Monitoring and Cordoning Wildfires with an Autonomous Swarm of Unmanned Aerial Vehicles

Unmanned aerial vehicles, or drones, are already an integral part of the equipment used by firefighters to monitor wildfires. They are, however, still typically used only as remotely operated, mobile sensing platforms under direct real-time control of a human pilot. Meanwhile, a substantial body of literature exists that emphasises the potential of autonomous drone swarms in various situational awareness missions, including in the context of environmental protection. In this paper, we present the results of a systematic investigation by means of numerical methods i.e., Monte Carlo simulation. We report our insights into the influence of key parameters such as fire propagation dynamics, surface area under observation and swarm size over the performance of an autonomous drone force operating without human supervision. We limit the use of drones to perform passive sensing operations with the goal to provide real-time situational awareness to the fire fighters on the ground. Therefore, the objective is defined as being able to locate, and then establish a continuous perimeter (cordon) around, a simulated fire event to provide live data feeds such as e.g., video or infra-red. Special emphasis was put on exclusively using simple, robust and realistically implementable distributed decision functions capable of supporting the self-organisation of the swarm in the pursuit of the collective goal. Our results confirm the presence of strong nonlinear effects in the interaction between the aforementioned parameters, which can be closely approximated using an empirical law. These findings could inform the mobilisation of adequate resources on a case-by-case basis, depending on known mission characteristics and acceptable odds (chances of success)

Socially-Sensitive Systems Design:Exploring Social Potential

In human society, individuals have long voluntarily organized themselves in groups, which embody, provide and/or facilitate a range of different social concepts, such as governance, justice, or mutual aid. These social groups vary in form, size, and permanence, but in different ways provide benefits to their members. In turn, members of these groups use their understanding and awareness of group expectations to help determine their own actions, to the benefit of themselves, each other, and the health of the group

Designing Behavioural Artificial Intelligence to Record, Assess and Evaluate Human Behaviour

The context of the work presented in this article is the assessment and automated evaluation of human behaviour. To facilitate this, a formalism is presented which is unambiguous as well as such that it can be implemented and interpreted in an automated manner. In the greater scheme of things, comparable behaviour evaluation requires comparable assessment scenarios and, to this end, computer games are considered as controllable and abstract environments. Within this context, a model for behavioural AI is presented which was designed around the objectives of: (a) being able to play rationally; (b) adhering to formally stated behaviour preferences; and (c) ensuring that very specific circumstances can be forced to arise within a game. The presented work is based on established models from the field of behavioural psychology, formal logic as well as approaches from game theory and related fields. The suggested model for behavioural AI has been used to implement and test a game, as well as AI players that exhibit specific behavioural preferences. The overall aim of this article is to enable the readers to design their own AI implementation, using the formalisms and models they prefer and to a level of complexity they desire

Force-Based Self-Organizing MANET/FANET with a UAV Swarm

This paper introduces a novel distributed algorithm designed to optimize the deployment of access points within Mobile Ad Hoc Networks (MANETs) for better service quality in infrastructure-less environments. The algorithm operates based on local, independent execution by each network node, thus ensuring a high degree of scalability and adaptability to changing network conditions. The primary focus is to match the spatial distribution of access points with the distribution of client devices while maintaining strong connectivity to the network root. Using autonomous decision-making and choreographed path-planning, this algorithm bridges the gap between demand-responsive network service provision and the maintenance of crucial network connectivity links. The assessment of the performance of this approach is motivated by using numerical results generated by simulations

Experimental Connectivity Analysis for Drones in Greenhouses

This study aims to explore the communication capabilities for video crucial applications of two commercial drones—the Parrot AR.Drone 2.0 and the Parrot Anafi—in a greenhouse environment. Experiments were conducted on Received Signal Strength (RSS), Round-Trip Time (RTT) and the throughput on 802.11n at the 2.4 GHz network. From the experiments, it was found that none of the UAVs have an isotropic radiation pattern. Indoor measurements close to the roof and the ground were more prone to signal degradation. Even though the RTT of the Parrot Anafi was higher than that of the AR.Drone 2.0, the Anafi in almost all cases managed to achieve higher throughput and lower path loss, proving its superiority for video application. In addition, the maximum distance that the Parrot Anafi could fly in the greenhouse without any video quality loss was 110 m, while the AR.Drone 2.0 was hardly able to reach 30 m. Finally, the effect of the propellers has an insignificant impact on the UAV connection characteristics in all tested scenarios

Experimental Connectivity Analysis for Drones in Greenhouses

This study aims to explore the communication capabilities for video crucial applications of two commercial drones—the Parrot AR.Drone 2.0 and the Parrot Anafi—in a greenhouse environment. Experiments were conducted on Received Signal Strength (RSS), Round-Trip Time (RTT) and the throughput on 802.11n at the 2.4 GHz network. From the experiments, it was found that none of the UAVs have an isotropic radiation pattern. Indoor measurements close to the roof and the ground were more prone to signal degradation. Even though the RTT of the Parrot Anafi was higher than that of the AR.Drone 2.0, the Anafi in almost all cases managed to achieve higher throughput and lower path loss, proving its superiority for video application. In addition, the maximum distance that the Parrot Anafi could fly in the greenhouse without any video quality loss was 110 m, while the AR.Drone 2.0 was hardly able to reach 30 m. Finally, the effect of the propellers has an insignificant impact on the UAV connection characteristics in all tested scenarios