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Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reconfigurable robots are deployed for applications demanding area coverage, such as cleaning and inspections. Reconfiguration per context, considering beyond a small set of predefined shapes, is crucial for area coverage performance. However, the existing area coverage methods of reconfigurable robots are not always effective and require improvements for ascertaining the intended goal. Therefore, this article proposes a novel coverage strategy based on internal rehearsals to improve the area coverage performance of a reconfigurable robot. In this regard, a reconfigurable robot is embodied with the cognitive ability to predict the outcomes of its actions before executing them. A genetic algorithm uses the results of the internal rehearsals to determine a set of the robot's coverage parameters, including positioning, heading, and reconfiguration, to maximize coverage in an obstacle cluster encountered by the robot. The experimental results confirm that the proposed method can significantly improve the area coverage performance of a reconfigurable robot. [ABSTRACT FROM AUTHOR]

Swarm robotics show collective behavior in order to work in multi agent scenario, where spatial area coverage is an emergent area of research. This work introduces a hybrid technique for optimized spatial area coverage. A combination of Dragonfly Algorithm (DA) and chaotic mapping is proposed and the entire study is carried out in two phases. In the first phase, DA's parameters are measured on the basis of percentage of area covered, entropy and number of pop-up threats detected. In the second phase, Chaotic distribution is applied in swarm algorithms (i.e. DA along with Bat Algorithm and Accelerated Particle Swarm Optimization) to implement hybridized models. Chaotic Dragonfly Algorithm along with Chaotic Bat Algorithm and Chaotic Accelerated Particle Swarm Optimization) is implemented. To evaluate the performance of hybridized models, a comprehensive comparison is drawn among the Levy and Chaotic versions of all three swarm algorithms. It is concluded that the proposed DA outperforms the rest. DA not only showed better results for the mentioned metrics, but also displayed uniform behavior over multiple runs. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in randomly deployed sensor networks is enhancing the network lifetime while providing full area coverage. The problem is more challenging when location information is not available. Scheduling the activities of sensor nodes in a way that each point of the area of interest is covered by at least one sensor node is a promising way when a smaller set of sensor nodes is scheduled autonomously. The autonomous sleep scheduling of sensor nodes can be efficiently achieved based on the topological properties of the sensor network in a distributed fashion. In this paper, we address the problem of autonomously scheduling of sensor nodes to provide full area coverage in wireless sensor networks, even when location information is unavailable. The goal is to prolong the network lifetime. The proposed method is based on homology. The idea is autonomous selection of the minimum number of active sensors with the highest level of energy based on the properties of the simplicial complex of the network. We formulate this problem as an integer programming problem. Then, we propose a distributed algorithm, which does not require the knowledge of the location of nodes or distance between them. Finally, we provide simulation results demonstrating the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles. [ABSTRACT FROM AUTHOR]

Drone area coverage primarily involves using unmanned aerial vehicles (UAVs) for extensive monitoring, surveying, communication, and other tasks over specific regions. The significance and value of this technology are multifaceted. Firstly, UAVs can rapidly and efficiently reach remote or inaccessible areas to perform tasks such as terrain mapping, disaster monitoring, or search and rescue, significantly enhancing response speed and execution efficiency. Secondly, drone area coverage in agricultural monitoring, forestry conservation, and urban planning offers high-precision data support, aiding scientists and decision-makers in making more accurate judgments and decisions. Additionally, drones can serve as temporary communication base stations in areas with poor communication, ensuring the transfer of crucial information. Drone area coverage technology is vital in improving work efficiency, reducing costs, and strengthening decision support. This paper aims to solve the optimization problem of multi-UAV area coverage flight path planning to enhance system efficiency and task execution capability. For multi-center optimization problems, a region decomposition method based on the Voronoi graph is designed, transforming the multi-UAV area coverage issue into the single-UAV area coverage problem, greatly simplifying the complexity and computational process. For the single-UAV area coverage problem and its corresponding area, this paper contrives a convolutional neural network with the channel and spatial attention mechanism (CSAM) to enhance feature fusion capability, enabling the model to focus on core features for solving single-UAV path selection and ultimately generating the optimal path. Simulation results demonstrate that the proposed method achieves excellent performance.

