Terrain Coverage Research Articles

Multicircuit Route Planning for UAVs Performing the Terrain Coverage Task

Multicircuit Route Planning for UAVs Performing the Terrain Coverage Task

Low-Cost Computer-Vision-Based Embedded Systems for UAVs

Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.

A newly developed chemical locomotory booster for cyborg insect to sustain its activity and to enhance covering performance

Terrestrial insect-machine hybrid robots have long been discussed as alternatives to artificial centimetre-scale mobile robots. These hybrid robots can possess various functionalities (e.g., localization, object detection) by embedding electronic devices while also benefitting from the insects' robust mobility. However, as insects have relatively low walking speed and insensitivity after repeated electrical stimulation, their motion activeness is hard to maintain at a high level. This study attempts to exploit chemical stimulation to boost the insects' walking activity. The use of methyl salicylate as a chemical booster was examined for improving walking activity. The chemical increased the insects' walking speed by 24.3%, which in turn extended their travel distance and region coverage. Also, methyl salicylate still retained the insects' reaction to the electrical induction. By incorporating the locomotory improvements of methyl salicylate with an electrical stimulation protocol, the hybrid robots could reach a terrain coverage speed of 21.6 cm2/s, much higher than only implementing the electrical stimulation (6.8 cm2/s) or no external stimulation at all (1.2 cm2/s). Overall, this is the first demonstration of methyl salicylate's boosting effect on the insects' activity and improving terrain coverage, filling a crucial gap in practical uses of terrestrial insect-machine hybrid robots.

An optimization-based approach for an integrated forest fire monitoring system with multiple technologies and surveillance drones

Wildfires pose a significant threat to forests and the delicate natural balance. In this work, we present a novel optimization approach for designing forest fire monitoring systems that incorporate surveillance towers, monitoring balloons, and drone technology, which have gained popularity in recent years for monitoring, logistics, and civil applications. We develop a compact mixed-integer linear programming model that includes location decisions for multiple monitoring technologies and routing of drones among monitoring facilities to achieve the most extensive possible terrain coverage. Additionally, we propose a matheuristic algorithm that comprises six components: a solution procedure, perturbation procedures, local search procedures, a call to the general-purpose solver to solve a mixed-integer linear programming model, a global reset strategy, a local reset strategy, and an acceptance criterion. We test the proposed model and algorithm on a set of random instances and a real-life case study in Chile. While the proposed model can only solve small instances, the matheuristic can find good-quality solutions for all instances. This study can aid the government and private sector in designing an integrated fire monitoring system that leverages watchtowers, monitoring balloons, and drones.

Experimental study of terrain coverage of an autonomous chaotic mobile robot

In this work, the design and implementation of an autonomous mobile robot that can cover given workspaces, is presented. The motion commands are produced by a bit sequence, which is created from the combination, through an XOR function, of two separate chaotic random bitstreams. The implementation of the proposed method is performed by a robotic platform which is controlled through a microcontroller (Arduino Uno). Also, an ultrasonic sensor is inserted into the robotic platform with basic aim to locate and avoid the boundaries of the workspace. The robot’s behavior is studied experimentally in different scenarios with various starting positions. The experimental results confirm that this approach, with an appropriate sensor for avoiding collision with the boundaries, can obtain very satisfactory results in regard to the fast scanning of the robot’s workspace with unpredictable way.

VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

The use of unmanned aerial vehicles with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximizing the land covered from the flight path a challenging objective, especially when the area to be monitored is irregular, large and includes many blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyze large areas and find new paths providing maximum visibility. This article shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called visibility-based path planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas of the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.

Multi UAV Coverage Path Planning in Urban Environments.

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

WSN Deployment Strategy for Real 3D Terrain Coverage Based on Greedy Algorithm with DEM Probability Coverage Model

The key to the study of node deployment in Wireless Sensor Networks (WSN) is to find the appropriate location of the WSN nodes and reduce the cost of network deployment while meeting the monitoring requirements in the covered area. This paper proposes a WSN node deployment algorithm based on real 3D terrain, which provides an effective solution to the surface-covering problem. First of all, actual geographic elevation data is adopted to conduct surface modeling. The model can vividly reflect the real terrain characteristics of the area to be deployed and make the deployment plan more visible and easy to adjust. Secondly, a probabilistic coverage model based on DEM (Digital Elevation Model) data is proposed. Based on the traditional spherical coverage model, the influence of signal attenuation and terrain occlusion on the coverage model is added to make the deployment model closer to reality. Finally, the Greedy algorithm based on grid scanning is used to deploy nodes. Simulation results show that the proposed algorithm can effectively improve the coverage rate, reduce the deployment cost, and reduce the time and space complexity in solving the WSN node deployment problem under the complex 3D land surface model, which verifies the effectiveness of the proposed algorithm.

