论文标题
直接基于Bézier的轨迹计划者，用于改善对未知环境的本地探索
Direct Bézier-Based Trajectory Planner for Improved Local Exploration of Unknown Environments
论文作者
论文摘要
自主探索是移动机器人的重要功能，因为他们的大多数应用都需要有效收集有关其周围环境的信息。在文献中，有几种方法，从基于边界的方法到涉及计划本地和全球探索道路的能力的混合解决方案，但只有很少的方法专注于通过正确调整计划的轨迹来改善本地探索，通常会导致“停留和行为”。在这项工作中，我们提出了一种基于RRT启发的新型Bézier的次要轨迹轨迹计划者，能够处理快速的本地探索问题。高斯工艺推断用于保证快速探索获得的检索，同时仍与勘探任务保持一致。将提出的方法与其他可用的最先进算法进行了比较，并在现实情况下进行了测试。实施的代码将作为开源代码公开发布，以鼓励进一步的开发和基准测试。
Autonomous exploration is an essential capability for mobile robots, as the majority of their applications require the ability to efficiently collect information about their surroundings. In the literature, there are several approaches, ranging from frontier-based methods to hybrid solutions involving the ability to plan both local and global exploring paths, but only few of them focus on improving local exploration by properly tuning the planned trajectory, often leading to "stop-and-go" like behaviors. In this work we propose a novel RRT-inspired Bézier-based next-best-view trajectory planner able to deal with the problem of fast local exploration. Gaussian process inference is used to guarantee fast exploration gain retrieval while still being consistent with the exploration task. The proposed approach is compared with other available state-of-the-art algorithms and tested in a real-world scenario. The implemented code is publicly released as open-source code to encourage further developments and benchmarking.
