Content Comparison

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• 1. Overview

• 2. Landmark Navigation

  • Block Diagram
    

• 3. Indoor Map Representation

  • IndoorGML - Indoor Geographic Markup Language
    
  • Cellular Space Model
  • Map generation Tool
    

• 4. Path Planning

  • Mobile Platform Model
    
  • Algorithms

• 5. Simulation and Results

  • Simulation Using VREP
    
  • Analysis
  • Prototype
  • Future work
    

• References

   

1. Overview

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This work shows an implementation and simulation of a path planning algorithm into an indoor structured environment.

Source Code

All the source code and project files are available through the system Bitbucket in the following link:

Image Added	https://hmontelo@bitbucket.org/hmontelo/me_396d_final_project.git

2. Landmark Navigation

Landmarks are distinct features that a mobile platform or robot can recognize from its sensory input. Landmarks can be geometric shapes (e.g., rectangles, lines, circles), and they may include additional information (e.g., in the form of bar-codes). In general, landmarks have a fixed and known position, relative to which a robot can localize itself. Landmarks are carefully chosen to be easy to identify; for example,  there must be sufficient contrast to the background. Before a robot can use landmarks for navigation, the characteristics of the landmarks must be known and stored in the robot's memory. The main task  in localization is then to recognize the landmarks reliably and to calculate the robot's position.

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Design	Generated MAP (Isometric View)	Example of GML file

4. Path Planning

Mobile Platform Model (used in the simulation)

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Generalized Coordinates
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where:
x and y are the two coordinates of the origin P of the moving frame θ is the orientation angle of the mobile robot Image Added is the rotation angle of the right driving wheel Image Added is the rotation angle of the left driving wheel The vehicle velocity v can be found in equation in the equation: Image Added Image Added is the angular velocity of the right wheel Image Added is the angular velocity of the left wheel

Kinematic Constraints
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Algorithm

A* Algorithm	Time Enhanced A* Algorithm
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5. Simulation and Results

Simulation Using

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VREP

Image Modified	Multimedia name sim_vrep_01.mp4

Analysis

• Temporal Analysis
  
  Test 1: Example of an overtaking. Two robots, 1 and 2, follow in the same direction with the same speed. Vehicle 2 overtakes the vehicle 1.

  Test2: Example of two paths without intersections.

  Test3: Example of the intersection of two Robots, followed by the passage and overtake of vehicle 2 by the vehicle 1.

  
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• Vehicle Crossing  
  

  Using A*	Using Time Enhanced A*
  Image Added	Image Added

   

• The implemented algorithms handles these problems in different ways. The Time enhanced A* Algorithm considers path planning and coordination simultaneously.
• The path of each vehicle is calculated according to the paths of the other vehicles. In this sense, the coordination between robots is ensured.
• The vehicles’ path is restricted to the predefined segments, while the Time enhanced A* gives the vehicle the flexibility to navigate in all free cells.
• Considering that the Time enhanced A* Algorithm includes time and movements of the other vehicles on their paths’ calculation, overtaking is allowed.
• It’s possible to conclude that the Time enhanced A* Algorithm is more flexible in the generation of paths, because the calculated paths can be adjusted according to their cost, obstacles and the other Robot movements

Prototypes

	Multimedia name 4wheel_Proto.mp4

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Future

References

1 U.S. Government. National Space-Based Positioning, Navigation, and Timing Coordination Office’s GPS information, 2010.

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5 OpenStreetMap. OpenStreetMap, 2010.

6 DuToit, N. (2010). Robotic Motion Planning in Dynamic, Cluttered, Uncertain Environments. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)

7  Knepper, R. A. and Kelly, A. (2006). High Performance State Lattice Planning Using Heuristic Look-Up Tables. In Proceedings of the IEEE-RSJ International Conference on Intelligent Robots and Systems (IROS)