ICRA 2012 Paper Abstract

Close

Paper WeA06.5

Xu, Wenda (Peking University), Wei, Junqing (Carnegie Mellon University), Dolan, John M. (Carnegie Mellon University), Zhao, Huijing (Peking University), Zha, Hongbin (Peking University)

A Real-Time Motion Planner with Trajectory Optimization for Autonomous Vehicles

Scheduled for presentation during the Regular Session "Trajectory Planning and Generation" (WeA06), Wednesday, May 16, 2012, 09:30−09:45, Meeting Room 6 (Oya'te)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 19, 2017

Keywords Motion and Path Planning, Nonholonomic Motion Planning, Wheeled Robots

Abstract

In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low computational complexity and is able to converge to a higherquality solution within a few iterations. Compared with the planner without optimization, this framework can reduce the planning time by 52% and improve the trajectory quality. The proposed motion planner is implemented and tested both in simulation and on a real autonomous vehicle in three different scenarios. Experiments show that the planner outputs highquality trajectories and performs intelligent driving behaviors.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2017 PaperCept, Inc.
Page generated 2017-10-19  00:05:29 PST  Terms of use