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%. [ABSTRACT FROM AUTHOR]

Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies. [ABSTRACT FROM AUTHOR]

Abstract Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

Reconfigurable robots have the potential to perform complex tasks by adapting their morphology to different environments. However, designing optimal morphologies for these robots is challenging due to the large design space and the complex interactions between the robot and the environment. An in-house robot named S m o r p h i , having four holonomic mobile units connected with three hinge joints, is designed to maximize area coverage with its shape-changing features using transformation design principles (TDP). The reinforcement learning (RL) approach is used to identify the optimal morphologies out of a vast combination of hinge angles for a given task by maximizing a reward signal that reflects the robot's performance. The proposed approach involves three steps: (i) Modeling the Smorphi design space with a Markov decision process (MDP) for sequential decision-making; (ii) a footprint-based complete coverage path planner to compute coverage and path length metrics for various Smorphi morphologies; and (iii) pptimizing policies through proximal policy optimization (PPO) and asynchronous advantage actor–critic (A3C) reinforcement learning techniques, resulting in the generation of energy-efficient, optimal Smorphi robot configurations by maximizing rewards. The proposed approach is applied and validated using two different environment maps, and the results are also compared with the suboptimal random shapes along with the Pareto front solutions using NSGA-II. The study contributes to the field of reconfigurable robots by providing a systematic approach for generating optimal morphologies that can improve the performance of reconfigurable robots in a variety of tasks. [ABSTRACT FROM AUTHOR]

Directional sensor nodes deployment is indispensable to a large number of applications including Internet of Things applications. Nowadays, with the recent advances in robotic technology, directional sensor nodes mounted on mobile robots can move toward the appropriate locations. Considering the probabilistic sensing model along with the mobility and motility of directional sensor nodes, area coverage in such a network is more complicated than in a static sensor network. In this paper, we investigate the problem of self-deployment and working direction adjustment in directional sensor networks in order to maximize the covered area. Considering the tradeoff between energy consumption and coverage quality, we formulate this problem as a finite strategic game. Then, we present a distributed payoff-based learning algorithm to achieve Nash equilibrium. The simulation results demonstrate the performance of the proposed algorithm and its superiority over previous approaches in terms of increasing the area coverage. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) equipped with visual sensors are extensively used in area coverage applications. As a UAV would only cover a fraction of the region of interest, the entire region needs to be covered by several UAVs where each UAV accomplishes its own tasks. For the covering of the target region, a working method consisting of three levels has been developed. The initial step employs the Voronoi partition technique to create a number of convex sub-polygonal areas inside the target area. In the second level, each sub-polygonal area is partitioned to provide a near-optimal collection of waypoints. At the third and final level, we find a path that visits each of the waypoints without colliding with anything and is as short as feasible. Collision due to both static and dynamic obstacles is also considered for avoidance. The first and second-level partitioning processes are carried out offline, whereas path planning is handled in real-time. Traditional methods like Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), and Cuckoo Optimisation Algorithm (COA) are used to evaluate the proposed work. The evaluated result of the proposed work is compared with the existing work on area coverage in terms of the percentage of inside and outside area coverage. Also, the performance of the proposed dynamic path planning method is compared with TSP and the Improved Follow the Gap Method (FGM-I). The outcome demonstrates that the proposed work is far more effective than the existing result and is suitable for the application in issue, which involves area coverage with the presence of obstacles. [ABSTRACT FROM AUTHOR]

This paper systematically studies the cooperative area coverage and target tracking problem of multiple-unmanned aerial vehicles (multi-UAVs). The problem is solved by decomposing into three sub-problems: information fusion, task assignment, and multi-UAV behavior decision-making. Specifically, in the information fusion process, we use the maximum consistency protocol to update the joint estimation states of multi-targets (JESMT) and the area detection information. The area detection information is represented by the equivalent visiting time map (EVTM), which is built based on the detection probability and the actual visiting time of the area. Then, we model the task assignment problem of multi-UAV searching and tracking multi-targets as a network flow model with upper and lower flow bounds. An algorithm named task assignment minimum-cost maximum-flow (TAMM) is proposed. Cooperative behavior decision-making uses Fisher information as the mission reward to obtain the optimal tracking action of the UAV. Furthermore, a coverage behavior decision-making algorithm based on the anti-flocking method is designed for those UAVs assigned the coverage task. Finally, a distributed multi-UAV cooperative area coverage and target tracking algorithm is designed, which integrates information fusion, task assignment, and behavioral decision-making. Numerical and hardware-in-the-loop simulation results show that the proposed method can achieve persistent area coverage and cooperative target tracking. [ABSTRACT FROM AUTHOR]

