用生物启发的主动推断框架，实现无需预训练的实时机器人导航。
Navigation and Exploration with Active Inference: from Biology to Industry
- 基于主动推断框架构建拓扑地图并定位
- 通过最小化不确定性实现高效探索，性能媲美顶尖方法
- 适用于2D/3D仿真与真实环境，适合智能机器人研发
动物通过构建和更新内部认知地图，在复杂动态环境中展现出卓越的导航能力。受这些生物机制启发，我们提出一种基于主动推断框架（AIF）的实时机器人导航系统。该模型增量式构建拓扑地图，推断智能体位置，并通过最小化预期不确定性与实现感知目标来规划动作，无需任何预先训练。集成于ROS2生态，我们在2D与3D环境（包括仿真与真实世界）中验证了其适应性与效率，表现可与传统及前沿探索方法比肩，提供了一种生物启发的导航新范式。
原文摘要 · Abstract (English)
By building and updating internal cognitive maps, animals exhibit extraordinary navigation abilities in complex, dynamic environments. Inspired by these biological mechanisms, we present a real time robotic navigation system grounded in the Active Inference Framework (AIF). Our model incrementally constructs a topological map, infers the agent's location, and plans actions by minimising expected uncertainty and fulfilling perceptual goals without any prior training. Integrated into the ROS2 ecosystem, we validate its adaptability and efficiency across both 2D and 3D environments (simulated and real world), demonstrating competitive performance with traditional and state of the art exploration approaches while offering a biologically inspired navigation approach.
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