Improving Trajectory Optimization Using a Roadmap Framework
Name
1811.02044.pdf
Description
Accepted version
Size
3.1 MB
Format
Adobe PDF
Checksum (MD5)
b336bd7a9786ad51a554679d74b496bc
Author(s)
Dai, Siyu
Orton, Matthew Ralph
Schaffert, Shawn
Hofmann, Andreas
Williams, Brian C
Date Issued
October 2018
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dai, Siyu, Orton, Matthew Ralph, Schaffert, Shawn, Hofmann, Andreas and Williams, Brian C. 2018. "Improving Trajectory Optimization Using a Roadmap Framework." IEEE International Conference on Intelligent Robots and Systems.
Version
Author's final manuscript
Abstract
© 2018 IEEE. We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application scenarios with 5000 test cases each, we show that optimization-based or sampling-based planners alone are not effective for realistic problems where fast planning times are required. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the combination of our sparse roadmap and trajectory optimization provides superior performance over other standard sampling-based planners' combinations. By using a multi-query roadmap instead of generating completely new trajectories for each planning problem, our approach allows for extensions such as persistent control policy information associated with a trajectory across planning problems. Also, the sub-optimality resulting from the sparsity of roadmap, as well as the unexpected disturbances from the environment, can both be overcome by the real-time trajectory optimization process.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2018.8594274
