INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING

Sampling-based methods have emerged as a promising technique for solving robot motion-planning problems. These algorithms avoid a priori discretization of the search-space by generating random samples and building a graph online. While the recent advances in this area endow these randomized planners with asymptotic optimality, their slow convergence rate still remains a challenge. One of the reasons for this poor performance can be traced to the widely used uniform sampling strategy that naively explores the entire search-space. Having access to an intelligent exploration strategy that can focus search, would alleviate one of the critical bottlenecks in speeding up these algorithms. This thesis endeavors to tackle this problem by presenting exploration algorithms that leverage different sources of information available during planning time.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Aerospace Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Joshi, Sagar Suhas

Advisor dc:contributor.advisor

• Tsiotras, Panagiotis

Committee members dc:contributor.committeemember

• Hutchinson, Seth
• Gombolay, Matthew
• Ravichandar, Harish
• Boots, Byron

• Robotics
• Motion Planning
• Learning for Robotics

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/66535

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/66535