A Decentralized Multirobot Spatiotemporal Multitask Assignment Approach for Perimeter Defense

Abstract

This article provides a new decentralized approach to solve a perimeter defense problem (PDP). In a typical PDP, many intruders try to enter a territory, and a group of defenders operating both inside and on the perimeter protects the territory by capturing the intruders on the perimeter. The objective of the defenders is to detect and capture the intruders before they enter the territory. Defenders sense the intruders independently and compute their trajectories to capture all the intruders in a cooperative way. Each intruder is estimated to reach a specific location on the perimeter at a specific time, and this is considered as a spatiotemporal task to be handled by a defender. At any given time, the PDP is converted to a decentralized multirobot spatiotemporal multitask assignment (DMRST-MTA) problem. The cost of executing a task for a defender is defined by a composite cost function that includes both the spatial and temporal cost components. In this article, a modified decentralized consensus-based bundle algorithm is presented to solve the above spatiotemporal multitask assignment problem. The performance evaluation of the proposed approach is presented based on Monte Carlo studies, and the results show the effectiveness of the proposed approach under different scenarios. The robustness studies also show that the proposed approach is robust against uncertainties in the heading angles of the intruders. The performance comparison with the decentralized adaptive partitioning approach for the various arrival distributions of intruders clearly shows that DMRST-MTA is efficient.