论文标题
在线运动计划的图表上的联合采样和轨迹优化
Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning
论文作者
论文摘要
在最普遍的运动计划技术中，由于分别处理严格的约束和高维系统的能力，取样和轨迹优化已成功。但是，在更高维度和优化中的本地最小问题中采样的限制阻碍了它们超越离线设置中静态场景的能力。在这里，我们考虑了具有较长视野的高度动态环境，需要快速在线解决方案。我们提出了一种统一的方法，该方法利用了采样和优化的互补优势，并以非常适合这个具有挑战性的问题的方式交织在一起。在多个合成和现实的模拟环境中的基准测试中，我们表明我们的方法在针对仅采用采样或仅优化的基准的各种指标上的性能明显更好。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 online 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
