用事件相机让机器人实时自主导航，节能又高效。
Real-Time Neuromorphic Navigation: Guiding Physical Robots with Event-Based Sensing and Task-Specific Reconfigurable Autonomy Stack
- 基于事件的神经形态视觉，动态响应更灵敏。
- 在Turtlebot和Bebop2上实现实时路径重规划与移动门穿越。
- 可重构架构适配不同任务，适合做低功耗智能机器人。
神经形态视觉受生物神经系统启发，近期在提升机器人自主性方面备受关注。本文系统提出一种神经形态导航框架，利用事件驱动的神经形态视觉实现机器人系统的高效、实时导航。该可重构框架针对地面机器人（Turtlebot）和空中机器人（Bebop2 quadrotor）的不同需求，优化了感知、规划与控制全栈的算法设计。通过两个案例验证：Turtlebot实现局部路径重规划以支持实时导航；Bebop2 quadrotor成功穿越移动门。本工作为任务特定、实时的机器人自主提供了可扩展方案，推动了低功耗自主导航的发展。
原文摘要 · Abstract (English)
Neuromorphic vision, inspired by biological neural systems, has recently gained significant attention for its potential in enhancing robotic autonomy. This paper presents a systematic exploration of a proposed Neuromorphic Navigation framework that uses event-based neuromorphic vision to enable efficient, real-time navigation in robotic systems. We discuss the core concepts of neuromorphic vision and navigation, highlighting their impact on improving robotic perception and decision-making. The proposed reconfigurable Neuromorphic Navigation framework adapts to the specific needs of both ground robots (Turtlebot) and aerial robots (Bebop2 quadrotor), addressing the task-specific design requirements (algorithms) for optimal performance across the autonomous navigation stack -- Perception, Planning, and Control. We demonstrate the versatility and the effectiveness of the framework through two case studies: a Turtlebot performing local replanning for real-time navigation and a Bebop2 quadrotor navigating through moving gates. Our work provides a scalable approach to task-specific, real-time robot autonomy leveraging neuromorphic systems, paving the way for energy-efficient autonomous navigation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。