Automated Trajectory Synthesis for UAV Swarms Based on Resilient Data Collection Objectives

TL;DR

This paper introduces Synth4UAV, a formal method for automatically generating resilient, efficient trajectories for UAV swarms to collect time-sensitive data, considering multiple constraints and failure contingencies.

Contribution

It presents a novel logical modeling and synthesis approach for resilient UAV trajectory planning that accounts for various operational constraints and failure scenarios.

Findings

The approach effectively generates trajectories satisfying data collection and resiliency constraints.

The method scales well with increasing problem size.

Synth4UAV ensures efficient coverage and robustness against UAV failures.

Abstract

The use of Unmanned Aerial Vehicles (UAVs) for collecting data from remotely located sensor systems is emerging. The data can be time-sensitive and require to be transmitted to a data processing center. However, planning the trajectory of a collaborative UAV swarm depends on multi-fold constraints, such as data collection requirements, UAV maneuvering capacity, and budget limitation. Since a UAV may fail or be compromised, it is important to provide necessary resilience to such contingencies, thus ensuring data security. It is important to provide the UAVs with efficient spatio-temporal trajectories so that they can efficiently cover necessary data sources. In this work, we present Synth4UAV, a formal approach for automated synthesis of efficient trajectories for a UAV swarm by logically modeling the aerial space and data point topology, UAV moves, and associated constraints in terms of the turning and climbing angle, fuel usage, data collection point coverage, data freshness, and resiliency properties. We use efficient, logical formulas to encode and solve the complex model. The solution to the model provides the routing and maneuvering plan for each UAV, including the time to visit the points on the paths and corresponding fuel usage such that the necessary data points are visited while satisfying the resiliency requirements. We evaluate the proposed trajectory synthesizer, and the results show that the relationship among different parameters follow the requirements while the tool scales well with the problem size.