Adaptable User Interface Based on the Ecological Interface Design Concept for Multiple Robots Operating Works with Uncertainty
Furukawa
- 发表年份
- 2010
- 引用次数
- 8
- 访问权限
- 开放获取
摘要
Problem statement: When supervising a team of semi-autonomous robots, the operator's task is not only the manipulation of each robot but also achievement of the top goal that is assigned to the entire team of humans and robots. The operators are demanded to comprehend highly complex states and make appropriate decisions in dynamic environment using the limited cognitive resources. The Ecological Interface Design (EID) is a design approach based on visualization of constraints in study environment onto the interface to reduce the cognitive workload. Our research question was "How should we design Human-Robot Interface (HRI) that allows operators to understand states of multiple robots systems working in environment with uncertainty?" Approach: Our proposed method was Adaptable User Interface (AUI) with EID. The interface is a framework that allowed the operators to take selectable use of displays indicating different types of functional information. Two types of expressions of functions were defined for indicating states of functions: indication of functional disposition and of effectiveness. Three cognitive experiments were conducted to reveal potential efficacy of the method using an experimental test-bed simulation. Results: The results of the first and second experiments showed that usefulness of functional indications is different from different works, tasks and operators. The third experiment demonstrated that operators could select and use appropriate information among a set of indicators with different types and levels of functional information, which are adaptive for tasks, their strategies, their skills and available information and knowledge about the tasks. Conclusion: From the remarks, it can be concluded that the proposed method (AUI+EID) is a useful and feasible HRI method for multiple robot operations, even knowledge about the work is not sufficient to build a set of dedicated indicators each has only necessary information for a situation.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002