Planning to Fail: Incorporating Reliability into Design and Mission Planning for Mobile Robots

Abstract

Current mobile robots generally fall into one of two categories as far as reliability is concerned -- highly unreliable, or very expensive. Most fall into the first category, requiring teams of graduate students or staff engineers to coddle them in the days and hours before a brief demonstration. The few robots that exhibit very high reliability, such as those used by NASA for planetary exploration, are very expensive. In order for mobile robots to become more widely used in real-world environments, they will need to have reliability in between these two extremes. In many applications some amount of unreliability is acceptable if it results in reduced costs. Even in applications where a failure probability very near zero is desired (such as planetary exploration), the ability to design robots to a specific reliability goal should allow us to reduce the costs of these highly reliable robots by designing them to be "just reliable enough" to complete the mission, rather than designing them to be "as reliable as possible." In order to design mobile robots with respect to reliability, we need quantitative models for predicting robot reliability and for relating reliability to other design parameters such as cost. To date, however, there has been very little formal discussion of reliability in the mobile robotics literature, and no general method has been presented for quantitatively predicting the reliability of mobile robots. This thesis focuses on this problem of predicting reliability for mobile robots and in particular for teams of mobile robots, and proposes solutions for using reliability as a design input for several mobile robot design problems: Given a choice of components from which to assemble a robot, how do we select the ones which will optimize the tradeoff of reliability against other factors such as cost? Given a choice of robots from which to assemble a multirobot team, how do we select the ones which will optimize the reliability tradeoffs for the entire robot team? Given a multirobot team and a list of mission tasks, how do we assign tasks to team members in order to maximize the probability of completing the mission?