Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms

Abstract

Abstract:The search of a given area is one of the most studied tasks in swarm robotics. Different heuristic methods have been studied in the past taking into account the peculiarities of these systems (number of robots, limited communications and sensing and computational capacities). In this work, we introduce a behavioral network made up of different well-known behaviors that act together to achieve a good performance, while adapting to different scenarios. The algorithm is compared with six strategies based on movement patterns in terms of three performance models. For the comparison, four scenario types are considered: plain, with obstacles, with the target location probability distribution and a combination of obstacles and the target location probability distribution. For each scenario type, different variations are considered, such as the number of agents and area size. Results show that although simplistic solutions may be convenient for the simplest scenario type, for the more complex ones, the proposed algorithm achieves better results.

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm

“…In previous works, we developed the city simulator that integrates models of traffic, pedestrians, climate, and pollution [8]; the behavior-based algorithm that allows the aerial swarm to carry out search and surveillance tasks [9,10]; and the data fusion algorithm to build traffic maps from car detections [11].…”

“…The drone swarm is controlled by an algorithm originally developed for a search task in an open environment [9] and later adapted to perform traffic monitoring in SwarmCity [8]. Similarly to that works, the drones are considered to fly at a constant height of 20 m, so they can detect cars and people within a circle with a radius of 10 m. To organize the surveillance, the area is split into cells of 14.1 × 14.1 m and the drones move between their centers.…”

Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety

“…This can be due to the location (indoor [99] or in so-called urban canyons between high rise buildings [50]) disasters (such as e.g., Hurricane Katrina, which lead to the disconnection of 3 million land-line phones and more than 2000 cell sites [55]) or when the infrastructure has been compromised (e.g., [120]). A significant amount of research [7,[12][13][14][15][16][17][18][19]121] is now directed at self-organizing formation control and self-assembly methods especially in the absence of global communication.…”

“…Such collectives of cooperating devices often solve problems far more efficiently than a single device, especially in environments subject to continuous change. In [12] the authors identify 3 categories of applications which are currently attracting a lot of attention from the community: (1) formation control and self-assembly methods ( [11,[13][14][15][16][17][18][19]), (2) localization and search methods ( [20][21][22][23][24]) and (3) techniques for solving optimization problems ( [25][26][27]). Furthermore, ref [12] highlights the ability to sense as an integral basic functionality.…”

Editorial: Special Issue “Swarm Robotics”

“…A model based on Swarm Chemistry is used in [12] to investigate as interesting patterns can be detected. Finally, a three-dimensional (3D) model identification method based on weighted implicit shape representation (WISR) is proposed in [13].…”