This paper introduces a real-time algorithm for navigating complex unknown environments cluttered with movable obstacles. Our algorithm achieves fast, adaptable routing by actively attempting to manipulate obstacles during path planning and adjusting the global plan from sensor feedback. The main contributions include an improved dynamic Directed Visibility Graph (DV-graph) for rapid global path searching, a real-time interaction planning method that adapts online from new sensory perceptions, and a comprehensive framework designed for interactive navigation in complex unknown or partially known environments. Our algorithm is capable of replanning the global path in several milliseconds. It can also attempt to move obstacles, update their affordances, and adapt strategies accordingly. Extensive experiments validate that our algorithm reduces the travel time by 33%, achieves up to 49% higher path efficiency, and runs faster than traditional methods by orders of magnitude in complex environments. It has been demonstrated to be the most efficient solution in terms of speed and efficiency for interactive navigation in environments of such complexity. We also open-source our code in the docker demo to facilitate future research.

翻译：本文提出了一种用于在堆满可移动障碍物的复杂未知环境中导航的实时算法。该算法通过在路径规划过程中主动尝试操控障碍物，并根据传感器反馈调整全局规划，实现了快速且自适应的路由。主要贡献包括：用于快速全局路径搜索的改进型动态定向可见性图（DV-graph）、可根据新感知信息在线自适应调整的实时交互规划方法，以及专为复杂未知或部分已知环境中交互式导航设计的综合框架。该算法能在数毫秒内完成全局路径重新规划，同时能尝试移动障碍物、更新其可供性并相应调整策略。大量实验证实：该算法在复杂环境中可将行进时间缩短33%，路径效率提升高达49%，且运行速度比传统方法快数个数量级。实验证明，在同等复杂程度的交互式导航环境中，该算法在速度与效率方面均为最优解决方案。我们同时开源了Docker演示中的代码，以促进未来研究。