The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review

Abstract

Control of autonomous vehicles for applications such as surveillance, search, and exploration has been a topic of great interest over the past two decades. In particular, there has been a rising interest in control of multiple vehicles for reasons such as increase in system reliability, robustness, and efficiency, with a possible reduction in cost. The exploration problem is NP hard even for a single vehicle/agent, and the use of multiple vehicles brings forth a whole new suite of problems associated with communication and cooperation between vehicles. The persistent surveillance problem differs from exploration since it involves continuous/repeated coverage of the target space, minimizing time between re-visits. The use of aerial vehicles demands consideration of vehicle dynamic and endurance constraints as well. Another aspect of the problem that has been investigated to a lesser extent is the design of the vehicles for particular missions. The intent of this paper is to thoroughly review the persistent surveillance problem, with particular focus on multiple Unmanned Air Vehicles (UAVs), and present some of our own work in this area. We investigate the different aspects of the problem and slightly digress into techniques that have been applied to exploration and coverage, but a comprehensive survey of all the work in multiple vehicle control for search, exploration, and coverage is beyond the scope of this paper.

On Realistic Target Coverage by Autonomous Drones

“…Several challenging aspects of persistent coverage have also been studied: energy-awareness (Derenick et al 2011), connectivity (Orfanus et al 2016), adaptive streaming (Wang et al 2016) and dynamic priorities (da Silva et al 2017). For a more thorough survey, we refer the reader to (Khan et al 2018;Nigam 2014). e proposed system leverages established results in persistent coverage through a two tier architecture.…”

Cloud-Supported Coverage Control for Persistent Surveillance Missions

“…Typical strategies involve optimization [27], auctions [10], biological meta-heuristics [4], potential fields [30], or space decomposition [20]. Of particular relevance are multi-agent persistent surveillance (persistent monitoring) problems, in which a mobile sensor team is tasked with continual surveillance of a region of interest, requiring each subregion to be visited multiple (or infinitely many) times with the goal of minimizing a cost, e.g., the time between visits or the likelihood of detecting stochastic events [19]. Persistent surveillance is a generalization of patrolling, where closed tours are sought for the purpose of protecting or supervising an environment.…”

“…In the bottom path, red is updated first, leaving a block that belongs to both regions simultaneously. alleviated through distributed control; however, such setups are application specific and may require extensive efforts to pose a mathematical problem that is suitable for use with formal techniques [19]. In contrast, decomposition-based approaches, which decouple the assignment and routing problem by first dividing the workspace among agents, offer a straightforward, modular framework to reasonably accomplish the desired goals, despite sacrificing optimality in general.…”

A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment

“…Search pattern, random walk, search map, digital pheromone [4], Glasius bio-inspired neural network (GBNN) [5,6], and optimization algorithms are the typical algorithms. The search pattern, such as zigzag and spiral [7], can effectively cover a given domain with fewer time t. r i d (t) is the decision radius of glowworm i at time t. The update equation of position is shown in (7). Finally, the decision radius is updated by (8).…”

“…x j (t) − x i (t) ) (7) r i d (t + 1) = min r s , max 0, r i d (t) + β(n t − N i (t) (8) FA: Firefly algorithms [36]. It is similar to GSO.…”