Embed Size (px)
Citation preview
Carnegie Mellon University THE ROBOTICS INSTITUTE
Manual collec*on of environmental data over a large area can be a *me-‐consuming, costly, and even dangerous process, making it a perfect candidate for automa*on with mobile robots. Despite this clear suitability and numerous advances in robo*cs resul*ng in decreased costs, improved reliability, and increased ease of use, the problem of powering autonomous robots has proved to be an effec*ve deterrent to their widespread use in the field.
Although many survey scenarios involve domains with ample ambient energy present in the form of winds or currents that could be exploited by a robot opera*ng given an appropriate strategy, past path planning research has neglected the study of energy-‐efficient methods in these domains, in lieu of con*nued pursuit of *me-‐ and length-‐op*mal planning algorithms. Furthermore, much of the limited work addressing this topic relies on prior knowledge of the energy distribu*on within the domain, which can be par*cularly difficult and expensive to determine, especially when moving fluids are involved. In this thesis we address the problem of planning energy-‐efficient paths that exploit ambient energy in the absence of complete a priori knowledge of the domain.
Although work on energy-‐efficient planning con*nues, the methods developed consistently rely on a priori models of the vehicle or environment to achieve energy savings. This gap in research is par*cularly stark when energy-‐efficient coverage path planning is considered; a significant por*on of the past work on this problem makes use of vehicle dynamics models and generally results in coverage plans that op*mize the number of turns or the velocity along the path, with just a few studies considering the harvest of ambient energy during coverage execu*on. This thesis inves*gates the development of coverage planning techniques that integrate the gathering of highly prac*cal domain knowledge with its exploita*on to achieve autonomous energy-‐efficient informa*on gathering. To this end we improve upon exis*ng LSPIV current measurement methods and contribute a novel constraint-‐based coverage path planner, which given even a few fuzzy domain energy constraints and incomplete domain knowledge, is believed to produce energy-‐efficient coverage plans that will outperform plans produced by tradi*onal methods. The addi*on of informa*on gain constraints can be used to bias the vehicle towards explora*on to acquire addi*onal domain knowledge that may further improve energy-‐efficiency, par*cularly when ini*al domain knowledge is limited.
The par*cular mo*va*ng applica*on behind this work is the dense mapping of environmental parameters in riverine environments using autonomous surface vehicles (ASVs) while exploi*ng evolving surface current knowledge to improve energy-‐efficiency throughout the process. To address this problem, we apply our coverage planner to compute complete coverage strategies around energy and informa*on gain constraints provided by our enhanced LSPIV surface current measurement system. In order to mo*vate and validate this work, we describe and present results from its applica*on to a scenario where an ASV is deployed to survey the bathymetry in a sec*on of river using an energy-‐efficient coverage strategy, which is ini*ally computed with incomplete surface current data and later improved by opportunis*c devia*on from the ini*al plan.
Thesis Committee:
John Dolan Co-chair
Paul Scerri Co-chair
George Kantor
Mel Siegel
Jordi Albó La Salle University
THESIS PROPOSAL Planning for Energy-Efficient Coverage and Exploratory Deviation by Robots in Rivers
Monday, May 7, 2018
1507 Newell Simon Hall 12:00 p.m.
Christopher Tomaszewski Abstract
