Cloud-Supported Coverage Control for Persistent Surveillance Missions

Abstract

A decomposition-based coverage control scheme is proposed for multi-agent, persistent surveillance missions operating in a communication-constrained, dynamic environment. The proposed approach decouples high-level task assignment from low-level motion planning in a modular framework. Coverage assignments and surveillance parameters are managed by a central base station, and transmitted to mobile agents via unplanned and asynchronous exchanges. Coverage updates promote load balancing, while maintaining geometric and temporal characteristics that allow effective pairing with generic path planners. Namely, the proposed scheme guarantees that (i) coverage regions are connected and collectively cover the environment, (ii) subregions may only go uncovered for bounded periods of time, (iii) collisions (or sensing overlaps) are inherently avoided, and (iv) under static event likelihoods, the collective coverage regions converge to a Pareto-optimal configuration. This management scheme is then paired with a generic path planner satisfying loose assumptions. The scheme is illustrated through simulated surveillance missions.

Dynamic Control Scheme of Multiswarm Persistent Surveillance in a Changing Environment

“…In the proposed model, the overall number of UAVs in the surveillance is set as U sys and the UAV is regarded as a resource in the repository pool [34], where the total surveillance capacity is P sys .…”

A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints

“…Research interest for the coverage and coordination of multi-agent has shown an increase in the field of artificial intelligence (AI) and control [1,2,3,4,5,6]. In particular, coverage control using multiple robots with limited sensing capabilities has received significant attention recently due to its versatility in many applications, such as mapping, patrolling, surveillance, and complete coverage [3,4,5,6,7,8,9,10,11]. However, it is more difficult to achieve persistent coverage for a group of multiple robots considering obstacle avoidance and coverage period, because mission domains may have differently shaped obstacles, as well as more complicated constraints.…”