通过多帧点约束实现机器人在动态环境中的主动导航
MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints
- 利用多帧观测与未来帧预测构建运动约束
- 可在未知动态环境中提前避障，提升导航鲁棒性
- 适合需要实时避障的移动机器人场景
复杂动态环境中的障碍物规避是实时机器人导航的关键挑战。基于模型和基于学习的方法在高度动态场景中常失效：传统方法假设环境静态，无法适应实时变化；学习方法依赖单帧观测估计运动约束，适应性受限。为此，本文提出一种新框架，通过引入多帧点约束（包括由专用模块预测的当前与未来帧），实现主动端到端导航。该方法结合预测模块，基于多帧观测预测移动障碍物的未来路径，使机器人能够主动预判并规避潜在危险。这种主动规划能力显著提升了在未知动态环境中的导航鲁棒性与效率。仿真与真实世界实验验证了方法的有效性。
原文摘要 · Abstract (English)
Obstacle avoidance in complex and dynamic environments is a critical challenge for real-time robot navigation. Model-based and learning-based methods often fail in highly dynamic scenarios because traditional methods assume a static environment and cannot adapt to real-time changes, while learning-based methods rely on single-frame observations for motion constraint estimation, limiting their adaptability. To overcome these limitations, this paper proposes a novel framework that leverages multi-frame point constraints, including current and future frames predicted by a dedicated module, to enable proactive end-to-end navigation. By incorporating a prediction module that forecasts the future path of moving obstacles based on multi-frame observations, our method allows the robot to proactively anticipate and avoid potential dangers. This proactive planning capability significantly enhances navigation robustness and efficiency in unknown dynamic environments. Simulations and real-world experiments validate the effectiveness of our approach.
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