Estimated path information gain-based robot exploration under perceptual uncertainty

Abstract

At present, the frontier-based exploration has been one of the mainstream methods in autonomous robot exploration. Among the frontier-based algorithms, the method of searching frontiers based on rapidly exploring random trees consumes less computing resources with higher efficiency and performs well in full-perceptual scenarios. However, in the partially perceptual cases, namely when the environmental structure is beyond the perception range of robot sensors, the robot often lingers in a restricted area, and the exploration efficiency is reduced. In this article, we propose a decision-making method for robot exploration by integrating the estimated path information gain and the frontier information. The proposed method includes the topological structure information of the environment on the path to the candidate frontier in the frontier selection process, guiding the robot to select a frontier with rich environmental information to reduce perceptual uncertainty. Experiments are carried out in different environments with the state-of-the-art RRT-exploration method as a reference. Experimental results show that with the proposed strategy, the efficiency of robot exploration has been improved obviously.

Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System

“…As Equation ( 21) describes, we simply subtract the entropy of Y given X from the entropy of just Y to calculate the reduction of uncertainty about Y given an additional information X about Y [61]. In our case, when we build a decision tree, we want to pick up the features that will give us the greatest reduction of uncertainty.…”