Robustness-Driven Exploration with Probabilistic Metric Temporal Logic
This addresses inefficient exploration for UAVs in unstructured environments like rainforests, though it appears incremental as it builds on existing P-MTL and MCMC methods.
The paper tackles the problem of autonomous UAV exploration in unknown environments by proposing a method that uses Probabilistic Metric Temporal Logic to guide exploration toward areas with more interesting information, resulting in 38% better coverage of illegal mining activities compared to a greedy baseline.
The ability to perform autonomous exploration is essential for unmanned aerial vehicles (UAV) operating in unstructured or unknown environments where it is hard or even impossible to describe the environment beforehand. However, algorithms for autonomous exploration often focus on optimizing time and coverage in a greedy fashion. That type of exploration can collect irrelevant data and wastes time navigating areas with no important information. In this paper, we propose a method for exploiting the discovered knowledge about the environment while exploring it by relying on a theory of robustness based on Probabilistic Metric Temporal Logic (P-MTL) as applied to offline verification and online control of hybrid systems. By maximizing the satisfaction of the predefined P-MTL specifications of the exploration problem, the robustness values guide the UAV towards areas with more interesting information to gain. We use Markov Chain Monte Carlo to solve the P-MTL constraints. We demonstrate the effectiveness of the proposed approach by simulating autonomous exploration over Amazonian rainforest where our approach is used to detect areas occupied by illegal Artisanal Small-scale Gold Mining (ASGM) activities. The results show that our approach outperform a greedy exploration approach (Autonomous Exploration Planner) by 38% in terms of ASGM coverage.