Carnegie Mellon University
The
Space-Time Functional Gradient Optimization for Motion Planning

Arunkumar Byravan, Byron Boots, Siddhartha Srinivasa, and Dieter Fox
IEEE International Conference on Robotics and Automation (ICRA), May, 2014.


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Abstract
Functional gradient algorithms (e.g. CHOMP) have recently shown great promise for producing locally optimal motion for complex many degree-of-freedom robots. A key limitation of such algorithms is the difficulty in incorporating constraints and cost functions that explicitly depend on time. We present T-CHOMP, a functional gradient algorithm that overcomes this limitation by directly optimizing in space-time. We outline a framework for joint space-time optimization, derive an efficient trajectory-wide update for maintaining time monotonicity, and demonstrate the significance of T-CHOMP over CHOMP in several scenarios. By manipulating time, T- CHOMP produces lower-cost trajectories leading to behavior that is meaningfully different from CHOMP.

Notes

Text Reference
Arunkumar Byravan, Byron Boots, Siddhartha Srinivasa, and Dieter Fox, "Space-Time Functional Gradient Optimization for Motion Planning," IEEE International Conference on Robotics and Automation (ICRA), May, 2014.

BibTeX Reference
@inproceedings{Srinivasa_2014_7673,
   author = "Arunkumar Byravan and Byron Boots and Siddhartha Srinivasa and Dieter Fox",
   title = "Space-Time Functional Gradient Optimization for Motion Planning",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   month = "May",
   year = "2014",
}