Title:
Machine learning and dynamic programming algorithms for motion planning and control

dc.contributor.advisor Tsiotras, Panagiotis
dc.contributor.author Arslan, Oktay
dc.contributor.committeeMember Feron, Eric
dc.contributor.committeeMember Theodorou, Evangelos
dc.contributor.committeeMember Dellaert, Frank
dc.contributor.committeeMember Song, Le
dc.contributor.committeeMember Karaman, Sertac
dc.contributor.department Aerospace Engineering
dc.date.accessioned 2016-01-07T17:22:27Z
dc.date.available 2016-01-07T17:22:27Z
dc.date.created 2015-12
dc.date.issued 2015-11-16
dc.date.submitted December 2015
dc.date.updated 2016-01-07T17:22:27Z
dc.description.abstract Robot motion planning is one of the central problems in robotics, and has received considerable amount of attention not only from roboticists but also from the control and artificial intelligence (AI) communities. Despite the different types of applications and physical properties of robotic systems, many high-level tasks of autonomous systems can be decomposed into subtasks which require point-to-point navigation while avoiding infeasible regions due to the obstacles in the workspace. This dissertation aims at developing a new class of sampling-based motion planning algorithms that are fast, efficient and asymptotically optimal by employing ideas from Machine Learning (ML) and Dynamic Programming (DP). First, we interpret the robot motion planning problem as a form of a machine learning problem since the underlying search space is not known a priori, and utilize random geometric graphs to compute consistent discretizations of the underlying continuous search space. Then, we integrate existing DP algorithms and ML algorithms to the framework of sampling-based algorithms for better exploitation and exploration, respectively. We introduce a novel sampling-based algorithm, called RRT#, that improves upon the well-known RRT* algorithm by leveraging value and policy iteration methods as new information is collected. The proposed algorithms yield provable guarantees on correctness, completeness and asymptotic optimality. We also develop an adaptive sampling strategy by considering exploration as a classification (or regression) problem, and use online machine learning algorithms to learn the relevant region of a query, i.e., the region that contains the optimal solution, without significant computational overhead. We then extend the application of sampling-based algorithms to a class of stochastic optimal control problems and problems with differential constraints. Specifically, we introduce the Path Integral - RRT algorithm, for solving optimal control of stochastic systems and the CL-RRT# algorithm that uses closed-loop prediction for trajectory generation for differential systems. One of the key benefits of CL-RRT# is that for many systems, given a low-level tracking controller, it is easier to handle differential constraints, so complex steering procedures are not needed, unlike most existing kinodynamic sampling-based algorithms. Implementation results of sampling-based planners for route planning of a full-scale autonomous helicopter under the Autonomous Aerial Cargo/Utility System Program (AACUS) program are provided.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/54317
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Robotic motion planning
dc.subject Sampling-based algorithms
dc.subject Rapidly-exploring random trees
dc.subject Dynamic programming
dc.subject Machine learning
dc.subject Closed-loop prediction
dc.subject High-level route planning
dc.title Machine learning and dynamic programming algorithms for motion planning and control
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Tsiotras, Panagiotis
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAdvisorOfPublication bd4969ec-a869-452f-81f1-9f2dc8118b3c
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
thesis.degree.level Doctoral
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