Information-theoretic active perception for multi-robot teams / Charrow, Benjamin.

Format:

Book

Thesis/Dissertation

Author/Creator:

Charrow, Benjamin, author.

Contributor:

Michael, Nathan, degree supervisor.

Kumar, Vijay, degree supervisor.

Taylor, Camillo, degree committee member.

Schwager, Mac, degree committee member.

Ribeiro, Alejandro, degree committee member.

Lee, Daniel, degree committee member.

University of Pennsylvania. Computer and Information Science, degree granting institution.

Language:

English

Subjects (All):

Robotics.

0771.

Local Subjects:

Robotics.

0771.

Genre:

Academic theses.

Physical Description:

1 electronic resource (175 pages)

Contained In:

Dissertation Abstracts International 77-01B(E).

Place of Publication:

Ann Arbor : ProQuest Dissertations & Theses, 2015.

Language Note:

English

Summary:

Multi-robot teams that intelligently gather information have the potential to transform industries as diverse as agriculture, space exploration, mining, environmental monitoring, search and rescue, and construction. Despite large amounts of research effort on active perception problems, there still remain significant challenges. In this thesis, we present a variety of information-theoretic control policies that enable teams of robots to efficiently estimate different quantities of interest. Although these policies are intractable in general, we develop a series of approximations that make them suitable for real time use.

We begin by presenting a unified estimation and control scheme based on Shannon's mutual information that lets small teams of robots equipped with range-only sensors track a single static target. By creating approximate representations, we substantially reduce the complexity of this approach, letting the team track a mobile target. We then scale this approach to larger teams that need to localize a large and unknown number of targets.

We also examine information-theoretic control policies to autonomously construct 3D maps with ground and aerial robots. By using Cauchy-Schwarz quadratic mutual information, we show substantial computational improvements over similar information-theoretic measures. To map environments faster, we adopt a hierarchical planning approach which incorporates trajectory optimization so that robots can quickly determine feasible and locally optimal trajectories. Finally, we present a high-level planning algorithm that enables heterogeneous robots to cooperatively construct maps.

Notes:

Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.

Advisors: Vijay Kumar; Nathan Michael Committee members: Daniel Lee; Alejandro Ribeiro; Mac Schwager; Camillo Taylor.

Ph.D. University of Pennsylvania 2015.

Local Notes:

School code: 0175

ISBN:

9781339026749