Map-Predictive Motion Planning in Unknown Environments
Map-Predictive Motion Planning in Unknown Environments
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DOI：
10.1109/icra40945.2020.9197522
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Amine Elhafsi;B. Ivanovic;Lucas Janson;M. Pavone
中科院分区：
文献类型：
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作者：
Amine Elhafsi;B. Ivanovic;Lucas Janson;M. Pavone
用于未知环境中的运动规划的算法通常受限于它们对未观察到的环境的结构进行推理的能力。因此，当前的方法通常依靠启发式方法沿边界选择中间目标来导航未知环境。提出了一种将地图预测和运动规划相结合的统一方法，用于动态约束机器人安全、高效地进行未知环境的自动导航。我们提出了一种数据驱动的方法，以机器人对周围环境的观察为背景，预测未观察环境的地图。然后，这些地图预测被用来规划从机器人位置到目标的轨迹，而不需要选择边界。我们将这种地图预测运动规划策略应用到随机生成的蜿蜒走廊环境中，与天真的前沿追踪方法相比，在轨迹持续时间方面取得了显著改善。我们还在实验中展示了与使用更复杂的层上选择启发式方法的方法类似的性能，同时显著减少了计算时间。
Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient au-tonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot’s observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot’s position to the goal without requiring frontier selection. We applied this map-predictive motion planning strategy to randomly generated winding hallway environments, yielding substantial improvement in trajectory duration over a naïve frontier pursuit method. We also experimentally demonstrate similar performance to methods using more sophisticated fron-tier selection heuristics while significantly reducing computation time.