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Table of contents
Acknowledgments
1 Introduction: Networks of Realistic Robots
1.1 Distributed Robotics
1.2 Motivation
1.3 The OBLOT Model
1.4 More Realistic Mobile Robots
1.4.1 Sensors
1.4.2 Transparency and Size
1.4.3 Environment
1.4.4 Memory and Communication
1.4.5 Synchronicity
1.4.6 Fairness and Boundedness
1.4.7 Rigid Motion
1.4.8 Faults
1.5 A Realistic Example: Collision Avoiding Blind Robots
1.6 Our Contributions
1.6.1 Published Work
I The Power of Lights
2 The LUMINOUS Model
2.1 OBLOT FSYNC versus LUMINOUS SSYNC
3 Benchmark: Two-Robot Gathering
3.1 2-color Impossibility ?
3.2 Our Algorithm: 2-color Rendezvous
4 Model Checking Rendezvous Algorithms
4.1 System Model
4.1.1 Configurations and Executions
4.1.2 Self-Stabilization
4.2 From the System Model to the Verification Model
4.2.1 Simple vs. Complete Self-Stabilization
4.2.2 Self-Stabilization and Rigidity
4.2.3 Proving Rendezvous Algorithms
4.3 Verification Model
4.3.1 Position
4.3.2 Activation and Synchrony
4.3.3 Movement Resolution
4.3.4 State Variables
4.3.5 Activation Phases
4.3.6 The Case of Non-Rigid, Non-Self-Stabilizing Algorithms
4.4 Checking Rendezvous Algorithms
4.4.1 Verified Algorithms
4.4.2 Verification by Model Checking
4.4.3 Performance
4.5 Investigating Lights with Weaker Consistency Guarantees
5 Safe and Unbiased Leader Election with Lights
5.1 Details of the Model
5.2 Problem Definition
5.3 Leader Election Based on Motion
5.4 Leader Election Based on Lights
5.5 Safe Leader Election
5.6 Unbiased Leader Election
5.7 Safe Unbiased Leader Election
Conclusion: The Power of Lights
II Unreliable Vision
6 Uncertain Visibility
6.1 Model Definition and Basic Results
6.2 FSYNC n robots Gathering
6.3 FSYNC Uniform Circle Formation
6.4 FSYNC Leader election
6.5 FSYNC LUMINOUS Rendezvous
7 Obstructed Visibility
7.1 Model and Problem Definition
7.2 Simplifying the Problem: Line Theorem
7.3 Obstruction Detection for the Line Configuration
7.4 Non-Line Obstruction Detection: a Simple Approach
7.5 Non-Line Obstruction Detection: Using a Token
7.5.1 Difficulty of Creating a Token with Obstructed Visibility
7.5.2 Algorithm Architecture
7.5.3 A Possible Solution
7.5.4 Gathering Information and Transmitting the Token
7.5.5 The Issue of Proving Obstructed Algorithms
7.5.6 Sidenote: Ensuring Token Unicity for a Line
Conclusion: Unreliable Vision
III Real World Performance
8 Monte-Carlo Simulation of Mobile Robots
8.1 Motivation
8.2 Overview of the Framework
8.3 Scheduling
8.4 Simulation Conditions
8.5 Existing Simulators
8.6 Limitations of the Simulation
8.6.1 Halting the Simulation: Victory and Defeat Conditions
8.6.2 The Consequences of the Discretized Euclidean Plane
9 Fuel Efficiency in the Usual Settings
9.1 Rendezvous Algorithms
9.2 Convergence For n Robots
10 Analyzing Algorithms in Realistic Settings
10.1 Visibility Sensor Errors
10.2 Convergence for n=2 Robots
10.3 Compass Errors
10.4 Geoleader Election
10.5 Errors in Color Perception
11 Improved Convergence and Leader Election
11.1 Fuel Efficient Convergence
11.2 Error Resilient Geoleader Election
11.2.1 Geoleader Election for Four Robots
11.2.2 Proposed Algorithm
Conclusion: RealWorld Performance
12 Conclusion: Networks of Realistic Robots
12.1 Our contributions
12.1.1 Published Work
12.2 Short-Term Perspectives
12.2.1 Analyzing More Models and Algorithms
12.2.2 Gathering of n Robots Using Two Colors
12.3 Long-Term Perspectives
12.3.1 A Proven Simulator
12.3.2 Stronger Simulator Adversaries
12.3.3 Obstruction Detection
12.3.4 Expanding Uncertain Visibility
12.3.5 Robots with Finite Memory Snapshots
A Appendix: Details and Results of the Model Checker
A.1 Movement Resolution
A.2 Verified Algorithms Written in Promela
A.3 Compile Options
A.4 Output
A.4.1 Vig2Cols in ASYNC (failure)
A.4.2 Her2Cols in ASYNC (Success)
B Appendix: Example of an Instance of the Simulator
C Appendix: Details of Color Perception Error
List of Acronyms
Bibliography
