Abstract

Improvements in robot autonomy are changing human-robot interaction from low-level manipulation to high-level task-based collaboration. When a robot can independently and autonomously executes tasks, a human in a human-robot team acts as a collaborator or task supervisor instead of a tele-operator. When applying this to planning paths for a robot's motion, it is very important that the supervisor's qualitative intent is translated into aquantitative model so that the robot can produce a desirable consequence. In robotic path planning, algorithms can transform a human's qualitative requirement into a robot's quantitative model so that the robot behavior satisfies the human's intent. In particular, algorithms can be created that allow a human to express multi-objective and topological preferences, and can be built to use verbal communication. This dissertation presents a series of robot motion-planning algorithms, each of which is designed to support some aspect of a human's intent. Specifically, we present algorithms for the following problems: planning with a human-motion constraint, planning with a topological requirement, planning with multiple objectives, and creating models of constraints, requirements, and objectives from verbal instructions. These algorithms create a set of robot behaviors that support flexible decision-making over a range of complex path-planning tasks.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2016-08-01

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd8899

Keywords

Path Planning, Human-Robot Interaction, Language Understanding

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