SKYSURF: A Self-learning Framework for Persistent Surveillance using Cooperative Aerial Gliders

TL;DR

This paper introduces a self-learning, cooperative UAV framework that enhances persistent surveillance by optimizing energy use and collision avoidance, significantly extending flight duration and target detection capabilities.

Contribution

It presents a novel behavioral management and decision-making system for cooperative soaring UAVs, integrating mission planning, dynamic navigation, and learning strategies for improved persistence.

Findings

Extended UAV flight time by 6 hours with minimal power use.

Achieved twice the target detection rate compared to non-cooperative methods.

Demonstrated robustness through comparative analysis with multiple algorithms.

Abstract

The success of surveillance applications involving small unmanned aerial vehicles (UAVs) depends on how long the limited on-board power would persist. To cope with this challenge, alternative renewable sources of lift are sought. One promising solution is to extract energy from rising masses of buoyant air. This paper proposes a local-global behavioral management and decision-making approach for the autonomous deployment of soaring-capable UAVs. The cooperative UAVs are modeled as non-deterministic finite state-based rational agents. In addition to a mission planning module for assigning tasks and issuing dynamic navigation waypoints for a new path planning scheme, in which the concepts of visibility and prediction are applied to avoid the collisions. Moreover, a delayed learning and tuning strategy is employed optimize the gains of the path tracking controller. Rigorous comparative analyses carried out with three benchmarking baselines and 15 evolutionary algorithms highlight the adequacy of the proposed approach for maintaining the surveillance persistency (staying aloft for longer periods without landing) and maximizing the detection of targets (two times better than non-cooperative and semi-cooperative approaches) with less power consumption (almost 6% of battery consumed in six hours).