A Multi-Objective Exploration Strategy for Mobile Robots
Francesco Amigoni, Α. Gallo
- 发表年份
- 2006
- 引用次数
- 55
摘要
Exploration strategies are used to guide mobile robots in building maps of environments. Usually, exploration strategies work greedily by evaluating a number of candidate observation positions on the basis of a utility function and selecting the best one. The utility functions are defined in an ad hoc manner as the compositionof values measuring different features of a candidate observation position, such as the travelling cost and the estimated information gain. In this paper, we propose a more general way to define an exploration strategy through multi-objective optimization. In our approach, the values of the features are kept separated without combining them in a particular utility function. Experimental results demonstrate the effectiveness of our method.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991