论文标题
在高度动态的环境中使用定时-ESDF的在线状态轨迹计划
Online State-Time Trajectory Planning Using Timed-ESDF in Highly Dynamic Environments
论文作者
论文摘要
在高度动态的环境中，在线状态时期轨迹计划仍然是一个未解决的问题，这是由于移动障碍的不可预测动议和从国家时间空间中的维度诅咒。现有的国家时间计划者通常是根据随机采样方法或在离散状态图上搜索的路径搜索实现的。这些规划人员的平稳性，路径清除和计划效率通常不令人满意。在这项工作中，我们在高度动态的环境中提出了一个基于梯度的规划师，以实现在线轨迹生成的国家时间空间。为了启用基于梯度的优化，我们提出了一个定时-ESDT，该定时 - ESDT支持距离和使用州时间密钥的梯度查询。根据定时-ESDT，我们还定义了与国家时间空间兼容的平滑先验和障碍可能性函数。然后将轨迹计划提交到地图问题，并通过有效的数值优化器解决。此外，为了提高计划者的最佳性，我们还定义了一个状态时间图，然后进行搜索路径以找到更好的优化器初始化。通过集成图形搜索，计划质量得到显着提高。实验结果对模拟和基准数据集的结果表明，我们的计划者可以胜过最新方法，证明其比传统方法具有显着优势。
Online state-time trajectory planning in highly dynamic environments remains an unsolved problem due to the unpredictable motions of moving obstacles and the curse of dimensionality from the state-time space. Existing state-time planners are typically implemented based on randomized sampling approaches or path searching on discretized state graph. The smoothness, path clearance, and planning efficiency of these planners are usually not satisfying. In this work, we propose a gradient-based planner over the state-time space for online trajectory generation in highly dynamic environments. To enable the gradient-based optimization, we propose a Timed-ESDT that supports distance and gradient queries with state-time keys. Based on the Timed-ESDT, we also define a smooth prior and an obstacle likelihood function that is compatible with the state-time space. The trajectory planning is then formulated to a MAP problem and solved by an efficient numerical optimizer. Moreover, to improve the optimality of the planner, we also define a state-time graph and then conduct path searching on it to find a better initialization for the optimizer. By integrating the graph searching, the planning quality is significantly improved. Experiment results on simulated and benchmark datasets show that our planner can outperform the state-of-the-art methods, demonstrating its significant advantages over the traditional ones.
