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.
Highlights
Environmental monitoring is one of the key applications of networked multi-robot systems, wherein each robot is expected to deploy over the mission space
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
We present a novel persistent object search and surveillance control with safety certificates for drone networks based on Control Barrier Function (CBF)
Summary
Environmental monitoring is one of the key applications of networked multi-robot systems, wherein each robot is expected to deploy over the mission space. The contributions of this paper are summarized as follows: 1) a novel constraint-based controller is presented so that a prescribed performance level is maintained, differently from the gradientbased persistent coverage algorithm (Hübel et al, 2008; Sugimoto et al, 2015), constraint-based coverage algorithms (Santos et al, 2019), and other related algorithms (Franco et al, 2015; PalaciosGasós et al, 2016; Wang and Wang, 2017), 2) a novel object search/surveillance problem is formulated, wherein the persistent coverage, safety certificates and energy persistency in (Egerstedt et al, 2018; Santos et al, 2019) and task switches between search and surveillance are integrated, and 3) the algorithm is demonstrated through experiments, where we put the vision data and associated image processing in the loop while other related publications purely examine only robot motion (Schwager et al, 2011; Sugimoto et al, 2015; Egerstedt et al, 2018; Funada et al, 2019; Santos et al, 2019). The incremental contributions relative to (Dan et al, 2020) are: 4) we implement the present partially distributed control architecture on Robot Operating System (ROS), while the experimental setup in (Dan et al, 2020) took a centralized control architecture, 5) owing to the contribution 4), we increase the number of drones from two to three in the experiment, and 6) we newly add simulation to precisely check if the performance is guaranteed in the absence of uncertain factors in real experiments
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