A Multi-Robot Sense-Act Approach to Lead to a Proper Acting in Environmental Incidents

Kovács²

2019

177

The use of UAVs in areas ranging from agriculture over urban services to entertainment or simply as a hobby has rapidly grown over the last years. Regarding serious/commercial applications, UAVs have been considered in the literature, especially as mobile sensing/actuation platforms (i.e., as a delivery platform for an increasingly wide range of sensors and actuators). With regard to timely, cost-effective and very rich data acquisition, both, NEC Research as well as TNO are pursuing investigations into the use of UAVs and swarms of UAVs for scenarios where high-resolution requirements, prohibiting environments or tight time constraints render traditional approaches ineffective. In this review article, we provide a brief overview of safety and security-focused application areas that we identified as main targets for industrial and commercial projects, especially in the context of intelligent autonomous systems and autonomous/semi-autonomously operating swarms. We discuss a number of challenges related to the deployment of UAVs in general and to their deployment within the identified application areas in particular. As such, this article is meant to serve as a review and overview of the literature and the state-of-the-art, but also to offer an outlook over our possible (near-term) future work and the challenges that we will face there.

Section: Reference Wan Rs Rtm Sar Gl Int Simentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To Smart Cities and Public Safetymentioning

confidence: 99%

See 1 more Smart Citation

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

Kovács²

2019

177

“…In the context of the article at hand we would like to emphasis 4 main application areas where UAVs are already used for various data-collection / sensing centered missions. These areas, which are briefly introduced below and for which the algorithm proposed in this article was mainly developed, are: Section 1.2.1: Precision agriculture [8,10,16,[22][23][24] Section 1.2.2: Disaster response and relief [9][10][11]13,16,21,[25][26][27] Section 1.2.3: Smart city applications and civil protection [9,11,16,21,28] Section 1.2.4: Land administration and wildlife protection [10,13,16,[29][30][31] 1.2.1. Precision agriculture…”

Section: Relevant Application Areasmentioning

confidence: 99%

Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Kovács²,

Saffre

et al. 2019

Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.

“…They are increasingly considered as candidates for mobile/aerial urban sensing platforms [1]. Projected advances in cloud computing, wireless sensors and networked unmanned systems motivate this claim further, and indeed, in the literature UAVs are increasingly being suggested as flexible mobile platforms [2] used for applications as wide as general monitoring [3][4][5][6], monitoring and surveillance [7][8][9], to provide situational awareness support [10], detect victims or their life-signs [4] and participate in search and rescue (SAR) or aerial tracking [7]. This is especially applicable in the direct aftermath of a natural of man-made disaster as these events often result in partial or total loss of communication and data-collection infrastructure.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Voronoi Tessellation/Genetic Algorithm Approach for the Deployment of Drone-Based Nodes of a Self-Organizing Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

Eledlebi

Ruta

et al. 2020

Using autonomously operating mobile sensor nodes to form adaptive wireless sensor networks has great potential for monitoring applications in the real world. Especially in, e.g., disaster response scenarios—that is, when the environment is potentially unsafe and unknown—drones can offer fast access and provide crucial intelligence to rescue forces due the fact that they—unlike humans—are expendable and can operate in 3D space, often allowing them to ignore rubble and blocked passages. Among the practical issues faced are the optimizing of device–device communication, the deployment process and the limited power supply for the devices and the hardware they carry. To address these challenges a host of literature is available, proposing, e.g., the use of nature-inspired approaches. In this field, our own work (bio-inspired self-organizing network, BISON, which uses Voronoi tessellations) achieved promising results. In our previous approach the wireless sensors network (WSN) nodes were using knowledge about their coverage areas center of gravity, something which a drone would not automatically know. To address this, we augment BISON with a genetic algorithm (GA), which has the benefit of further improving network deployment time and overall coverage. Our evaluations show, unsurprisingly, an increase in energy cost. Two variations of our proposed GA-BISON deployment strategies are presented and compared, along with the impact of the GA. Counter-intuitively, performance and robustness increase in the presence of noise.