Dragonfly: a Tool for Simulating Self-Adaptive Drone Behaviours

Abstract

Drone simulators can provide an abstraction of different applications of drones and facilitate reasoning about distinct situations, in order to evaluate the effectiveness of these applications. In this paper we describe Dragonfly, a simulator of the behaviours of individual and collection of drones in various environments, involving random contextual variables and different environmental settings. Dragonfly supports the use of several drones in applications and evaluates the satisfaction of requirements under normal and exceptional situations. It simulates adaptive behaviours of drones due to exceptional situations. The adaption of drones is based on the use of wrappers implemented using aspect-oriented programming.

Cautious Adaptation of Defiant Components

“…We developed an open source drone simulator called Dragonfly [23] to implement both normal and exceptional situations of drones. Through its graphical user interface, the simulator allows the user to configure one or several drones, determine the source and target hospital locations; specify a river flowing between the hospitals; and set contextual variables such as initial battery level, battery consumption rate, wind strength, and drone above water.…”

LiveBox: A Self-Adaptive Forensic-Ready Service for Drones

“…In the following experiment, we have generated 100 flight paths using a software the simulators Dronology [12], Dragonfly [13], [14] and a specialised airspace editing tool ArduPilot. 8 Here is a basic algorithm to check compliance of zone by the geolocations on the flight paths of a duration T , which provides a a probabilistic answer to each flight path:…”

“…[12]. Apart from simulation-based approaches [13], policiesbased [17] and autonomy [18] approaches have also been proposed to control drone safety [19]. The goal of these approaches is to identify critical safety hazards where drones may collide with each other (i.e., collision avoidance), whilst our goal is in forensic readiness, i.e., to track the drones and Y. Yu et al: LiveBox: Self-Adaptive Forensic-Ready Service for Drones log their situations as tamper-proof and reliable evidence for forensic investigations [20].…”

“…FIGURE13. The total number of digits required to report the approximated geolocations with respect to non-adaptive or self-adaptive (SA) precision reporting strategies: SA reduces the bandwidth required by 46% from 64940 down to 44336 digits, while maintaining zero error rate.…”

RoboMAX: Robotic Mission Adaptation eXemplars

“…Similar to the 'Feed Me, Feed Me' IoT exemplar [12], our RoboMAX exemplars provide the high-level requirements and key contextual information associated with the considered systems and their adaptation concerns. In this way, our repository serves a different purpose than, and complements, existing SEAMS exemplars, which provide simulators for simple robotics applications (UNDERSEA [13], Dragonfly [14] and DARTSim [15]) or generic frameworks for developing selfadaptive cyber-physical applications (DEECo [16], Intelligent Ensembles [17]), or datasets (AMELIA [18]).…”