Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning
Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning
复制标题
在线运动规划的图联合采样和轨迹优化
DOI：
10.1109/iros51168.2021.9636064
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Mustafa Mukadam
中科院分区：
文献类型：
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作者：
Kalyan Vasudev Alwala;Mustafa Mukadam
在最流行的运动规划技术中，采样和轨迹优化由于其分别处理紧约束和高维系统的能力而获得成功。然而，在更高维度的采样和局部最小值问题的优化限制，阻碍了他们超越静态场景在离线设置的能力。在这里，我们考虑高度动态的环境，需要一个快速的在线解决方案。我们提出了一个统一的方法，利用采样和优化的互补优势，并交错它们都在一种方式，非常适合这个具有挑战性的问题。在多个合成和现实的模拟环境中的基准测试，我们表明，我们的方法在各种指标上的表现显着更好，对基线，采用无论是采样或优化。项目页面：https://sites.google.com/view/jistplanner
Among the most prevalent motion planning techniques, sampling and trajectory optimization have emerged successful due to their ability to handle tight constraints and high-dimensional systems, respectively. However, limitations in sampling in higher dimensions and local minima issues in optimization have hindered their ability to excel beyond static scenes in offline settings. Here we consider highly dynamic environments with long horizons that necessitate a fast on-line solution. We present a unified approach that leverages the complementary strengths of sampling and optimization, and interleaves them both in a manner that is well suited to this challenging problem. With benchmarks in multiple synthetic and realistic simulated environments, we show that our approach performs significantly better on various metrics against baselines that employ either only sampling or only optimization. Project page: https://sites.google.com/view/jistplanner
通过概率推理实现近似最佳的连续时间运动规划和控制
DOI：
10.1109/icra.2017.7989082
发表时间：
2017
期刊：
International Conference on Robotics and Automation
影响因子：
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作者：
Mukadam, Mustafa;Cheng, Ching-An;Yan, Xinyan;Boots, Byron
通讯作者：
Boots, Byron