Graph-based path planning for intelligent UAVs in area coverage applications

In recent times, Unmanned Air Vehicles (UAVs) are being deployed for several tasks of terrain coverage such as surveillance, photogrammetry, smart irrigation, civil security, and disaster management. In these applications, one of the most vital issues to be addressed is, covering the area under observation with minimum traversal for the UAV. So, the problem addressed in this paper is as follows: For a given geographic area and the given parameters describing the UAV’s coverage capacity, the problem is to find an optimal route that covers the given geographic area. In this paper, an optimal path planning algorithm for the area under observation, given as a closed curve, is proposed. The algorithm partitions the given area of interest into multiple non-uniform rectangles while considering other parameters such as the flying altitude of the UAV and obstacles that could be encountered during its flight. The problem is transformed into Traveling Salesman Problem by constructing a graph from the rectangular partitioning. Effective approximate solutions are provided to this problem, using the Minimum Spanning Tree (MST) approximation algorithm and Ant Colony Optimization (ACO). The experimental results show that ACO outperforms the MST based algorithm as it does not get stuck in local minima.

Landscape evolution in the area of Kazimierski Landscape Park

Abstract The presented research concerning the landscape evolution of the area of the Kazimierski Landscape Park assumed the analysis of landscape changes that took place in the western part of the Nałęczów Plateau in eastern Poland as a result of increased anthropopressure. To achieve this goal, the diverse data was employed: archives of the “Archeological Survey of Poland” obtained from the National Heritage Board of Poland, the registry data from the 16th century made available as part of the “Atlas Fontium” project elaborated by the Institute of History of the Polish Academy of Sciences, old map of the Western Galicia from 1808, a tactical map of the Military Geographical Institute in Warsaw from 1937, and the “Topographic Objects Database” (BDOT10k) obtained from the Head Office of Geodesy and Cartography. Based on the analysed data, maps presenting the development of the settlement network from the moment of permanent settlement of the area up to the present state and maps showing the changes in land use in the Kazimierski Landscape Park over the last centuries have been designed. The results of historical and geographical analyses carried out as part of the research were compared with the results of geomorphological research conducted in the studied area. Verification of the obtained results allowed to determine the scope in which man influenced the evolution of the landscape of the analysed area, including its diversification depending on the historical period and the type of the economy. The process of georeferencing the map of Western Galicia elaborated by colonel A.M. von Heldensfeld from 1808 and the tactical map 1:100,000 of Military Geographical Institute from 1937 covering the research area (14,974 ha) was carried out in the ArcGIS Desktop software. The calibration process was evaluated in the Map Analyst program which makes it possible to analyze the distortions of old maps. Then, vectorization of selected terrain coverage classes and visualization of spatial data were conducted. This way, the land use maps were analyzed in terms of environmental changes that occurred over the past centuries by identifying the areas with the largest development changes. The obtained results were compared with the database of historical objects created for the purposes of this research, based on data provided by the National Heritage Board of Poland and the Institute of History of the Polish Academy of Sciences in order to determine the development trends of the settlement network in the region. The additional statistical analysis made it possible to determine the trend of changes and to interpret the distribution of areas threatened by the occurrence of mass movements with the analyzed anthropogenic determinants.

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

Use of UAV-Borne Spectrometer for Land Cover Classification

Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU) navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

Post‐disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches

AbstractIn this paper, we provide a review of the principal aspects related to search & rescue (SAR) with unmanned aerial vehicles (UAVs), with particular interest in the phase of post‐disaster assessment (PDA). Some areas of interest related to this topic have been chosen for the analysis: the aerial platforms used in the field, multirobot software architectures, onboard sensors and simultaneous localization and mapping approaches, terrain coverage algorithms, autonomous navigation techniques, and human‐swarm interfaces. All these aspects have been analyzed with respect to the state‐of‐the‐art, and also in relation to the project PRISMA, which focuses on the development and deployment of robots and autonomous systems that can operate in emergency scenarios, with a specific reference to monitoring and real‐time intervention.

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy.

Mountainous terrain coverage in mobile sensor networks

The sensing field on three-dimensional (3D) terrains that is supposed to be fully covered by deployed nodes may not be covered because of random aerial deployment. Non-uniform coverage has a negative impact upon that their lifetime and performance get short and worse, respectively. Mobile sensor networks (MSNs) are introduced to improve a coverage problem from sensor malfunction by an obstacle or the terrestrial environment. Providing coverage in a mountainous terrain has become critical issues in MSNs. This work proposes a 3D coverage method that can consider a physical feature, such as slope, in a mountainous terrain, that is, a 3D spiral model that allows mobile sensors to move freely in spiral lines devised using the height and radius of a concaved terrain. In the proposed model, the reasonable number of sensors to be deployed and the designated sensing points achieve full coverage. This paper focuses on full coverage for which the spiral model challenges the reasonability in the number of sensors. The experimental results show that the proposed method provides improved performance in terms of coverage ratios and expected coverage time; in addition, this has higher accuracy in lower power cost and better expansibility.

