computational intelligence in autonomous mobile robotics-A review , 2002, Proceedings of 2002 International Symposium on Micromechatronics and Human Science.

[6]  Xianzhong Dai,et al.  Extended Monte Carlo algorithm to collaborate distributed sensors for mobile robot localization , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[7]  F.H.F. Leung,et al.  A practical fuzzy logic controller for the path tracking of wheeled mobile robots , 2003 .

[8]  Charles W. Warren,et al.  Fast path planning using modified A* method , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[9]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[10]  Wolfram Burgard,et al.  Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization , 2005, IEEE Transactions on Robotics.