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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.
Mobile robots at times require navigating in an unstructured environment to carry out various
designated tasks. It is an established fact that classical robot control methods based on precise
models are only suitable for industrial mobile robots designed to perform simple tasks and operating
in structured environments. Autonomous vehicles are generally defined as vehicles that are capable
of intelligent motion and action without requiring either tele-control or operator intervention [1].
Obstacle avoidance is essential for autonomous navigation. The general problem of path planning
for autonomous robots comprises search for a feasible path in an unstructured environment in order
to reach a particular goal with a given initial pose [2]. A number of obstacle avoidance algorithms
are developed by different researchers [1, 2, 3, 4, 5, 6, 7] based on hybrid adaptive fuzzy-potential
field approach, electrostatic potential field approach, classical control, neuro-fuzzy methods,
distance function approach, behavior-based & heuristic approach, etc. Fuzzy techniques are well
suited to model decision making processes as described in [8, 9, 10, 11, 14]. The inadequacy of
most of these algorithms is that it considers only the most critical obstacle for obstacle avoidance.
Potential field approach is applied for mobile robot navigation in disorderly environments with
static and moving obstacles.
A neuro-fuzzy reasoning scheme for mobile robot navigation has been presented in [12]. The
approach is based on decomposing a multidimensional fuzzy system into a set of simple parallel
neural networks. With the increase of the system complexity, the determination of the right set of
rules and membership functions to describe the system behavior becomes increasingly difficult in
the design of the Fuzzy Logic controller [13]. For complex systems, Fuzzy logic controller needs to
be associated with some alternate classical methodology.
This paper describes the autonomous navigation of a mobile robot in an unstructured
environment having static obstacles. The intelligent algorithm developed for this purpose considers
a two layered fuzzy logic controller and a classical mathematical formulation. This algorithm can
consider a number of critical obstacles at a time which is not considered in most of the literature
[10, 14]. The simulation and experimental results are also presented.
Advanced Materials Research Vols. 403-408 (2012) pp 4633-4642 Online: 2011-11-29
© (2012) Trans Tech Publications, Switzerland
doi:10.4028/www.scientific.net/AMR.403-408.4633
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans
Tech Publications, www.ttp.net. (ID: 202.3.77.241, Indian Institute of Technology, Kanpur, Kanpur, India-04/10/15,07:54:22)