A Decentralized Multi-UAV Spatio-Temporal Multi-Task Allocation Approach for Perimeter Defense

TL;DR

This paper introduces a decentralized multi-UAV approach for perimeter defense, modeling intruders as spatio-temporal tasks and solving the allocation problem efficiently with a modified consensus algorithm.

Contribution

It presents a novel decentralized multi-task allocation method for perimeter defense using a modified consensus algorithm, enabling scalable and efficient multi-UAV coordination.

Findings

Achieves similar performance to centralized methods under partial observability.

Reduces computational time compared to state-of-the-art centralized approaches.

Demonstrates effectiveness through Monte-Carlo simulations across various scenarios.

Abstract

This paper provides a new solution approach to a multi-player perimeter defense game, in which the intruders' team tries to enter the territory, and a team of defenders protects the territory by capturing intruders on the perimeter of the territory. The objective of the defenders is to detect and capture the intruders before the intruders enter the territory. Each defender independently senses the intruder and computes his trajectory to capture the assigned intruders in a cooperative fashion. The intruder is estimated to reach a specific location on the perimeter at a specific time. Each intruder is viewed as a spatio-temporal task, and the defenders are assigned to execute these spatio-temporal tasks. At any given time, the perimeter defense problem is converted into a Decentralized Multi-UAV Spatio-Temporal Multi-Task Allocation (DMUST-MTA) problem. The cost of executing a task for a trajectory is defined by a composite cost function of both the spatial and temporal components. In this paper, a decentralized consensus-based bundle algorithm has been modified to solve the spatio-temporal multi-task allocation problem, and the performance evaluation of the proposed approach is carried out based on Monte-Carlo simulations. The simulation results show the effectiveness of the proposed approach to solve the perimeter defense game under different scenarios. Performance comparison with a state-of-the-art centralized approach with full observability, clearly indicates that DMUST-MTA achieves similar performance in a decentralized way with partial observability conditions with a lesser computational time and easy scaling up.