This work presents a fully integrated tree-based combined exploration-planning algorithm: exploration-rapidly-exploring random trees (RRT) (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating “where to go next” by considering a tradeoff between maximizing information gain and minimizing the distances traveled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, and real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully robot operating system (ROS) integrated and straightforward to use.

中文翻译：

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用于 3D 无人机探索的基于树的次佳轨迹方法


这项工作提出了一种完全集成的基于树的组合探索规划算法：探索快速探索随机树（RRT）（ERRT）。该算法专注于为完全未知和非结构化环境中的局部探索提供实时解决方案，同时直接将探索行为、机器人安全路径规划和机器人驱动纳入中心问题。 ERRT 提供了一个完整的采样和基于树的解决方案，通过考虑最大化信息增益和最小化行进距离以及沿路径的机器人驱动之间的权衡来评估“下一步去哪里”。完整的方案通过在受限、狭窄的地下和无法使用 GPS 的环境中进行的广泛模拟、比较和实际现场实验进行评估。该框架完全集成了机器人操作系统 (ROS)，并且易于使用。