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I recently purchased Lego’s new NXT hobbyist robotics kit. After familiarizing myself with the basic layout of the command module, available hardware and sensors, and third-party programing languages by building several single-task robots, I decided to use the NXT as a forum for my studies in SLAM robotics. As I have previously quoted, Dr. John Leonard of MIT says,

The problem of SLAM is stated as follows: starting from an initial position, a mobile robot travels through a sequence of positions and obtains a set of sensor measurements at each position. The goal is for the mobile robot to process the sensor data to produce an estimate of its position while concurrently building a map of the environment.

My goal and new perspective on the problem is to design a system which is as simple as possible. I immediately realize that much of the complication in SLAM systems lays in the timeliness with which the system processes information in real time. I know that some compromise between speed and simplicity had to be established, and in pursuing the most simple system, I am willing to sacrifice a lot in speed. So I have created what I am calling the “slam five robot”. The real acronym is S.S.S.S.S.L.A.M. which stands for “super-simple, super-slow, simultaneous localization and mapping robot”.

The system consists of three components: the physical hardware of the robot, the control algorithm which guides the hardware and sensors, and the post processing algorithm which interprets the sensor data. I begin here by describing the hardware and sensor setup.

Front view of the SLAM-5 bot, showing ultrasonic range finder and wiring for the drive motors.

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Georgia Tech’s Robotics Initiative has continued the free lecture series this semester. The bad news is that this semester’s schedule has been so poorly advertised to the public (no eye catching flyers like last semester) that I missed the first three of the year. The good news is that if you search for it, you’ll find that this semester’s schedule has lectures planned almost every week.

Last week’s guest speaker was Dr. Oussama Khatib, who is a member of Stanford University’s Artificial Intelligence Lab and Professor in computer science. Furthermore, he is the President of the International Foundation of Robotics Research. Dr. Khatib presented his research which focuses on human-centered and human-friendly robotics.
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Several weeks ago , I had the pleasure of attending another lecture organized by the Robotics Initiative at Georgia Tech . This time the speaker was Dr. John Leonard who presented his current research in Simultaneous Localization And Mapping (SLAM). Dr. Leonard is a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). His lecture was an amalgamate of theory, research applications, and future aspirations.

Dr. Leonard concisely describes SLAM by saying on his website:

The problem of SLAM is stated as follows: starting from an initial position, a mobile robot travels through a sequence of positions and obtains a set of sensor measurements at each position. The goal is for the mobile robot to process the sensor data to produce an estimate of its position while concurrently building a map of the environment.

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Last week, Dr. Steven M. LaValle, a Professor of Computer Science at The University of Illinois at Urbana-Champaign, presented as part of Georgia Tech’s Robotics and Intelligent Machines (RIM) Seminar Series. This was the second is a series of eight seminars spanning the Fall 2006 semester and I had the pleasure of attending and hear him speak.

Dr. LaValle structured his lecture around his new book, Planning Algorithms, which is available for free download here. He describes the book by saying:

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

While he was there to present his entire book, he chose to focus on three areas of overlap between mechanical engineering, electrical engineering, and computer science which present “great potential for impact”: motion planning with differential constraints, feedback motion planning, and information spaces.
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