Structure-based color learning on a mobile robot under changing illumination
Structure-based color learning on a mobile robot under changing illumination
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DOI：
10.1007/s10514-007-9038-7
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发表时间：
2007-10
影响因子：
3.5
通讯作者：
M. Sridharan;P. Stone
中科院分区：
文献类型：
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作者：
M. Sridharan;P. Stone
机器人技术和人工智能的一个核心目标是能够部署一个代理在真实的世界中长时间自主行动。为了在真实的世界中运行，自主机器人依赖于感官信息。尽管来自机载摄像机的视觉信息潜在丰富，但许多移动的机器人继续依赖于非视觉传感器，如触觉传感器、声纳和激光。这种对相对低保真度传感器的偏好可以归因于在有限的计算资源下实时操作的特性要求。照明的变化带来了另一个巨大的挑战。对于真正的扩展自治，代理必须能够识别自己何时放弃当前模型以学习新模型;以及如何在当前情况下学习。我们描述了一个独立的视觉系统，工作在船上的视觉为基础的自主机器人在不同的照明条件下。首先，我们提出了一个基线系统，能够在机器人的计算和内存限制的颜色分割和对象识别。这依赖于手动标记的数据，并在恒定和合理均匀的照明条件下运行。然后，我们通过引入算法来放松这些限制：（i）自主规划的颜色学习，其中机器人使用其环境的知识（对象的位置、大小和形状）以自动生成合适的运动序列并学习期望的颜色，以及（ii）照明变化检测和适应，其中机器人自己识别何时照明条件已经充分改变以保证修正其颜色知识。我们的算法在索尼ERS-7 Aibo机器人上得到了充分的实施和测试。
A central goal of robotics and AI is to be able to deploy an agent to act autonomously in the real world over an extended period of time. To operate in the real world, autonomous robots rely on sensory information. Despite the potential richness of visual information from on-board cameras, many mobile robots continue to rely on non-visual sensors such as tactile sensors, sonar, and laser. This preference for relatively low-fidelity sensors can be attributed to, among other things, the characteristic requirement of real-time operation under limited computational resources. Illumination changes pose another big challenge. For true extended autonomy, an agent must be able to recognize for itselfwhento abandon its current model in favor of learning a new one; andhowto learn in its current situation. We describe a self-contained vision system that works on-board a vision-based autonomous robot under varying illumination conditions. First, we present a baseline system capable of color segmentation and object recognition within the computational and memory constraints of the robot. This relies onmanuallylabeled data and operates underconstantand reasonably uniform illumination conditions. We then relax these limitations by introducing algorithms for (i) Autonomous planned color learning, where the robot uses the knowledge of its environment (position, size and shape of objects) to automatically generate a suitable motion sequence and learn the desired colors, and (ii) Illumination change detection and adaptation, where the robot recognizes for itself when the illumination conditions have changed sufficiently to warrant revising its knowledge of colors. Our algorithms are fully implemented and tested on the Sony ERS-7 Aibo robots.