The literature approaches, devoted to sensor improve network coverage, are deterministic in terms of deployment environment and node configuration parameters. Nevertheless, this type of approaches has not proven to be very successful in uncertain deployment environments. This paper aims to deal with this issue using theories of uncertainty. We consider deployment environment's imperfections and the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in wireless sensor networks (WSNs) is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to Schedule the Active/Passive State of Sensor nodes Strategy (FP-3SNS). This model helps to plan the scheduling of node states (Active/Passive) based on possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated our model (FP-3SNS) with (a) a running example (that shows the best choice of the active node with a probability of 0.81215); (b) a statistical evaluation (calculation of the confidence interface), where the average coverage reliability at 95% of FP-3SNS use is between (92.94, 96.27); and (c) a comparison with maximum sensing coverage region problem (MSCR), coverage maximization with sleep scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP), Probing Environment and Adaptive Sleeping with Location Information Protocol (PEAS-LI), and Variable Length Particle Swarm Optimization Algorithm with a Weighted Sum Fitness Function (WS-VLPSO). The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem and the proposed model maintained a coverage between 99.99 and 90.00% for a significant period of time. [ABSTRACT FROM AUTHOR]

Multi-robot coverage systems are widely used in operations such as environmental monitoring, disaster rescue, and pollution prevention. This study considers inherent positioning errors in positioning systems and ground mobile robots with limited communication distance and poor quality in practice. A centroidal Voronoi tessellation algorithm-based formation control technology for multi-robots is optimized. First, by constructing buffered Voronoi cells (BUVCs) for each robot, the collision avoidance ability of the multi-robot formation movement is improved. Next, the formation control problem of multi-robots in a limited communication range and non-convex environment is realized via discrete Voronoi partitioning, a communication distance constraint, and an obstacle avoidance strategy. Simulation and experiment results demonstrate that the proposed method can effectively solve the position generation problem of multi-robot coverage systems in a non-convex environment with actual sizes of the robots and positioning system errors and can further improve the collision avoidance performance of robots and the robustness of BUVC algorithms. [ABSTRACT FROM AUTHOR]

Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed randomly over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. The reduction of the number of active VSNs decreases energy consumption but at the same time reduces the area coverage and connectivity. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming-based approach (PA_1). But PA_1 is not tractable for large instances as the problem is NP-Hard. Hence, a heuristic approach (PA_2) based on an advanced genetic algorithm is also proposed in the present work for obtaining a near-optimal solution. Simulation studies are carried out to compare the performance of PA_1 and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among the three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1 and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates a suboptimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment. [ABSTRACT FROM AUTHOR]

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. • Construction of a Random Bit Generator with the use of two Logistic Maps. • The realization of the chaotic random bit generator is based on a microcontroller. • An ultrasonic distance sensor for the detection of the boundaries has been used. • Experimental study of the workspace's coverage. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presented a mathematical description of an arbitrary shape area, and after analyzing the characteristics of the near-polar orbital co-ground trajectory constellation that satisfies the revisit interval constraint, an optimization model of the orbital parameters design of reference satellites in regional coverage constellations was constructed. Then based on the designed area covering the constellation of common ground trajectories, a simple and effective two-pulse maneuvering scheme was adopted for the needs of sudden tasks, and an existing satellite scheduling optimization model using the golden section method to constrain the boundary of substellar point coverage was constructed. Finally, the optimal design of regional coverage constellations and existing satellite dispatches was carried out for the sea areas with the most frequent tropical cyclone activities in the northwest Pacific Ocean, and the simulation results verified the effectiveness of the proposed optimization model and have certain practical use value.

