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Tightly Coupled Sensor-Behavior Approaches for Real World Recognition

用于现实世界识别的紧耦合传感器行为方法

基本信息

批准号：
07245105
负责人：
SHIRAI Yoshiaki
金额：
$ 67.01万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research on Priority Areas
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1997
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-07245105/
关键词：
Environment Recognition Mobile Robot Work Planning Optimal Planning Realtime Vision Sensor Fusion Learning センサーフュージョン

项目摘要

Tightly coupled sensor-behavior is important for intelligent systems to behave in the real world. Such systems should deal with uncertainty of sensor information, lack of information of the environment, uncertainty of motion, and uncertainty of a priori knowledge. Tightly coupled sensor-behavior is a promising approach to this problem. The result of this research is the followings :・ Interpretation of Sensor Information for Real World Understanding : We studied fusion of various visual information to recognize environments, and implemented the methods in J.Rob.Vision (developed in this research) to realize realtime processing. Considering the reliability of recognition results, we made the optimal plan generation method which minimize the cost of sensing and that of motions. Finally, we made experiments of understanding real environments by sensing and actions.・ Sensing mechanism for Behaviors Adaptive to the Real World : In order to move in a changing environment such as roads, we int … More egrated sonar-based obstacle detection, sign pattern-based recognition of roads and cars, and sign pattern learning during motion of a robot, and then we developed a guide robot for a blind people.・ Behavior Control Based on Environment Model and Sensory Information : By developing a three-finger manipulation system, we realized realtime detection of object posture by stereo vision and detection of grasping force on fingers. We proposed error avoidance by monitoring task states and verified by assembly experiments. Moreover, we realized a system which selects a suitable cloth in a pile of clothes by a recursive region split method, and picks it up by a hand with a special rotor.・ Adaptive Functions in Perception-Action Systems : We proposes "behavior network" which represents and controls concurrent process of selecting sensor-behavior and of predicting the environment based on the memory, and studied how to represent adaptive behaviors of a robot. We made a four legged robot with vision on J.Rob.Leg(developed in this research). By experiments of the adaptive behaviors in complex environments, we verified the performance of the behavior network. Less

紧密耦合的传感器行为对于智能系统在真实的世界中的行为是重要的。这样的系统应该处理传感器信息的不确定性、环境信息的缺乏、运动的不确定性和先验知识的不确定性。紧耦合的传感器行为是一个很有前途的方法来解决这个问题。本研究的主要成果如下：·用于真实的世界理解的传感器信息的解释：我们研究了各种视觉信息的融合以识别环境，并在J.Rob.Vision（本研究开发的）中实现了该方法以实现实时处理。考虑到识别结果的可靠性，我们提出了最优的方案生成方法，使传感器的成本和运动的成本最小。最后，我们进行了通过感知和行动理解真实的环境的实验。·适应真实的世界的行为感知机制：为了在道路等变化的环境中移动，我们 ...更多信息综合了基于声纳的障碍物检测、基于路标模式的道路和汽车识别以及机器人运动过程中的路标模式学习等技术，研制了一种盲人导盲机器人。基于环境模型和感知信息的行为控制：通过开发三指操作系统，实现了立体视觉实时检测物体姿态和手指抓取力的检测。我们提出了通过监控任务状态来避免错误，并通过装配实验进行了验证。此外，我们还实现了一个系统，该系统通过递归区域分割方法从一堆衣服中选择合适的布料，并通过带有特殊转子的手将其拾取。感知-动作系统中的自适应功能：提出了基于记忆的感知-动作系统中的“行为网络”来表示和控制感知-动作系统中传感器行为选择和环境预测的并发过程，并研究了如何表示机器人的自适应行为。我们在J.Rob.Leg（本研究中开发的）上制作了一个具有视觉的四足机器人。通过复杂环境下的自适应行为实验，验证了行为网络的性能。少