Obstacle Avoidance and Navigation of Autonomous Mobile Robot

Rekha Raja; S N. Shome; S. Nandy; R. Ray

doi:10.4028/www.scientific.net/AMR.403-408.4633

Paper Titles

A Simplified Prediction Method for EMI Generated by Three-Phase PWM Rectifier
p.4606

Optimization of Fabrication Process for MEMS Based Microneedles Using ICP Etching Technology
p.4611

Design and Modeling of a Wide Tuning Range, High Q, Low Supply Voltage MEMS Based Tunable Capacitor for Wireless Communication Applications
p.4617

Physical Level Simulation of PolyMUMPs Based Monolithic Tri-Axis MEMS Capacitive Accelerometer Using FEM Technique
p.4625

Obstacle Avoidance and Navigation of Autonomous Mobile Robot
p.4633

Tracking a Chaotic System Using an Observer-Based Nonlinear State-Feedback Controller
p.4643

Two-Degree-of-Freedom Helicopter Closed-Loop Identification through a Cascade Controller
p.4649

A Fuzzy Logic Based Automatic Control of Rotary Crane (A Simulation Approach)
p.4659

Robust Feedback Linearization Approach to Pitch Autopilot Design
p.4667

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Obstacle Avoidance and Navigation of Autonomous...

Obstacle Avoidance and Navigation of Autonomous Mobile Robot

Abstract:

This paper presents a hybrid obstacle avoidance methodology for autonomous navigation of a mobile robot in an unstructured environment. Decision is taken based on the classical method depending on the environmental scenario where the space between multiple obstacles is measured and the feasibility of passing the robot through any immediate pair of obstacles examined. In other cases, the decision is taken by the Fuzzy Logic controller. The developed algorithm is simulated and experimentally validated with a mobile robot platform equipped with forward-looking sonar for obstacle detection. Odometry sensors assist in localization of the mobile robot. The developed algorithm is found adequately intelligent to navigate the robot from any start position through to the desired goal position avoiding obstacles, and without taking recourse to any pre-built map. The simulated results exhibit fair agreement with the experimental results.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

4633-4642

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.4633

Citation:

Cite this paper

Online since:

November 2011

Authors:

Rekha Raja, S N. Shome, S. Nandy, R. Ray

Keywords:

Autonomous Mobile Robot, Autonomous Navigation, Collision Avoidance, Fuzzy Logic Controller

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] D. Janglova, Neural networks in mobile robot motion, Int. Journal of Advanced Robotic Systems, vol. 1(1), pp.15-22, (2004).

Google Scholar

[2] A. Ramirez-Serrano and M. Boumedine, Real-time navigation in unknown environments using Fuzzy logic and ultrasonic sensing, Proc. of IEEE Int. Symposium on Intelligent Control, Dearborn, p.26 – 30, (1996).

DOI: 10.1109/isic.1996.556172

Google Scholar

[3] J. Borenstein and Y. Koren, Real-time obstacle avoidance for fast mobile robots in cluttered environments, Proc. of IEEE Int. Conference on Robotics and Automation, Cincinnati, pp.572-577, (1990).

DOI: 10.1109/robot.1990.126042

Google Scholar

[4] L. Mc Fetridge and M. Yousef Ibrahim, New technique of mobile robot navigation using a hybrid adaptive fuzzy-potential field approach, Computers Ind. Engg., vol. 35(3-4), pp.471-474, (1998).

DOI: 10.1016/s0360-8352(98)00136-3

Google Scholar

[5] Mark Leyden, D. Toal and C. Flanagan, A fuzzy logic based navigation system for a mobile robot, Proc. of Automatisierungs Symposium, Wismar, Germany, (1999).

Google Scholar

[6] O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots,. Int. Journal of robotics Research, 5(1), pp.90-98, (1986).

DOI: 10.1177/027836498600500106

Google Scholar

[7] Y. Koren and J. Borenstein, Potential field methods and their inherent limitations for mobile robot navigation, Proc. of IEEE Conference on Robotics and Automation, Sacramento, California, pp.1398-1404, (1991).

DOI: 10.1109/robot.1991.131810

Google Scholar

[8] Ali Okatan and D. Gokalp, Obstacle Avoidance and Fuzzy Control of a mobile robot, Int. Conference on Informatics, Turkey, (2004).

Google Scholar

[9] Cang Ye, Yung N.H.C. and Danwei Wang, A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance, Transaction on systems, man, and cybernetics-part B, vol. 33(1), (2003).

DOI: 10.1109/tsmcb.2003.808179

Google Scholar

[10] D. Banerjee, G. Purkayastha, S. N. Shome, S. Datta and D. Pal, Simulation of FLC-based autonomous robot, National Conference on Advanced Manufacturing & Robotics, CMERI, Durgapur, pp.89-94, (2004).

Google Scholar

[11] Hassanzadeh I., Ghadiri H. and Dalayimilan R., Design and implementation of a simple fuzzy algorithm for obstacle avoidance navigation of a mobile robot in dynamic environment, Int. Symposium on Mechatronics and Its Applications, Jordon, pp.1-6, (2008).

DOI: 10.1109/isma.2008.4648863

Google Scholar

[12] V. Kanakakis , K. P. Valavanis and N. C. Tsourveloudis, Fuzzy-Logic Based Navigation of Underwater Vehicles, Journal of Intelligent and Robotic Systems, vol. 40(1), p.45 – 88, (2004).

DOI: 10.1023/b:jint.0000034340.87020.05

Google Scholar

[13] Khaldoun K. Tahboub and Munaf S. N. Al-Din, A Neuro-Fuzzy Reasoning System for Mobile Robot Navigation, Jordan Journal of Mechanical and Industrial Engineering, vol 3(1), p.77 – 88, (2009).

Google Scholar

[14] R. Ray, B. Bepari and S. Bhaumik, On the Development of an Intelligent Mobile Robotic Vehicle for Stair Navigation, Intelligent Autonomous Systems: Foundation and Applications, D.K. Pratihar and L.C. Jain (Eds. ): Intelligent Autonomous Systems, SCI 275, p.87–122, (2010).

DOI: 10.1007/978-3-642-11676-6_5

Google Scholar