Seminar ppt

Study of Various Aspect of Tunnelling for Highway or Water Resources Work with Case Study of Important

Tunnels ConstructedPresented by

Santosh Kumar Sahu(M.Tech)

Roll No.- 12MT03Under the Guidance of

Prof. Suresh Kumar

Department of Civil Engineering

CAMBRIDGE INSTITUTE OF TECHNOLOGY

TATISILWAI, RANCHI

835103

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

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 .

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.

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.

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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°.

Simulation Results

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.

• Future Work could be included the multiple mobile robots with dynamic obstacles.

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).

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and Machine Science, Guanajuato, Mexico, 19-25 June (2011).

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Automation, pp. 636-642, May (2004).

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