Diploma Thesis DIP-3630

BibliographyDuraki, Alen: An experimental analysis of optimization base motion planning.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Diploma Thesis No. 3630 (2014).
51 pages, english.
CR-SchemaI.2.8 (Problem Solving, Control Methods, and Search)
I.2.9 (Robotics)
Abstract

Through the advance of technology, robots aren’t just objects from science fiction novels, but they are becoming more and more part of our everyday life and are specially designed and constructed to support us. From little household helper that finish domestic chores to big industrial robots that assemble huge machines and automobiles, there are hundreds of different types of robots. Especially in interaction with humans it is important for a robot to react to environmental changes. In case of upcoming difficulties the robots ought not to freeze in place and take a lot of time to calculate their possibilities on how to avoid obstacles that might be in their way. Rapidly-exploring random trees and other sampling-based methods are one way to solve this problem, where robots are able to find a viable solution and execute it. However, the resulting solutions were not efficient enough and post-processing consisting of optimization methods had to be applied to smooth out the trajectories. The necessity of this optimization lead to a new generation of planning methods that were based on optimization alone. In many domains, these optimization-based motion planners constitute the state-of-the-art.

This thesis compares various optimization-based motion planners within an efficient common environmental representation with respect to the following criteria: speed, accuracy, and applicability. Through a variety of tests with arbitrary obstacles the methods will be compared and the results presented. The document concludes with an outlook on further work and possibilities.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Machine Learning und Robotics
Superviser(s)Ratliff, Nathan
Entry dateSeptember 9, 2014
   Publ. Computer Science