Robust Collision Avoidance via Sliding Control
Name
lopezICRAfinal.pdf
Description
Accepted version
Size
3.46 MB
Format
Adobe PDF
Checksum (MD5)
a288efe306705f10a2bb9042462d31c7
Author(s)
Lopez, Brett T.
Slotine, Jean-Jacques E
How, Jonathan P.
Date Issued
September 2018
Journal
2018 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Lopez, Brett T., Slotine, Jean-Jacques and How, Jonathan P. "Robust Collision Avoidance via Sliding Control." 2018 IEEE International Conference on Robotics and Automation, 21-25 May 2018, Brisbane, QLD, Australia, edited by Kevin Lynch et al. Institute of Electrical and Electronics Engineers (IEEE), 2018
Version
Author's final manuscript
Abstract
Recent advances in perception and planning algorithms have enabled robots to navigate autonomously through unknown, cluttered environments at high-speeds. A key component of these systems is the ability to identify, select, and execute a safe trajectory around obstacles. Many of these systems, however, lack performance guarantees because model uncertainty and external disturbances are ignored when a trajectory is selected for execution. This work leverages results from nonlinear control theory to establish a bound on tracking performance that can be used to select a provably safe trajectory. The Composite Adaptive Sliding Controller (CASC) provides robustness to disturbances and reduces model uncertainty through high-rate parameter estimation. CASC is demonstrated in simulation and hardware to significantly improve the performance of a quadrotor navigating through unknown environments with external disturbances and unknown model parameters. Keywords: Trajectory; Electron tubes; Uncertainty; Robustness; Optimization; Adaptation models
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8460817
