Multi-Robot 3D Coverage of Unknown Terrains

International audienceIn this paper we study the problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. In such a mission, the robots should simultaneously accomplish two objectives: firstly, to make sure that the overall terrain is visible by the team and, secondly, that the distance between each point in the terrain and one of the robots is as small as possible. These two objectives should be efficiently fulfilled given the physical constraints and limitations imposed at the particular coverage application (i.e., obstacle avoidance, limited sensor capabilities, etc). As the terrain's morphology is unknown and it can be quite complex and non-convex, standard multi-robot coordination and control algorithms are not applicable to the particular problem treated in this paper. In order to overcome such a problem, a new approach that is based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated in this paper. Both rigorous mathematical arguments and extensive simulations on unknown terrains establish that the proposed approach provides an efficient methodology that can easily incorporate any particular constraints and quickly and safely navigate the robots to an arrangement that optimizes surveillance coverage

On realistic target coverage by autonomous drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of such drones, it is necessary to develop new enhanced formulations of both common and emerging sensing scenarios. Namely, several fundamental challenges in visual sensing are yet to be solved including (1) fitting sizable targets in camera frames; (2) positioning cameras at effective viewpoints matching target poses; and (3) accounting for occlusion by elements in the environment, including other targets. In this article, we introduce Argus, an autonomous system that utilizes drones to collect target information incrementally through a two-tier architecture. To tackle the stated challenges, Argus employs a novel geometric model that captures both target shapes and coverage constraints. Recognizing drones as the scarcest resource, Argus aims to minimize the number of drones required to cover a set of targets. We prove this problem is NP-hard, and even hard to approximate, before deriving a best-possible approximation algorithm along with a competitive sampling heuristic which runs up to 100× faster according to large-scale simulations. To test Argus in action, we demonstrate and analyze its performance on a prototype implementation. Finally, we present a number of extensions to accommodate more application requirements and highlight some open problems

Distributed Coverage Control for Mobile Anisotropic Sensor Networks

<p>Distributed algorithms for (re)configuring sensors to cover a given area are important for autonomous multi-robot operations in application areas such as surveillance and environmental monitoring. Depending on the assumptions about the choice of the environment, the sensor models, the coverage metric, and the motion models of sensor nodes, there are different versions of the problem that have been formulated and studied. In this work, we consider the problem of (re)configuring systems equipped with anisotropic sensors (e.g., mobile robot with limited field of view cameras) that cover a polygonal region with polygonal obstacles for detecting interesting events. We assume that a given probability distribution of the events over this polygonal region is known. Our model has two key distinguishing features that are inherently present in covering problems with anisotropic sensors, but are not addressed adequately in the literature. First, we allow for the fact that the sensing performance may not be a monotonically decreasing function of distance. Second, motivated by scenarios where the sensing performance not only depends on the resolution of sensing, but also on the relative orientation between the sensing axis and the event, we assume that the probability of detection of an event depends on both sensing parameters and the angle of observation. We present a distributed gradient-ascent algorithm for (re)configuring the system of mobile sensors so that the joint probability of detection of events over the whole region is maximized. Simulation results illustrating the performance of our algorithms on different systems, namely, mobile camera networks, mobile acoustic sensor networks, and static pan-tilt-zoom camera networks are presented.</p