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A study of interaction scheme and external computation resource for heterogenous autonomous robots

异构自主机器人交互方案与外部计算资源研究

基本信息

批准号：
17500117
负责人：
OSAWA Eiichi
金额：
$ 2.24万
依托单位：
Future University-Hakodate
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

项目摘要

We have developed an integrated architecture for multiple small mowing robots. The architecture includes (1) a mixture scheme to improve reliability of robots, (2) agent framework to enable cooperation among robots, (3) a scheduling methods for robots, and (4) learning scheme for robot action policy. The developed mowing robots consists of a basic mowing unit, CPU, GPS, and communication module as well.The previous work shows that small mowing robots suffers from several problems. They include (1) robots easily stack on uneven surface, (2) low path trace capability, and (3) communication is unstable in the outside environment Furthermore, robots need to be recharged when they gets lower electricity energy. Tb handle these problems, we need to device robust and efficient working strategy for robots, which includes robots and charging stations. For that purpose, we have developed mixture scheme to improve the reliability of robots.Also, we have developed agent framework based on Web service technology, which enables cooperation among robots. The framework includes Java mobile agent Robots are able to cooperate based on the framework We also show efficiency of the scheme.As far as the learning of action policy of robots is concerned, we have developed two learning features of robots. One is for collision avoidance, and the other is for on demand learning of escape from stuck situation. The simulation results shows efficiency of these features. We also implemented these feature on the real robots. The experimental results of the real robots are consistent with the simulation results.

我们已经为多个小型割草机器人开发了一个集成架构。该体系结构包括：(1)提高机器人可靠性的混合方案；(2)实现机器人间协作的智能体框架；(3)机器人调度方法；(4)机器人动作策略的学习方案。所开发的割草机器人由基本割草单元、CPU、GPS和通信模块组成。先前的工作表明，小型割草机器人存在几个问题。它们包括：(1)机器人容易堆积在不平坦的表面上；(2)路径跟踪能力低；(3)机器人在外界环境中通信不稳定。此外，机器人在电量较低时需要充电。为了解决这些问题，我们需要为机器人设计强大而高效的工作策略，其中包括机器人和充电站。为此，我们开发了混合方案，以提高机器人的可靠性。开发了基于Web服务技术的代理框架，实现了机器人间的协作。该框架包含Java移动代理，机器人能够基于该框架进行协作，并证明了该方案的有效性。就机器人动作策略的学习而言，我们开发了机器人的两个学习特征。一个是为了避免碰撞，另一个是为了从卡住的情况下逃生的随需应变学习。仿真结果表明了这些特征的有效性。我们也在真实的机器人上实现了这些功能。真实机器人的实验结果与仿真结果吻合较好。