Design methods for cost-effective teams of mobile robots in uncertain terrain

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 59-62).Conducting planetary exploration missions with mobile robots is expensive, with costs ranging from hundreds of millions to billions of dollars. Developing reliable robots to work remotely on rough, uncertain terrain is imperative for these missions. One potential tactic for improving the cost-effectiveness of these missions is to distribute the mass allowance for the mission over a team of smaller robots, rather than using a single robot. However, there is limited work on determining the size and design for a team of robots to provide the best overall performance when operating on hazardous terrain. This thesis develops a framework for designing mass-restricted, homogenous teams of mobile robots that will operate in a region with uncertain terrain conditions. The framework is built around three models: a four-wheeled robot model, a probabilistic model of terrain hazards, and a robot-terrain interaction model. The models are formulated into an optimization problem that can be used to determine the best design for a team of robots based on the team's combined equivalent straight-line velocity (CESLV), a novel measure of mission performance. CESLV is an effective measure of mission performance for both predetermined (static) mission plans and dynamic mission plans, where observations made by the robots can change the future mission tasks. A graphical user interface (GUI) is also presented which allows a designer to explore the design tradespace for the team of robots while considering important factors that are not captured by the models. In a case study of a Mars exploration mission, a team of robots provides superior performance to a single robot. A sensitivity analysis shows that the optimal size of the robot team is robust to inaccuracy in the terrain conditions. Additionally, the tradespace UI captures a trend in robot team design that would have otherwise gone unnoticed.by Nathaniel Steven Michaluk.S.M