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal "morphology" is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., "n". The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot's optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of S m o r p h i using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method based on Fuzzy Logic Systems (FLSs) optimized to maximize the area coverage of a tiling robot by reconfiguring per an obstacle considering the "Infi" concept. The consideration of an infinite number of reconfigurable shapes by a tiling robot is defined as the "Infi" concept. The major advantage of the proposed method over state-of-the-art "Infi" methods is the ability to tune based on cost measurements without demanding an explicit set of training data. The optimization techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), were used for tuning the FLSs. The FLSs tuned using these three techniques were compared with state of the art by considering hTetro through simulations. According to the statistical outcomes, the proposed method can significantly improve the area coverage compared to the conventional methods while providing performance and behavior similar to the existing "Infi" methods that demand training data. Thus, the work proposed in this paper would be of great interest for the development of a reconfigurable floor cleaning robot since the complete area coverage is a foremost feature expected from a floor cleaning robot. [ABSTRACT FROM AUTHOR]

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%.

Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage. [ABSTRACT FROM AUTHOR]

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.

Abstract The p-center location problem in an area is an important yet very difficult problem in location science. The objective is to determine the location of p hubs within a service area so that the distance from any point in the area to its nearest hub is as small as possible. While effective heuristic methods exist for finding good feasible solutions, research work that probes the lower bound of the problem’s objective value is still limited. This paper presents an iterative solution framework along with two optimization-based heuristics for computing and improving the lower bound, which is at the core of the problem’s difficulty. One method obtains the lower bound via solving the discrete version of the Euclidean p-center problem, and the other via solving a relatively easier clustering problem. Both methods have been validated in various test cases, and their performances can serve as a benchmark for future methodological improvements.

Reconfigurable robots have the potential to perform complex tasks by adapting their morphology to different environments. However, designing optimal morphologies for these robots is challenging due to the large design space and the complex interactions between the robot and the environment. An in-house robot named Smorphi, having four holonomic mobile units connected with three hinge joints, is designed to maximize area coverage with its shape-changing features using transformation design principles (TDP). The reinforcement learning (RL) approach is used to identify the optimal morphologies out of a vast combination of hinge angles for a given task by maximizing a reward signal that reflects the robot’s performance. The proposed approach involves three steps: (i) Modeling the Smorphi design space with a Markov decision process (MDP) for sequential decision-making; (ii) a footprint-based complete coverage path planner to compute coverage and path length metrics for various Smorphi morphologies; and (iii) pptimizing policies through proximal policy optimization (PPO) and asynchronous advantage actor–critic (A3C) reinforcement learning techniques, resulting in the generation of energy-efficient, optimal Smorphi robot configurations by maximizing rewards. The proposed approach is applied and validated using two different environment maps, and the results are also compared with the suboptimal random shapes along with the Pareto front solutions using NSGA-II. The study contributes to the field of reconfigurable robots by providing a systematic approach for generating optimal morphologies that can improve the performance of reconfigurable robots in a variety of tasks.

Multi-robot coverage systems are widely used in operations such as environmental monitoring, disaster rescue, and pollution prevention. This study considers inherent positioning errors in positioning systems and ground mobile robots with limited communication distance and poor quality in practice. A centroidal Voronoi tessellation algorithm-based formation control technology for multi-robots is optimized. First, by constructing buffered Voronoi cells (BUVCs) for each robot, the collision avoidance ability of the multi-robot formation movement is improved. Next, the formation control problem of multi-robots in a limited communication range and non-convex environment is realized via discrete Voronoi partitioning, a communication distance constraint, and an obstacle avoidance strategy. Simulation and experiment results demonstrate that the proposed method can effectively solve the position generation problem of multi-robot coverage systems in a non-convex environment with actual sizes of the robots and positioning system errors and can further improve the collision avoidance performance of robots and the robustness of BUVC algorithms.

Coverage path planning technologies is of great significance for improving the efficiency and accuracy of UAV search. This paper proposes a area coverage path planning(CPP) algorithm for the concave polygon area, which aims to enable UAV to complete the coverage search of the specified area more efficiently. Firstly, the algorithm flow is given by dealing with concave points in concave polygon area and dividing the area by convex decomposition. Secondly, the traverse sequence of the subarea is given based on the minimum redundant path, and the covering traverse without omission of the area by UAV are discribed in detail. Finally, the correctness and effectiveness of the proposed algorithm are proved by simulation experiments.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)