Stable Cooperative Surveillance With Information Flow Constraints

Abstract

Abstract-We consider a cooperative surveillance problem for a group of autonomous air vehicles (AAVs) that periodically receives information on suspected locations of targets from a satellite and then must cooperate to decide which AAV should search for each target. This cooperation must be performed in spite of imperfect intervehicle communications (e.g., messages with random but bounded delays), less than full communication connectivity between vehicles, uncertainty in target locations, and imperfect vehicle search sensors. We represent the state of the search progress with a "search map," and use an invariant set to model the set of states where there is no useful information on target locations. Arrivals of new suspected target location information from the satellite corresponds to perturbations of the search map from this invariant set. A cooperation strategy that pursues a type of "persistent area denial" will try to force trajectories of the system into the invariant set by exploiting initial target information and search progress by the AAVs. We show that the invariant set is exponentially stable for a class of cooperative surveillance strategies. We provide a comparative analysis of cooperative and noncooperative strategies. Next, we show via simulations the impact of imperfect communications, imperfect vehicle search sensors, uncertainty in search locations, and pop-up suspected locations on performance.

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

“…Accordingly, an important research problem in distributed surveillance is to design environment-based cooperative control laws for all coordinated agents such that the given area can be monitored efficiently. Recent research on distributed surveillance has been reported in [268]- [275], where a number of physical limitations were identified and considered such as time delay and uncertainties [271], [274], collision avoidance between agents [272], and heterogeneously distributed agents [274]. The current research is conducted under the assumption that each agent has enough power such that any designed control law can be applied.…”

Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance

“…According to [13], the probability that UAV m detects target j with one look in a grid where the target j exists is defined as a m j , and l m (q, t k ) is the number of looks on grid q performed by UAV m at t k time instant. Hence, the history detection numbers on grid q performed by UAV m can be recorded as:…”

“…Considering the communication delay, UAV m receives the detection history number that UAV n has searched on grid q can be expressed as h m,n (q, t k − τ n,m ) at t k time instant, where τ n,m is the arbitrary and finite communication delay, and L m,n (q, t k − τ n,m ) is the received corresponding total number of looks on grid q of UAV n. Hence, detection history information of the group stored by UAV m at t k time instant can be recorded asĥ m (q, t k ) = {h m,n (q, t k − τ n,m ), n ∈ U}. According to one look detection probability and history detection information, the detection probability of target j in grid q can be estimated [13] as:…”

“…Generally, surveillance region is uniformly divided into grids with the same size, and the existence probabilities of unknown targets are associated with different search maps, such as probability map [10,11], pheromone map [12] and rate of return map [13,14]. Based on the latest detection information gained by multiple UAVs, search map is updated according to corresponding criteria, such as Bayesian rules, pheromone update rules and rate of return update rules.…”

Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

“…Over the last one decade or so, computer-aided simulation of natural swarms has attracted a lot of research interest. It has been perceived through the efforts of the researchers that the functional principles from such biological swarms can be utilized in engineering for the development and control of multi-agent systems, such as the formation control of multi-robot systems [2,3], unmanned land, sea, or air vehicles [4,5], surveillance [6], rendezvous [7], coverage control on mobile sensing networks [8], and so on. Recently the concept of Computing Networks [9] has been developed to generalize swarm and neural architectures.…”