Efficient computing for autonomous navigation using algorithm-and-hardware co-design

Autonomous navigation algorithms are the backbone of many robotic systems, such as self-driving cars and drones. However, state-of-the-art autonomous navigation algorithms are computationally expensive, requiring powerful CPUs and GPUs to enable them to run in real time. As a result, it is prohibitive to deploy them on miniature robots with limited computational resources onboard. To tackle this challenge, this thesis presents an algorithm-and-hardware co-design approach to design energy-efficient algorithms that are optimized for dedicated hardware architectures at the same time. It covers the design for three essential modules of an autonomous navigation system: perception, localization, and exploration.

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

• Zhang, Zhengdong,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Vivienne Sze.

• 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/1721.1/122691

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122691