Syllabus

Course Meeting Times

Lectures: 2 sessions/week, 1 hour/session

Labs: 2 sessions/week, 3 hours/session

Prerequisites

Although this is listed as a graduate course, undergraduates are more than welcome to register. The target class size is 20 students, 10 undergraduates and 10 graduate students. Graduate students are required to complete additional assignments, or work in smaller teams on group projects.

Students should have experience in programming and the Linux or OS X environment (familiarity with interacting via the command-line). A Linux/C++ boot camp will be offered in the first full week of the class for students with less experience in the above areas wishing to sharpen their skills.

In water experiments and competitions with the marine vehicles will be done with provided platforms with little or no need for physical modifications. The emphasis in this course is on the algorithmic concepts of autonomy, sensing and communications, not the hardware. Students looking for a greater focus on hardware and mechanical design may consider 2.017.

Course Description

This course covers basic topics in autonomous marine vehicles, focusing mainly on software and algorithms for autonomous decision making (autonomy) by underwater vehicles operating in the ocean environments, autonomously adapting to the environment for improved sensing performance. It will introduce students to underwater acoustic communication environment, as well as the various options for undersea navigation, both crucial to the operation of collaborative undersea networks for environmental sensing. Sensors for acoustic, biological and chemical sensing by underwater vehicles and their integration with the autonomy system for environmentally adaptive undersea mapping and observation will be covered. The subject will have a significant lab component, involving the use of the MOOS-IvP autonomy software infrastructure for developing integrated sensing, modeling and control solutions for a variety of ocean observation problems, using simulation environments and a field testbed with small autonomous surface craft and underwater vehicles operated on the Charles River.

Course Objectives

Education Objectives of the Course

• Understand the relationship between autonomy, sensing, navigation and control on an un- manned marine vehicle.
• Field an autonomous system, from mission and software configuration planning, mission monitoring of activities, launch and recovery of the vehicle and post-mission analysis and debugging techniques.
• Augment the baseline vehicle autonomy software with student developed modules, tested and verified in simulation prior to field exercises.
• Understand common vehicle sensors, and sensor processing algorithms, and how mission autonomy affects the quality of sensor gathering algorithms.
• Use several modes of inter-vehicle communications, and understand the strengths and weaknesses of each mode in terms of latency, bandwidth, reliability and cost, as it relates to effectively configuring a vehicle mission needing connectivity between platforms and humans.
• Understand the basic tools needed to effectively develop software for robotic platforms in a group environment, and resolve conflicts and adhere to group goals in the software cycle.

Equipment

This class will be contain several in-water project assignments using small unmanned surface craft launched from the MIT Sailing Pavilion. Wiki pages are available for both the Clearpath Robotics Kingfisher vehicles and the Robotic Marine Systems autonomous kayaks. Students will have write access to the wiki pages during the semester.

• Kingfishers
• Autonomous Kayaks

Grading