Exact geometry algorithms for robotic motion planning

The current generation of robotic motion planning algorithms is dominated by derivatives of the PRM and RRT algorithms. These methods abstract away all geometric information about the underlying problem into a collision checker. While this approach yields simple and general purpose algorithms, it often comes at the cost of theoretical guarantees and performance. In this thesis, we explore deterministic motion planning algorithms that have explicit knowledge of the geometry of the underlying problems. By exploiting this geometry, we give algorithms that can achieve stronger theoretical guarantees and better performance in some problems. This thesis is divided into two main sections comprising of two different motion planning scenarios. In the first case, we explore issues of decidability in task and motion planning by giving a decision procedure for prehensile task and motion planning. In the second section, we present a holonomic motion planning algorithm that can almost always identify the exact optimal solution as a system of differential equations, which can be numerically solved to produce an asymptotically-optimal solution.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Deshpande, Ashwin.

Advisor dc:contributor.advisor

• Tomás Lozano-Pérez and Leslie P. Kaelbling.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122736

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122736