Use Of Swarm Research Articles

Environmental safety problems of swarm use of UAVs in precision agriculture

Digital technologies are an integral part of the functioning of many sectors of the state, which applies, in particular, to the agricultural sector, which plays a significant role in shaping the state’s economy, ensuring food independence and export potential. One of the promising areas of digital transformation is the swarm use of unmanned aerial vehicles in precision agriculture, the use of which significantly increases productivity while reducing production costs. Analysis of the transport and technological cycles of UAVs allows us to identify a number of existing problems in the swarm use of UAVs that are associated with anthropogenic impact. The article examines the potential impact of unmanned aerial vehicles on the environment, analyzing such aspects as noise and vibration emissions, air and soil pollution, electromagnetic radiation, and the impact on biodiversity and ecosystems in general. The study is aimed at identifying key environmental problems, identifying ways to minimize and solve them. The authors offer recommendations for ensuring environmental safety and sustainable development of precision agriculture with the swarm use of unmanned systems.

Cost-effectiveness analysis of the implementation of transport and technological cycles in the swarm use of agricultural UAVs

The article presents an approach that provides a comprehensive analysis of the cost-effectiveness of the implementation of transport and technological cycles in the swarm use of agricultural UAVs. A generalized “effect-cost” assessment is presented for ensuring the transport process within the framework of acceptable implementations of the transporttechnological cycle. It is shown that for swarm applications of UAVs in precision agriculture, cost-benefit analysis is also directly related to the microprocessor performance of the UAV swarm. The cost-effectiveness model proposed in the work is based on a previously obtained solution to the problem of optimal performance of a UAV swarm used for spraying crops. The results of improving the cost function are presented using a model example that illustrates the proposed approach. It is noted that the presented problem statement helps developers clearly identify alternatives and formulate additional questions that need to be answered to make a decision.

Ubiquitous Unmanned Aerial Vehicles (UAVs): A Comprehensive Review

Unmanned aerial vehicles (UAVs) or drones have found applications in various fields including military operations, construction, parcel delivery, mapping, medical, search and rescue missions, exploration of hidden areas, monitoring power lines and oil rigs, precision farming, aerial surveillance, and wireless communication. Due to their modular flexibility and programmability, drone technologies have become a strategic industrial/commercial sector that has gained significant attention as it redefines and combines different emerging domains, such as: service and delivery convergence, lean manufacturing, environmental monitoring and security. The use of swarms of interconnected UAV systems has gained popularity in providing innovative approaches to tasks requiring wide-area multiple collaborative sensors. However, despite advances in maneuverability, user interface, and cost-effectiveness, drones still face limitations in flight autonomy due to restricted flight time for continuous missions. Battery endurance, drone weight, and payload are critical development vectors. This review highlights the importance of drones, their future development priorities and functionality. Specifically, it focuses on UAV classification, swarms, and charging. Moreover, it explores UAV applications, challenges, and security issues based on recent research and development. Additionally, the study delves into anti-drone technology that can assist countries in controlling and ensuring the safe utilization of drones.

Wildfire detection in large-scale environments using force-based control for swarms of UAVs

Wildfires affect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfires can be beneficial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfires. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The first three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of different sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fires using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fires are generated in the area.

Swarm Robotics: Simulators, Platforms and Applications Review

This paper presents an updated and broad review of swarm robotics research papers regarding software, hardware, simulators and applications. The evolution from its concept to its real-life implementation is presented. Swarm robotics analysis is focused on four aspects: conceptualization, simulators, real-life robotics for swarm use, and applications. For simulators and robots, a detailed comparison between existing resources is made. A summary of the most used swarm robotics applications and behaviors is included.

The Use of Swarms of Unmanned Aerial Vehicles in Mitigating Area Coverage Challenges of Forest-Fire-Extinguishing Activities: A Systematic Literature Review

The use of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, has grown rapidly over the past two decades and continues to expand at a rapid pace. This has resulted in the production of many research papers addressing the use of UAVs in a variety of applications, such as forest firefighting. The main purpose of this paper is to provide a comprehensive overview of UAV-based forest-fire-extinguishing activity (FFEA) operations. To achieve this goal, a systematic literature review was conducted to answer a specific set of questions, which were carefully formulated to address the results of research conducted between 2008 and 2021. This study aims to (i) expand our understanding of the development of UAVs and their current contributions to the FFEA; (ii) identify particularly novel or unique applications and characteristics of UAV-based fire-extinguishing systems; (iii) provide guidance for exploring and revising further ideas in this field by identifying under-researched topics and other areas in which more contributions are needed; and (iv) explore the feasibility of using UAV swarms to enable autonomous firefighting in the forest without human intervention. Of the 1353 articles systematically searched across five databases (Google Scholar, ACM Digital Library, Science Direct, Scopus, and IEEE Explore), 51 highly relevant articles were found to meet the inclusion criteria; therefore, they were analyzed and discussed. The results identified several gaps in this field of study among them the complexity of coordination in multi-robotic systems, the lack of evaluation and implementation of fire extinguishing systems, the inability of handling multiple spot fires, and poor management of time and resources. Finally, based on the conducted review, this paper provides significant research directions that require further investigations by researchers in this field including, the deployment of UAV-based Swarm Robotics, further study on the characteristics of the fire extinguishing systems; design more effective area coverage; and the propose of a self-firefighting model that enables individuals to decide on the course of events efficiently and locally for better utilization and management of time and resources.

Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price reduction and the increased capabilities of drones. The drones in the swarm need to cooperatively explore an unknown area, in order to identify and monitor interesting targets, while minimizing their movements. In this work, we propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. The proposed framework relies on the possibility for the UAVs to exchange some information through a communication channel, in order to achieve context-awareness and implicitly coordinate the swarm's actions. Our experiments show that the proposed method can yield effective strategies, which are robust to communication channel impairments, and that can easily deal with non-uniform distributions of targets and obstacles. Moreover, when agents are trained in a specific scenario, they can adapt to a new one with minimal additional training. We also show that our approach achieves better performance compared to a computationally intensive look-ahead heuristic.

Time Efficient Unmanned Aircraft Systems Deployment in Disaster Scenarios Using Clustering Methods and a Set Cover Approach

Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network of elements that can communicate, the system is said to form an Unmanned Aircraft System (UAS). This system is particularly effective when the aircraft within are organized into swarms with sets of objectives to accomplish. The extensive use of swarms into UASs is more and more exploited nowadays due to the decreasing cost of those aircraft. In the present work we are interested in a particular application of UASs, namely their deployment in disaster scenarios for communications services provision to targets on the ground. These ground targets, however, are not part of the UASs and should not be confused with ground-based controllers. The present work does not only focus on coverage for ground targets but also on a guaranteed minimum number of covers for each target, which is called the redundancy requirement. The research work also ensures that the deployed UAS forms a unique connected component so that a steady stream of communication is kept with the targets to cover. Research work similar to the present perform the initial deployment of their aircraft in a different manner, either randomly, based on a predetermined grid formation, or using other elaborated methods. This work proposes a new solution based on the use of clustering algorithms, combined to a design of the problem formulated as a set cover optimization model. The clustering phase is used to discretize the search space and ease the optimization phase by locating regions of interest, and then a further procedure is applied, only when needed, to reconnect scattered connected components and guarantee connectivity in the networks. This way of doing it has achieved a deployment of UASs with maximum coverage for all targets, a guaranteed minimum number of covers for each of them, and results in a competitive computation time. The latter also allowed for more scalability by extending the tests to very large input instances.

Deceiving Autonomous Drones

This speculative article examines the concept of deceiving autonomous drones that are controlled by artificial intelligence (AI) and can work without operational input from humans. This article examines the potential of autonomous drones, their implications and how deception could possibly be a defence against them and /or a means of gaining advantage. It posits that officially, no truly autonomous drone is operational now, yet the development of AI and other technologies could expand the capabilities of these devices, which will inevitably confront society with a number of deep ethical, legal, and philosophical issues. The article also examines the impact of autonomous drones and their targets in terms of the power/deception nexus. The impact of surveillance and kinetic impacts on the target populations is investigated. The use of swarms can make deception more difficult although security can be breached. The Internet of Things can be considered as based on the same model as a swarm and its impact on human behaviour indicates that deception or perhaps counter-deception should be considered as a defence. Finally, the issues raised are outlined. However, this article does not provide definitive answers but, hopefully, exposes a number of issues that will stimulate further discussion and research in this general area.

Ultra-Reliable IoT Communications with UAVs: A Swarm Use Case

UAVs are powerful IoT components, offering sensing, communications and data analysis in the air. Recently, 3GPP TS 22.261, TR 22.862, and TR 36.777 have specified performance requirements for communications between multiple UAVs in the 5G domain. This article discusses communications reliability challenges in a UAV swarm context. Recommendations for designing an ultra-reliable communications system for UAV swarms are introduced with a focus on the software protocol stack and RF hardware. For the purpose of demonstration, we developed Easy- Swarm, an open-source UAV swarming platform that adopts the LoRa radio at the physical layer and a low-latency channel access protocol at the MAC layer. Real-life testbeds are built consisting of 10 UAVs and 20 robotic cars that produce background traffic. LoRa, WiFi and LTE networks are employed to provide broadband and cellular wireless network support. Results show that using LoRa leads to better reliability, in particular allowing for higher swarm density and longer coverage distance, than when WiFi is used. In addition, LTE provides the best reliability and latency for UAV swarms with good network connectivity.

Distributed optimization Grid resource discovery

Grid computing is a framework for large-scale resource sharing and indexing that evolves with the goal of resource provisioning. In this paper, we develop a distributed learning automata (DLA) based on multi-swarm discrete particle swarm optimization (PSO) approach for Grid resource discovery, called distributed optimization grid (DOG) resource discovery algorithm. This algorithm makes use of swarms of particles for different computational resource metrics while a group of DLA is the control unit of each swarm of particles. The algorithm takes advantage of the PSO solution diversity to optimize the quality of delivered resource. Moreover, the recommended algorithm uses DLA as a fully distributed model for imitating the Grid infrastructure topology. Our experimental results show that DOG is fast as well as efficient and accurate.

ChemInform Abstract: Fantastic Voyage: Designing Self‐Powered Nanorobots

AbstractReview: 89 refs.

Fantastic Voyage: Designing Self‐Powered Nanorobots

The use of swarms of nanobots to perform seemingly miraculous tasks is a common trope in the annals of science fiction.1 Although several of these remarkable feats are still very much in the realm of fiction, scientists have recently overcome many of the physical challenges associated with operating on the small scale and have generated the first generation of autonomous self-powered nanomotors and pumps. The motors can be directed by chemical and light gradients, pick up and deliver cargo, and exhibit collective behavior.