國立虎尾科技大學 |

Safety-Aware System Optimization for Autonomous Machines.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Safety-Aware System Optimization for Autonomous Machines./
Author:	Hsiao, Yu-Shun.
Description:	1 online resource (131 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Robotics. -
Online resource:	click for full text (PQDT)
ISBN:	9798382783307

Safety-Aware System Optimization for Autonomous Machines.
Hsiao, Yu-Shun.

Safety-Aware System Optimization for Autonomous Machines. - 1 online resource (131 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Harvard University, 2024.

Includes bibliographical references

Autonomous machines such as vehicles, drones, and robotic manipulators promise to transform the world by unleashing humans from repetitive, dangerous, and labor-intensive tasks. However, their widespread deployment requires advances in safety, real-time performance, and resilience. This thesis tackles these key challenges through end-to-end system optimization that maintains safety while improving performance and fault tolerance. We optimize the entire Perception-Planning-Control (PPC) computing pipeline that takes in sensor readings and output control commands. First, this thesis develops a model to quantify the perception processing rate requirements for safe autonomous driving in complex scenarios, connecting the varying real-time latency requirements with the operating scenarios. Second, we accelerate the time-consuming 3D mapping in perception. A specialized accelerator is designed that achieves substantially higher throughput and energy efficiency over a CPU, enabling real-time perception for 3D mapping. Third, we accelerate the computationally expensive optimization-based motion planning algorithms with a variable precision search method that reduces memory bandwidth pressure without sacrificing positional and orientational precision. Lastly, we assess autonomous machines' fault tolerance characteristics against real-world noises and errors to generate reliable control commands. We propose a fault characterization framework that evaluates the impact of silent data corruptions (SDCs) on application-level metrics. To mitigate SDCs, we propose lightweight anomaly detection techniques to recover failures in the computing pipeline with insignificant overhead. This dissertation enables the development of safe, real-time, and resilient autonomous machines. The contributions chart a path toward robust deployment of autonomous machines that can transform society.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382783307Subjects--Topical Terms:

561941
Robotics.
Subjects--Index Terms:

Autonomous systemsIndex Terms--Genre/Form:

554714
Electronic books.

Safety-Aware System Optimization for Autonomous Machines.
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