Evaluation of Similarity Metrics Under the Context of an Autonomous Reactive System

Currently, in the field of robotics, institutions and researchers are working on the design and development of autonomous navigation systems on robots for dynamic environments. The most advanced implementations of autonomous behaviors are found on vehicles or wheeled devices, allowing them to move on controlled environments and even on rough terrain. In this paper, it is presented the design of an autonomous reactive system for humanoid robots. This system requires to know the current state of the robot, during a specific activity, to make the right reactive action for a specific situation. In the context of inquiring the current state of the robot, we consider the implementation of a knowledge base populated with diverse states of the joints and their possible reactive actions. To recover the possible reactive actions from the knowledge base, it is required to search for the current state of the robot in the knowledge base. However, this process may incur in high computational cost depending on the size of the knowledge base. Therefore, in this work, we carried out a comparative study of six similarity metrics, with the objective of identifying the metric that offers the best computational time. In these studies, it is identified that the metrics with lower mathematical complexity showed the best results. Additionally, we used Wilcoxon and Friedman statistics tests to assess the performance of the similarity metrics. Finally, we included an analysis of the characteristics and functionality of several similarity metrics, which showed that some of them are not suitable in the context of our proposal. On the other hand, other metrics were identified as viable and with potential for future works