Uncertainty measurement for environmental recognition ability of intelligent robot
Yan Yan, Tang Zhenmin, Yong kang Liu · 2012
Ground intelligent robot may fall across some unpredictable tasks and environmental conditions. Traditional methods on ability evaluation barely concern fuzziness and randomness of system. This paper presents a method with uncertainty measurement theory to evaluate the environmental recognition ability of intelligent robot. Firstly, a cloud model with parameters including expectation, entropy and hyper-entropy is proposed. Then, a Two-Dimensional Multi-rules Cloud Reasoning algorithm (2DMCR) is addressed basing the cloud model. With a set of X-condition reasoning rules on Environmental Recognition ability, the algorithm constructs a multi-rules generator and calculates the expectation of cloud model. Finally, a practical experiment based on ground intelligent robots in our laboratory is given. The results show that this method is feasible in determining appropriate level of ER ability and can also be used in evaluation of other intelligent system as well.