Sensing and Infrastructure Design for Robots: A Plan-Based Perspective

Currently there do not exist general-purpose robots, and the procedures by which robots are designed are often ad hoc. Additionally, designers must deal with considerations including budget, energy requirements, and the availability of parts, all of which complicate the problem. Abstract formal theories have, among other benefits, the potential to assist designers in developing and understanding the capabilities of novel robotic systems. Of particular interest is the concept of action-based sensors, which focus on the idea that a robot need only know enough to know what action to perform next. As a mathematical abstraction, action-based sensors prescribe actions to the agent; details of the sensor itself are irrelevant. From this information-oriented perspective, this concept also links planning directly to the design problem: the definition of what action “should” be taken depends upon the plan a robot is executing, serving to specify its desired behavior. While the theoretical abstractions of sensors are technology-neutral, we present ways to connect action-based sensors to the considerations and constraints faced by real robot designers. Action-based sensors have been formalized in terms of specific plans (informally those that take the fewest actions to achieve a goal), but there exist cases in which it is useful to consider other plans. In extending this formalization to include all plans, we find that certain plans have obstructions that prevent their expression as action-based sensors. We have developed an algorithm to remove these obstructions, which result from the interactions between a robot and its environment. After this, we move from the question of what a robot must sense about the environment to the question of how an environment should provide information. The use of infrastructure for spaces shared by multiple agents is another way in which designers can simplify tasks for agents. The complexity of this design problem arises from infrastructure’s ability to modify both what an agent observes and the outcome of actions. We present a method for modeling the impact of infrastructure to determine its utility to a given agent, and also consider how the utility of the infrastructure can vary depending on the differing needs of agents and how they make use of the environment. The present work, in addition to extending Erdmann’s original theory, focuses on the way in which information that must be retained by the agent can be contained within a plan’s structure. Use of a graph-based framework allows for us to identify if that structure is necessary for successful execution of the plan. This dissertation then shifts to a complementary design problem, examining the ability of infrastructure to externalize information and actuation requirements. It also presents a model for predicting the impact of introducing new infrastructure. Finally, it will explore the ways in which information can be used to estimate sensor failures in robots and bound the space of possible configurations. Transitioning from the design of robots and their environments to their operation, this dissertation also presents a method for estimating sensor failures. Through knowledge of the world structure and expected observations, inconsistencies can be tracked to form hypotheses on potential sensor failures. We introduce a lattice-based method of expressing these failures, as well as an algorithm for tracking inconsistencies. The algorithm allows for an often concise representation of a potentially exponential set of hypotheses, enabling use during a robot’s execution. This basis also allows for the robot to determine if a failure interferes with its ability to complete a task. We also present a method through which the sensors that are required for task completion can be determined at any point. The primary means to validate the theoretical results in this dissertation are a range of case studies. For action-based sensors, we consider several varieties of design problems including sensor selection and navigation problems. Moving beyond the sets of action-based sensors considered in these design problems, we also examine concise combinatorial representations for sets of sensors more generally, and apply these to settings involving robot self-diagnosis. For infrastructure, we provide a taxonomy as a guide by which to examine several different cases in which infrastructure is introduced to an environment. These case studies focus both on changes in agent behavior after being introduced, as well as ways in which the value of the introduced infrastructure can be deter-mined. For the identification of sensor failures, an example is also presented that demonstrates the concise nature of the model, particularly when compared to naïve methods