Leveraging Precomputation with Problem Encoding for Warm-Starting Trajectory Optimization in Complex Environments
Leveraging Precomputation with Problem Encoding for Warm-Starting Trajectory Optimization in Complex Environments
复制标题
利用预计算和问题编码实现复杂环境中的热启动轨迹优化
DOI：
10.1109/iros.2018.8593977
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Vijayakumar
中科院分区：
文献类型：
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作者：
W. Merkt;V. Ivan;S. Vijayakumar
通过优化进行运动规划在很大程度上是基于局部提高轨迹的代价，直到找到最优解。因此，初始轨迹的选择对运动规划器的性能有很大的影响，特别是当成本场景包含局部极小时。虽然可以使用多个启发式和近似来有效地在线计算初始化，但它们基于的一般假设并不总是与手头的任务匹配。在本文中，我们利用了重复任务按某种度量相似这一事实。我们离线将问题的解决方案存储为初始种子轨迹库，并使用问题编码来动态检索接近最优的热启动初始化。我们比较了不同的初始化策略如何影响拟牛顿和概率推理求解器的全局收敛和运行时间。我们对38自由度NASA Valkyrie机器人的分析表明，尽管存在全局非光滑和不连续的约束，例如碰撞造成的约束，在高维状态空间中进行高效和最优的规划是可能的。
Motion planning through optimization is largely based on locally improving the cost of a trajectory until an optimal solution is found. Choosing the initial trajectory has therefore a significant effect on the performance of the motion planner, especially when the cost landscape contains local minima. While multiple heuristics and approximations may be used to efficiently compute an initialization online, they are based on generic assumptions that do not always match the task at hand. In this paper, we exploit the fact that repeated tasks are similar according to some metric. We store solutions of the problem as a library of initial seed trajectories offline and employ a problem encoding to retrieve near-optimal warm-start initializations on-the-fly. We compare how different initialization strategies affect the global convergence and runtime of quasi-Newton and probabilistic inference solvers. Our analysis on the 38- DoF NASA Valkyrie robot shows that efficient and optimal planning in high-dimensional state spaces is possible despite the presence of globally non-smooth and discontinuous constraints, such as the ones imposed by collisions.