Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm.

https://www.mdpi.com/2076-3417/11/1/363/pdf

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

Forest fires are one of the major environmental concerns, each year millions of hectares are destroyed over the world, causing economic and ecological damage as well as human lives. Thus, predicting such an environmental issue becomes a critical concern to mitigate this threat. Several technologies and new methods have been proposed to predict and detect forest fires. The trend is toward the integration of artificial intelligence to automate the prediction and detection of fire occurrence. This paper presents a comprehensive survey of the machine learning algorithms based forest fires prediction and detection systems. First, a brief introduction to the forest fire concern is given. Then, various methods and systems in forest fires prediction and detection systems are reviewed. Besides works that reported fire prediction and detection systems, studies that assessed the factors influencing the fire occurrence and risk are discussed. The main issues and outcomes within each study are presented and discussed.

A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

A data-driven intelligent planning model for UAVs routing networks in mobile Internet of Things

This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

/pdf/a-review-on-drones-controlled-in-real-time-10fhf0xm7d.pdf

A review on drones controlled in real-time

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

This work presents a monitoring system for tactical forest fire-fighting operations based on a team of unmanned aerial vehicles and remote sensing techniques. Functions and missions of the system, as well as its architecture, are considered. Image processing and remote sensing algorithms are presented, a way for data integration into a fire-spreading model in a real-time forest fire response decision support system is proposed. The combination of automatic monitoring and remote sensing techniques with an approximate fire-spreading model can provide required credibility and efficiency of fire prediction and response.

Forest Fire-Fighting Monitoring System Based on UAV Team and Remote Sensing

This work presents the fire monitoring and detecting system for tactical forest fire-fighting operations based on a team of unmanned aerial vehicles, remote sensing, and image processing. The idea of such a system and its general parameters and possibilities are described. Functions and missions of the system, as well as its architecture, are considered. The image processing and remote sensing algorithms are presented, a way for data integration into a real-time DSS is proposed. The results of experimental research of the prototype system are presented. The combination of multi-UAV-based automatic monitoring, remote sensing and image processing techniques provides required credibility and efficiency of the fire detection.

Forest Fire Monitoring System Based on UAV Team, Remote Sensing, and Image Processing

This work presents the fuzzy-rough spatial model for the real-time decision support systems for complex dynamic systems. The proposed model uses the dynamic fuzzy-rough topology and the three-level spatial hierarchy, which represents discontinuous spatial areas at the upper level, continuous regions at the middle level and their approximation by the grid of cubic cells at the lower level. The boundaries of regions are blurred using fuzzy or rough sets of cells. The implementation of the model based on six GIS layers is presented. It reduces the computational complexity and provides the acceptable performance of DSS.

Approximate spatial model based on fuzzy-rough topology for real-time decision support systems

This work presents a novel approach to forest fight fighting using a heterogeneous ensemble of unmanned aerial vehicles of different types, which can perform patrol, confirmation, monitoring, and extinguishing missions. Their coordinated teamwork allows suppressing of the forest fires in the places not easily accessible by humans due to involving vehicles specially equipped with fire-extinguishing tools. The functions and missions of the ensemble as well as its architecture are considered. The coordination model based on the case-scenario control system and joint coordination framework of the heterogeneous ensemble is presented. The proposed model provide the required credibility and efficiency of fire response.

Forest Fire Fighting Using Heterogeneous Ensemble of Unmanned Aerial Vehicles

This work presents the soft topology model and the method of finding the suitable compensation for vehicles' activity scenarios that can keeping spatial configuration and satisfying safety restrictions for the vehicle onboard control system. The qualitative safety assessment is proposed, the implementation of the method based on the soft level topology is presented. The proposed method has acceptable computational complexity.

Igor Sokol

Papers

Forest Fire-Fighting Monitoring System Based on UAV Team and Remote Sensing

Forest Fire Monitoring System Based on UAV Team, Remote Sensing, and Image Processing

Approximate spatial model based on fuzzy-rough topology for real-time decision support systems

Forest Fire Fighting Using Heterogeneous Ensemble of Unmanned Aerial Vehicles

Maintaining the order of heterogeneous ensemble of unmanned vehicles using level soft topology