ICRA'09 Paper Abstract

Close

Paper FrA4.3

Kushleyev, Aleksandr (University of Pennsylvania), Likhachev, Maxim (University of Pennsylvania)

Time-Bounded Lattice for Efficient Planning in Dynamic Environments

Scheduled for presentation during the Regular Sessions "Motion and Path Planning - I" (FrA4), Friday, May 15, 2009, 09:10−09:30, Room: 402

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Motion and Path Planning, Autonomous Navigation, Nonholonomic Motion Planning

Abstract

For vehicles navigating initially unknown cluttered environments, current state-of-the-art planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dynamic. Planning under these conditions is more difficult for two reasons. First, tracking and predicting the trajectories of moving objects (i.e., cars, humans) is very noisy. Second, the planning process is computationally more expensive because of the increased dimensionality of the state-space, with time as an additional variable. Moreover, re-planning needs to be invoked more often since the trajectories of moving obstacles need to be constantly re-estimated.

In this paper, we develop a path planning algorithm that addresses these challenges. First, we choose a representation of dynamic obstacles that efficiently models their predicted trajectories and the uncertainty associated with the predictions. Second, to provide real-time guarantees on the performance of planning with dynamic obstacles, we propose to utilize a novel data structure for planning - a time-bounded lattice - that merges together short-term planning in time with long-term planning without time. We demonstrate the effectiveness of the approach in both simulations with up to 30 dynamic obstacles and on real robots.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2022 PaperCept, Inc.
Page generated 2022-01-21  10:09:38 PST  Terms of use