This document presents a study on rule-based navigation for autonomous mobile robots in uncertain environments. It describes using a rule-based sensor network approach where rules are formulated based on sensor data to guide the robot's movements. Simulation results show the rule-based technique is effective for path planning to reach targets while avoiding obstacles. The approach is also compared to other techniques to prove its ability to safely and efficiently navigate environments.

1. Study of Various Aspect of Tunnelling for Highway or Water
Resources Work with Case Study of Important Tunnels Constructed
Presented by
Santosh Kumar Sahu
(M.Tech)
Roll No.- 12MT03
Under the Guidance of
Prof. Suresh Kumar
Department of Civil Engineering
CAMBRIDGE INSTITUTE OF TECHNOLOGY
TATISILWAI, RANCHI
835103

2. Content
1. Introduction
2. Rule Base Navigation in current analysis
3. Some of the Rules for RBT
4. Simulation results
5. Conclusion and future work
6. References

3. Introduction
• Now-a-days mobile robots are widely used in various field of engineering such as aerospace
research, nuclear research, mining industry and the production industry.
• Navigation of mobile robot is one of the elementary problems in robotic research field. In
general the navigation algorithms are classified as global and local, depending on the
surrounding environment.
• In global navigation, the surrounding environment is completely known to the mobile robot
and the path which avoids the obstacle is predefined.
• In other side local navigation, the surrounding environment is completely unknown to the
robot and various types of sensors are used to detect and recognize obstacles and avoid
collisions.
• Several efficient techniques have been developed by the researchers in the navigation of
mobile robot.
• The hallmark of this presentation describes navigation of an autonomous mobile robot in a
cluttered environment using Rule Based Sensor Network .

4. Finally, simulation experiments using MATLAB program have shown that, the RULE
BASE model is suitable and effective for path planning of a mobile robot in uncertain
terrain to find and reach to target objects.

5. Rule Base Navigation in current analysis
• This technique addresses the navigation technique of mobile robot and based on human perception,
generated by the induction.
• This navigation analysis covers much of the rules and these rules are implemented inside controller to
control the navigation of mobile robot (through its sensory part) inside environment and rules are
formulated from human psychological nature or perception.
• A Sensory part senses the objects placed around the robot surrounding (as a digital image) and
converted these objects image into digital form (primarily as raw data).
• Collections of environmental data depends upon different sensors i.e. placed around it in systematic
manner on its body and according to information collected by its sensory models adjust itself to it.
• At the time of data collection from environment by sensors, some of the data’s collected by sensors are
wrong in nature but it also play the role at the time of navigation, accordingly rule based technique
play the role to avoid this error by applying its logic and conduct safe and fast navigation.

6. Sensor Integration and Fusion takes place for navigation control

7. Some of the Rules for Rule Based Technique
Whenever obstacle & target both are located in left side of the robot that time heuristic
rule is in following way
Rule 1:-
If LOD = 140 & ROD ≤160 & FOD ≤ 220 & TA =75° than CSA = 0°.
Similarly, rule also formulated with reference to the obstacle as well as targets are located
in right side of the robot. Some of the heuristic rules listed below:
Rule 4:-
If LOD ≤ 160 & ROD = 140 & FOD ≤ 220 & TA =27° than CSA = 55°.
In addition, heuristic rule for that time when obstacle is located at the front side of the
robot & target is located at the right side of the robot.
Rule 6:-
If LOD ≤ 110 & ROD ≤ 110 & FOD = 1200 & TA =22° than CSA = 29°.

8. Simulation Results

9. Conclusion and future work
• The approach is based on the advantages of knowledge base reasoning of rule base
interference system.
• RULE BASE system has advantages of replacing the large number of simulation data.
• The proposed technique was analyzed in a number of simulated experiments and it was
found that the results compromise with satisfaction the obstacle avoidance and moving
towards the requirements.
• The above technique also compared with other techniques to proved the authenticity of the
RULE BASE controller.
• The proposed technique show the efficiency of the navigational controller by simulation
results.
• FutureWork could be included the multiple mobile robots with dynamic obstacles.

10. References
• Coax Flight Simulator in Webots by Edgard Font Calafell (Master Thesis) on 06/28/2011.
• Velappa Ganapathy, Soh Chin Yun and Wen Lik Dennis Lui, “Utilization of Webots and Khepera II as a Platform for Neural Q-Learning Controllers,”IEEE Symposium on
Industrial Electronics & Applications (ISIEA 2009) Kuala Lumpur, Malaysia 4-6 October 2009 Volume 2 pp. 547-1032.
• Olivier Michel, “Symbiosis between Virtual and Real Mobile Robots” , Virtual World Lecture Notes in Computer Science, Microprocessor and Interface Lab, Swiss Federal
Institute of Technology, Lausanne, Switzerland, Volume 1434, 1998, pp. 254-263.
• Lukasz Przytula, M. Szczuka,“On Simulation of NAO Soccer Robots in Webots”, Proceedings of the International Workshop CS&P, September 28-30, Pułtusk, Poland
(2011).
• V. Nunez, L.I. Olvera and J.A. Pamanes, “Simulation and Experimentation of Walking of the bioloid Humanoid Robot”, 13th World Congress in Mechanism and Machine
Science, Guanajuato, Mexico, 19-25 June (2011).
• S. K. Pradhan, D. R. Parhi and A. K. Panda, “Navigation of Multiple Mobile Robots using Rule-based-neuro-fuzzy technique”, International journal of computational
intelligent, vol-3, no. 2, pp. 142–152, October (2005).
• A. Abubaker, “A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique”, Research journal of Applied science Engineering and
Technology, vol-4, no. 15, pp. 2577–2583, (2012).
• M. A. Batalin, G. S. Sukhatme, and M. Hattig, “Mobile Robot Navigation Using a Sensor Network”, IEEE International Conference of Robotics and Automation, pp. 636-
642, May (2004).
• E. M. Saad, M. H. Awadalla, A. M. Hamdy, and H. I. Ali, “Efficient Distributed Controller for Wandering Robot Formations using Local Sensing and Limited Range
Communications”, International Journal of computers, vol-2, no. 3, pp. 330–339, (2008).
• P. Benavidez and M. Jamshidi, “Mobile robot navigation and target tracking system”, 6th International Conference on System of Systems Engineering, pp. 299–304, Jun.
(2011).