用四维时空感知提升机器人动态环境导航能力
4D-based Robot Navigation Using Relativistic Image Processing
- 构建4D张量模型处理时序传感器数据
- 实现基于视觉与感官信息的实时位置预测
- 适合智能机器人自主避障与动态路径规划
机器感知是实现动态环境中安全交互与移动的重要前提。这不仅需要及时感知周围几何结构与距离，还需通过预设、学习并可复用的机器人技能来应对变化情境，以避免物理损伤或人身伤害。在此背景下，4D感知能够预测自身位置及环境随时间的变化。本文提出一种基于4D的机器人导航方法，采用相对论图像处理技术，在构造的4D空间中以张量模型处理时序相关的传感器信息。4D导航通过视觉与感官的4D信息，扩展了机器人的因果理解能力和交互范围。
原文摘要 · Abstract (English)
Machine perception is an important prerequisite for safe interaction and locomotion in dynamic environments. This requires not only the timely perception of surrounding geometries and distances but also the ability to react to changing situations through predefined, learned but also reusable skill endings of a robot so that physical damage or bodily harm can be avoided. In this context, 4D perception offers the possibility of predicting one's own position and changes in the environment over time. In this paper, we present a 4D-based approach to robot navigation using relativistic image processing. Relativistic image processing handles the temporal-related sensor information in a tensor model within a constructive 4D space. 4D-based navigation expands the causal understanding and the resulting interaction radius of a robot through the use of visual and sensory 4D information.
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