Persistent Object Search and Surveillance Control With Safety Certificates for Drone Networks Based on Control Barrier Functions

Abstract

In this paper, we address a persistent object search and surveillance mission for drone networks equipped with onboard cameras, and present a safe control strategy based on control barrier functions The mission for the object search and surveillance in this paper is defined with two subtasks, persistent search and object surveillance, which should be flexibly switched depending on the situation. Besides, to ensure actual persistency of the mission, we incorporate two additional specifications, safety (collision avoidance) and energy persistency (battery charging), into the mission. To rigorously describe the subtask of persistent search, we present a novel notion of γ-level persistent search and the performance certificate function as a candidate of a time-varying Control Barrier Function. We then design a constraint-based controller by combining the performance certificate function with other CBFs that individually reflect other specifications. In order to manage conflicts among the specifications, the present controller prioritizes individual specifications in the order of safety, energy persistency, and persistent search/object surveillance. The present controller is finally demonstrated through simulation and experiments on a testbed.

A Two-Layer Control Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Network

“…Finally, the persistent monitoring algorithm comprises modeling the importance of area on each smaller region as a density function and deploying the robots based on a timevarying Voronoi-based coverage controller [20], [21]. Here, we introduce a novel approach to construct a density function for fields in grids.…”

“…Specifically, the density function on a sub-region is updated, marking it less important, once a robot senses it. Built on top of this idea, the persistent coverage control scheme [15], [20], [21] introduces the concept of information decay that rebounds the density function after the robot leaves the sub-region for a certain time. This addition generates a persistent patrolling motion over a given region of interest.…”

“…Specifically, the first layer (task allocation strategy) encapsulates the targeted grids as several smaller regions via Kmeans clustering [23], [24] followed by assigning the robots to monitor the regions accordingly via a modified ant colony optimization (M-ACO) [25]. Then, the second layer (per-sistent monitoring algorithm) models the importance of the area in each smaller region as a density function and further generates the control input for each robot based on a persistent coverage control algorithm [20], [21]. In particular, the main contributions of this paper are listed as follows:…”

Distributed Coverage Hole Prevention for Visual Environmental Monitoring with Quadcopters via Nonsmooth Control Barrier Functions

“…CBFs provide the forward invariance property to the admissible set of the robot state space [30], [31]. CBFs have been employed in various applications ranging from bipedal robots [32], [33] to swarm robots [34]- [37] and seeing success also in the context of environmental monitoring [38]- [40]. The work [38] proposes a control framework that guarantees the robot's battery is never depleted during a monitoring task.…”

“…The proposed methods do not impose any requirements on the rate of change of the density functions and can be executed in a distributed fashion. In [40], a notion of information reliability is encoded through a time-varying density function as in [41], which decays if a region is not surveilled for a long period. Then, a patrolling motion is achieved by a time-varying CBF together with other safety constraints.…”