16.412J/6.834J Cognitive Robotics, Spring 2004

Final Projects

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(**To view Final Projects from previous semesters click here.**)

Spring 2005
Proposal
Final Report
Source
Students
Continuously Planning for Autonomous Navigation Using Conflict-Directed A* to Generate Temporal Flexible State Plans

[PDF]
N/A
  • Larry Bush
  • Brian Bairstow
  • Tony Jiminez
An Empirical Investigation Of Mutation Parameters and Their Effects on Evolutionary Convergence of a Chess Evaluation Function

[PDF]

[PDF]
  • Jeremie Pouly
  • Justin Fox
Mapping Contoured Terrain Using SLAM with a Radio-Controlled Helicopter Platform
  • Kaijen Hsiao
  • Jason Miller
  • Henry Lefebvre de Plinval-Salgues
Spatial Intention Recognition Using Optimal Margin Classifiers
  • Thomas Coffee
  • Shen Qu
  • Shannon Dong
Project Proposal Multiple Agent SLAM
  • Ethan Howe
  • Jennifer Novosad
A SIFT-Based Pictorial Image Model  
  • James Lenfestey
A Bayesian Net Inference Tool for Hidden State in Texas Hold’em Poker

[PDF]

[PDF]
N/A
  • Brian Mihok
  • Michael Terry
GPS Integrity Monitoring
  • Thomas Temple

 

 

Schedule


 Monday, May 9th  
10:35 Bush, Bairstow and Jimenez (24 min)
11:01 Lenfestey (8 min)
11:11 Fox and Pouly (16 min)
11:29 Coffee, Dong and Qu (24 min)
  Wednesday, May 11th  
10:35 de Plinval, Hsiao and Miller (24 min)
11:01 Temple (8 min)
11:11 Howe and Novosad (16 min)
11:29 Mihok and Terry (16 min)


The following final project summary is excerpted, in part, from the Final Project Guidelines (PDF).

Objectives

The purpose of the project is to develop a deep understanding of one or two methods for creating cognitive robots and intelligent embedded systems, and to innovate upon these methods, to lend novel insight into their behavior through analysis or to apply the method in an innovative manner.

More specifically, the student should demonstrate the ability to:

  • Clearly state and motivate an interesting, focused innovation to intelligent embedded systems. An innovation may be an important analytical question, a novel algorithmic extension or an innovative application.
  • Extract and evaluate the relevant literature using the web and library resources.
  • Provide a simple explanation for the algorithms used in the project, using pedagogical examples to highlight key features of the algorithm.
  • If a design project, describe the design of the intelligent embedded systems you are creating and the rationale for the method applied in the context of the project. If this is an analysis project, then described the experimental method that you are pursuing.
    Implement and demonstrate an algorithm or application in support of your project goals.
  • Evaluate the approach analytically and/or empirically.


Grading

  • A - represents mastery: the ability to analyze and extend existing methods in a way that is novel and insightful; the ability to explain and motivate in a manner that is particularly intuitive.
  • B - represents solid competence: the ability to articulately motivate, explain, implement and evaluate a focused set (i.e., 1 or 2) of intelligent embedded systems methods.
  • C - represents partial competence of the above.


Report Guidelines

The report should reveal a depth of understanding. It should communicate the objectives, core description, developments and results of the project. Three main elements to present in the report:

  • Articulation of the set of methods upon which your project is built, in a pedagogical (tutorial-like) manner.
  • Empirical and/or analytical evaluation and insights.
  • Innovation that involves applying and/or extending methods in a novel way.


Presentation Guidelines

Each team gives a presentation (approximately 5 minutes / group member). Selected student work from Spring 2004 is linked in the table below. A list of suggested topics is linked at the bottom of this section.

 

 

 

 

 

 

 

 

 

 

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