Browse wiki

From MurrayWiki
Jump to: navigation, search
Optimization-Based Navigation for the DARPA Grand Challenge
Abstract This research addresses the path planning
This research addresses the path planning problem with a nonlinear optimization method running in real time. An optimization problem is continually solved to find a time-optimal, dynamically feasible trajectory from the vehicle’s position to some receding horizon ahead (20m-70m forward). The locally optimal numerical solver optimizes both the spatial and temporal components of the trajectory simultaneously, and feeds its output to a trajectory-following controller. The method has been implemented and tested on a modified Ford E350 van. Using one stereo pair and four LADAR units as terrain sensors, the vehicle was able to consistently traverse a 2 mile obstacle course at the DGC qualifying event. At the main DGC event, the vehicle drove 8 autonomous miles through the Nevada desert before experiencing non-planning issues. During this time, the planning system generated a plan 4.28 times per second on average. This execution speed, coupled with a feedback-based trajectory-following controller was shown to be adequate at providing smooth and reliable obstacle avoidance even on complicated terrain.
cle avoidance even on complicated terrain.  +
Authors Dmitriy Kogan and Richard M. Murray  +
Funding Correct-by-Construction Synthesis of Control Protocols for Aerospace Systems +
ID 2006k  +
Source Submitted, 2006 Conference on Decision and Control  +
Tag km06-cdc  +
Title Optimization-Based Navigation for the DARPA Grand Challenge +
Type Conference Paper  +
Categories Papers
Modification date
This property is a special property in this wiki.
15 May 2016 06:17:35  +
URL
This property is a special property in this wiki.
http://www.cds.caltech.edu/~murray/preprints/km06-cdc.pdf  +
hide properties that link here 
Optimization-Based Navigation for the DARPA Grand Challenge + Title
 

 

Enter the name of the page to start browsing from.