Accelerated Motion Planning Through Hardware/Software Co-Design

Loading...
Thumbnail Image

Date

2019

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

274
views
337
downloads

Abstract

Robotics has the potential to dramatically change society over the next decade. Technology has matured such that modern robots can execute complex motions with sub-millimeter precision. Advances in sensing technology have driven down the price of depth cameras and increased their performance. However, the planning algorithms used in currently-deployed systems are too slow to react to changing environments; this has restricted the use of high degree-of-freedom (DOF) robots to tightly-controlled environments where planning in real time is not necessary.

Our work focuses on overcoming this challenge through careful hardware/software co-design. We leverage aggressive precomputation and parallelism to design accelerators for several components of the motion planning problem. We present architectures for accelerating collision detection as well as path search. We show how we can maintain flexibility even with custom hardware, and describe microarchitectures that we have implemented at the register-transfer level. We also show how to generate effective planning roadmaps for use with our designs.

Our accelerators bring the total planning latency to less than 3 microseconds, several orders of magnitude faster than the state of the art. This capability makes it possible to deploy systems that plan under uncertainty, use complex decision making algorithms, or plan for multiple robots in a workspace. We hope this technology will push robotics into domains and applications that were previously infeasible.

Description

Provenance

Citation

Citation

Murray, Sean (2019). Accelerated Motion Planning Through Hardware/Software Co-Design. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/19887.

Collections


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.