Multispectral Laser Head for Terrain Identification and Analysis

The re ectance pro lometer, constructed in Institute of Optoelectronics, Military University of Technology, is an optoelectronic device based on the laser remote sensing technique, designed for use on unmanned aerial vehicles. The main task of the pro lometer is to analyze the re ectance signals for laser radiation from the multispectral range: 850, 905, and 1550 nm. On the basis of the analysis the pro lometer enables two fundamental functions: to de ne the shape of the ground and identify elements of terrain coverage, along with analysis of its physicochemical properties. The re ectance pro lometer is a system based on a modern concept of measurement, allowing to obtain unique information about tested objects, whose measurements or detection are not possible by means of thermal imaging systems and systems operating in the visible range. The received information is visualized in 3D format, which allows for more accurate, precise and comprehensive way of analyzed area model presentation.

A new hybrid terrain coverage method for underwater robotic exploration

It is well known that it is difficult to explore underwater terrains using an autonomous underwater vehicle due to the varieties and complexities of underwater terrain elements. Since conventional underwater terrain coverage techniques are usually based on the assumption that the underwater surface is planar, they generate an unnecessary exploration path especially on steep sloped surfaces of ocean basins. This paper proposes a new type of coverage technique, the hybrid terrain coverage framework (HTCF), which considers various surface conditions in three-dimensional environments and generates an efficient exploration path for all environments. The HTCF incorporates a planar terrain coverage algorithm, a spiral path terrain coverage algorithm, and a hybrid decision module to recognize and select the most suitable technique depending on the sloped surface variations. Simulation results show that the proposed HTCF is more efficient than the conventional terrain coverage algorithm in terms of the energy consumption of the underwater vehicle.

Micro air vehicle trajectory generation in pitch plane

For autonomous operations, unmanned micro air vehicles depend on novel trajectory generation schemes. Trajectories parallel to the ground are fairly well understood in surveillance and reconnaissance contexts. In addition, trajectories with altitude as one of the navigational parameters are also envisioned for aerial robots, urban terrain coverage, etc. In these missions, trajectory generation in pitch plane becomes an important problem. In this article, a linear algebraic procedure is applied to generate the pitch plane trajectories. That is, when a terrain (latitude, longitude and altitude) is sensed from a global positioning system, a trajectory to navigate the aircraft along the terrain is presented. By linear algebra, the state vector is spanned using a set of known vectors and some of their scalars (coefficients of spanning vectors) are solved in an optimization framework so that the state variables mapping the desired terrain are determined. After the trajectories are generated, normally smoothening is recommended so that the trajectories are continuous for autonomous control development. Thus, an extended Kalman filter is applied to smooth the trajectories. A nonlinear micro air vehicle model is considered to illustrate the trajectory generation and smoothening scheme.

The issue of visualization of point objects

This article discusses the problem of point object placement in 3D space for the purposes of visualization. We have to deal with this kind of task, when we have to place objects on a DTM (Digital Terrain Model), which most often is an irregular triangle grid. Unfortunately, the case when the point objects are placed on a horizontal surface is very rare. This is the reason why there is a need for a technology that allows us to prepare the data required to work on the digital terrain coverage model development. The approach presented is an attempt to solve this problem in a simplified yet sufficient model for this kind of elaborations.

Automated approach for rigorous light detection and ranging system calibration without preprocessing and strict terrain coverage requirements

Light detection and ranging (LiDAR) has demonstrated its capabilities as a prominent technique for the direct acquisition of high density and accurate topographic information. To achieve the potential accuracy of such systems, a rigorous system calibration should be performed. We introduce a novel rigorous LiDAR system calibration procedure where the system parameters are determined by minimizing the discrepancies among conjugate surface elements in overlapping strips and control data, if available. The method is automated and does not require specific features (e.g., planes or lines) in the covered area or preclassification of the point cloud. Suitable primitives, which do not involve preprocessing of the data, are implemented. The correspondence between conjugate primitives is determined using a robust matching procedure. A modification to the Gauss Markov model is introduced to keep the implementation of the calibration procedure simple while utilizing higher order primitives. Experimental results have demonstrated the effectiveness of the proposed method over different types of terrain coverage. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.7.076201]