A Symbolic-AI Approach for UAV Exploration Tasks
Yixin Zhang, Joe McCalmon, Ashley Peake, Sarra Alqahtani, Paúl Pauca
Abstract
Performing autonomous exploration and exploitation is essential for un- manned aerial vehicles (UAVs) operating in unknown environments. Often, such missions involve first building a map of the environment via pure exploration and subsequently exploiting it for specific downstream tasks. But, conducting separate exploration and exploitation steps is not always feasible in practice. In this paper, we develop a novel exploration approach enabling exploration and exploitation in a single step for an area-of-interest (AoI) search task. The basic idea is to employ a probabilistic information gain map, called a belief map, as a prior to guide the exploration trajectory, while efficiently reducing false positive information in the process. The approach is composed of three layers. The first is an information potential layer to decide the exploration direction for the UAV. Next, the proximity layer exploits detected AoI by exploring their proximal areas. The last layer, a forced movement layer, is responsible for enabling the UAV to escape local maxima caused by the previous layers. We tested the performance of our approach in two different tasks relative to two exploration methods published in the literature. The results demonstrate that our proposed approach is capable of navigating through randomly generated environments and covering more AoI in fewer time steps compared to the baselines.