期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 32, 期 9-10, 页码 1164-1193出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364913488805
关键词
Motion planning; constrained optimization; distance fields
类别
资金
- DARPA
- Quality of Life Technologies NSF ERC
- Intel Pittsburgh
In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using seven-degree-of-freedom manipulators and a rough-terrain quadruped robot.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据