Collaborative Motion Planning a Dissertation to My Collaborators on Collaborative Motion Planning, Fellow Graduate Student V Read Sandström, Undergraduate Students

Planning motion is an essential component for any autonomous robotic system. An intelligent agent must be able to efficiently plan collision-free paths in order to move through its world. Despite its importance, this problem is PSPACE-Hard which means that even planning motions for simple robots is computationally difficult. State-of-the-art approaches trade completeness (always able to provide a solution if one exists or report none exists) for probabilistic completeness (probabilistically guaranteed to find a solution if one exists but cannot report if none exists) and improved efficiency. These methods use sampling-based techniques to design a sequence of motions for the robot. However, as these methods are random in nature, the probability of their success is directly related to the expansiveness, or openness, of the underlying planning space. In other words, narrow passages, complex systems, and various constraints make planning with these methods difficult. On the other hand, humans can often determine approximate solutions for these difficult solutions quickly. In this research, we explore user-guided planning in which a human operator works together with a sampling-based motion planner. By having a human-in-theloop, a human can steer a sampling-based planner towards a solution. This strategy can provide benefits to many applications such as computer-aided design and virtual prototyping, to name a few. We begin by classifying and creating simple models of common user-guided and heuristic-guided motion planning methods. Our models encompass three forms of user input: configuration-based, path-based, and region-based input. We compare and contrast these approaches and motivate our choice of a region-based collaborative