Landmines Detection by Using Mobile Robots

Table of Contents

Chapter 1: Introduction

1.1 Landmines over the world

1.2 Challenges

1.3 Motivations

1.4 Contributions

1.5 Thesis Outline

Chapter 2: Landmines detection strategies

2.1 Landmines Detection Overview

2.1.1 Classification of Mines

2.2 Mine Detection Technologies

2.3 Metal Detector

2.4 Electromagnetic Methods

2.4.1 Ground Penetrating Radar (GPR)

2.4.2 Nuclear Quadruple Resonance (NQR)

2.4.3 Microwaves

2.4.4 Electrical Impedance Tomography(EIT)

2.4.5 Infrared Method

2.4.6 X-Ray Backscatter Method

2.4.7 Sound and Ultrasound

2.4.8 Neutron Method

2.5 Acoustic/Seismic

2.6 Biological Method

2.6.1 Dogs and Rats

2.6.2 Bees

2.6.3 Bacteria

2.6.4 Antibodies method

2.6.5 Chemical Methods

2.7 Mechanical Methods

2.7.1 Probes and Prodders

2.7.2 Mine Clearing Machines

2.8 State of discussed solutions

2.9 Robot detecting strategies

2.9.1 Disadvantages of Robots

2.10 Summary

Chapter 3: Demining robot techniques

3.1 Motion planning

3.1.1 Motion planning Problem

3.1.2 Locomotion

3.1.3 Motion system

3.1.4 Motion Planning Techniques

3.1.4.1 Sampling-Based Planning

3.1.4.2 Probabilistic Roadmap Method

3.1.4.3 Graph Search

3.1.4.4 Uninformed Search

3.1.4.5 Heuristic Search

3.1.4.6 A* Algorithm

3.1.4.7 Complete coverage Technique

3.1.5 Area Mapping

3.2 Sensor Fusion

3.2.1 Sensor fusion Applications

3.2.2 The multiple sensors integration

3.2.3 Multiple Sensors Fusion Advantages

3.2.4 Sensor fusion problems

3.2.5 Multisensor Fusion

3.2.5.1 The Integration Functions

3.2.5.2 The Rule-Based and Network System

3.2.6 Multisensor Fusion Levels

3.2.6.1 Signal-Level Fusion

3.2.6.2 Pixel Level Fusion

3.2.6.3 Feature-Level Fusion

3.2.6.4 Decision Level Fusion

3.2.7 Sensor fusion system in landmines detection

Chapter 4: Related Work

4.1 Landmines Detection by Robots

4.2 Summary

Chapter 5: The proposed framework

5.1 The Proposed Low-Cost Robot System

5.2 Motion Planning

5.3 Low cost robot structure

5.4 Multi-sensor Fusion (Decision Level Fusion)

5.5 The vision system

5.6 Destroying mines

5.7 Conclusion

Chapter 6: The Experimental Results

6.1 System Components

6.1.1 Chemical sensor

6.1.2 Metal Detector

6.1.3 Ultrasound Sensor

6.1.4 Camera Sensor

6.2 Experimental Results

6.3 Discussion

6.4 Conclusion

Chapter 7: Conclusions

7.1 Conclusions

7.2 Future Work

Research Objectives and Thematic Focus

This thesis aims to develop an efficient, low-cost autonomous robotic system for the detection and demining of buried landmines. The primary research question centers on how to integrate multi-sensor fusion and effective motion planning to improve detection accuracy, reduce false alarms, and mitigate risks to human operators compared to existing, expensive demining technologies.

• Design of a cost-effective autonomous mobile robot for landmine exploration.
• Implementation of complete coverage path planning algorithms for systematic minefield searching.
• Development of multi-sensor data fusion (decision-level) to minimize false detection rates.
• Integration of robotic arms and obstacle avoidance algorithms for safe autonomous navigation and mine disposal.

Excerpt from the Book

Summary of Chapters

Chapter 1: Introduction: Provides an overview of the global landmine problem, its impact on civilians, and the specific challenges and motivations behind developing a low-cost robotic solution.

Chapter 2: Landmines detection strategies: Reviews existing manual, mechanical, and robotic demining techniques, evaluating their strengths, limitations, and the necessity for improved autonomous methods.

Chapter 3: Demining robot techniques: Discusses critical robot technologies, focusing on motion planning algorithms and sensor fusion concepts essential for high-accuracy mine detection.

Chapter 4: Related Work: Analyzes previously published research on robotic landmine detection systems, highlighting successful models and identified gaps in current technologies.

Chapter 5: The proposed framework: Details the design and architecture of the proposed low-cost robotic system, including sensor integration and the complete coverage path planning approach.

Chapter 6: The Experimental Results: Presents the findings from simulations and field tests, demonstrating the efficacy of the proposed system in terms of detection accuracy and reliability.

Chapter 7: Conclusions: Summarizes the research findings, confirms the success of the prototype, and provides recommendations for future improvements such as nanotechnology applications.

Keywords

Landmines, Demining, Autonomous Mobile Robots, Sensor Fusion, Motion Planning, Complete Coverage Algorithm, Decision Level Fusion, Obstacle Avoidance, Low-Cost System, Mine Detection, Robotics, Artificial Intelligence, Embedded Systems, Signal Processing, Humanitarian Demining.

Frequently Asked Questions

What is the core focus of this research?

The work focuses on creating a cost-effective autonomous mobile robot capable of detecting and defusing buried landmines to protect human deminers.

What are the primary thematic areas explored?

The study explores robot locomotion, path planning, multi-sensor data integration (sensor fusion), and strategies for autonomous obstacle avoidance in minefields.

What is the main research objective?

The objective is to minimize the human risk and financial cost associated with demining by using a low-cost, multi-sensor robotic system that achieves high accuracy.

Which scientific methods are utilized?

The author employs complete coverage path planning algorithms, decision-level sensor fusion (using metal detectors, chemical sensors, cameras, and ultrasound), and experimental testing in simulated minefield environments.

What is covered in the main body of the work?

The main body covers the theoretical background of detection strategies, the motion planning techniques, the technical design of the proposed robotic framework, and the empirical results of the performance evaluation.

What are the characterizing keywords of the thesis?

Key terms include landmines, demining, sensor fusion, autonomous robots, complete coverage, and low-cost detection systems.

How does decision-level fusion improve the system?

Decision-level fusion integrates independent inputs from multiple sensors to confirm a detection; if multiple sensors report a mine, the probability of mine existence is confirmed, effectively reducing false alarms.

Why is the "Complete Coverage" algorithm important for this robot?

This algorithm ensures that the robot systematically scans every point in the field at least once, which is critical for ensuring no mines are overlooked in unknown or high-risk areas.