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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Song, Zhihua Zhang, Han Liu, Fei Chen, Shitao Zhang, Fa Air Force Engn Univ Equipment Management & Unmanned Aerial Vehicle Co Xian Shaanxi Peoples R China Beijing Inst Technol Zhuhai Sch Zhu Hai Peoples R China

ISBN: (纸本)9781538657386

Real-time unmanned aerial vehicle coverage path planning is a challenging task. Although different patterns are outlined and optimized, the algorithm of patterns generation given the width of the sensor's footprint and the map with obstacles is rarely studied. A Cellular Automaton (CA) based algorithm is proposed and implemented for the spiral pattern and tested in maps with obstacles on the corner and off the edge. The CA is asynchronously updated and there is only one UAV-cell, whose time series indexes through the computing can be assigned as the coverage path. The coverage path planning algorithm proposed in the paper is fast because of the intrinsic discreteness of CA.

关键词： Unmanned Aerial Vehicle coverage path planning Cellular Automata

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Multi-Agent coverage path planning using a Swarm of Unmanned Aerial Vehicles 19

Multi-Agent Coverage Path Planning using a Swarm of Unmanned...

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19th IEEE-India-Council International Conference (INDICON)

作者： Chethan, Ragala Kar, Indrani Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati India

ISBN: (纸本)9781665473507

In recent years, rapid technological advancements in unmanned aerial vehicles (UAVs) have propelled their applications to a wide range of areas such as agriculture, mapping & surveying, surveillance, and many more. A swarm of UAVs can efficiently accomplish the goals rather than a single UAV due to its ability to cover a larger area. Multi-agent coverage path planning (CPP) is the process of determining efficient coverage paths for the swarm of UAVs to completely cover an area of interest. A cellular grid is formed using hexagonal decomposition to ensure complete coverage, with the centroid of each cell acting as a waypoint. The k-means clustering algorithm is implemented to divide the set of waypoints such that the workload is uniformly distributed among the UAVs. Multi-agent CPP is transformed into multiple single-agent CPP, thereby alleviating the exploratory complexity of multi-agent CPP. A mixed-integer linear programming based vehicle routing problem is formed to optimize the path of each UAV considering various constraints.

关键词： coverage path planning Vehicle Routing Problem

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An Offline-Online Strategy for Goal-Oriented coverage path planning using A Priori Information 14

An Offline-Online Strategy for Goal-Oriented Coverage Path P...

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14th IEEE International Conference on Industry Applications (INDUSCON)

作者： Khanam, Zeba Saha, Sangeet Ognibene, Dimitri McDonald-Maier, Klaus Ehsan, Shoaib Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Univ Milano Bicocca Dept Psychol Milan Italy

ISBN: (纸本)9781665441186

Recent times are witnessing the emergence of indoor sites with extenuating circumstances that place a strict time constraint on mobile robots to reach a target while covering a given area. This has created a global demand to equip mobile robots with the ability to autonomously plan a coverage path to reach the static target effectively and efficiently. The current approaches to achieve such tasks, however, are either time-consuming or human-operator dependent. To this end, an offline-online strategy is proposed to meet the speeding-up challenge by efficiently modelling the environment using a priori information. In the 'offline' stage of the strategy, the layout of the environment is segmented into a set of regions. The corners and dead-ends are identified based on the spatial mobility of the regions. The global path is then computed by deriving a graph-structured, road map using the segmented regions. In the 'online' stage, the global path is traversed by selecting frontiers which concurrently minimizes the covered area and time. In case the path is obstructed, a re-planning strategy is deployed. The proposed strategy is evaluated by various experiments against two baseline search approaches in three simulated environments. The results manifest a significant reduction in time to reach the goal and coverage area which caters to the strict time constraint for mobile robots.

关键词： A Priori Information coverage path planning Goal-Oriented Sensor based coverage Frontier Detection Static Target

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Complete coverage path planning Algorithm For Known 2d Environment 2

Complete Coverage Path Planning Algorithm For Known 2d Envir...

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2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Gajjar, Sumit Bhadani, Jaydeep Dutta, Pramit Rastogi, Naveen GEC Bhuj Dept Elect & Commun Engn Bhuj India Inst Plasma Res RHRTD Div Gandhinagar India

ISBN: (纸本)9781509037049

path-planning is an important primitive for autonomous mobile robots that lets robots find the optimal path between two points. Typical path-planning algorithms deal with finding an optimized path from start to end using a map of the environment and the robot to be aware of its location with respect to the map. Complete coverage path planning algorithm, on the other hand, is used in application where an autonomous mobile robot is required to visit all the points at least once in the given environment size with known obstacles. In this paper, a complete coverage path planning algorithm is developed and tested on a actual hardware. The algorithm is development for a known 2D environment with static obstacles with an optimization on minimal coverage time. The scaling of computational time with increasing map sizes and simulation results for different types of obstacle geometries are also presented. This kind of algorithm can be used for area inspection, cleaning robots, painter robot, land mine detectors, lawn mowers, agricultural field machines etc.

关键词： coverage path planning Obstacle Avoidance MATLAB Simulation Autonomous Guided Vehicle

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Static coverage path planning for UAVs with Conical Field of View When Monitoring Rectangular Ground Areas 23

Static Coverage Path Planning for UAVs with Conical Field of...

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23rd International Conference on System Theory, Control and Computing (ICSTCC)

作者： Marica, Vlad Curiac, Christian-Daniel Quentel, Paul Yves Marie Stangaciu, Cristina-Sorina Micea, Mihai-Victor Politehn Univ Comp & Informat Technol Dept Timisoara Romania Tech Univ Munich Dept Elect & Comp Engn Munich Germany

ISBN: (纸本)9781728106991

Finding a suitable UAV path to cover a given ground area is of paramount importance when accomplishing monitoring or mapping missions. In order to obtain an appropriate UAV route to inspect a rectangular ground area using a sensor with a conical field of view, this paper analysis three classic trajectories, namely scan, rectangular spiral and Hilbert paths. Using carefully chosen metrics we conclude that the scan path is the right solution to this static coverage planning problem.

关键词： unmanned aerial vehicle (UAV) quadcopter coverage path planning mapping

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Boustrophedon coverage path planning for UAV Aerial Surveys in Wind

Boustrophedon Coverage Path Planning for UAV Aerial Surveys ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Coombes, Matthew Chen, Wen-Hua Liu, Cunjia Loughborough Univ Technol Dept Automot & Aeronaut Engn Loughborough LE11 3TQ Leics England

ISBN: (纸本)9781509044948

In the quickly developing world of precision agriculture UAV remote sensing, there is a need for a greater understanding of winds effect on fixed wing aerial surveying, as this is missing from current literature. This paper presents a method to define and calculate flight times in a Boustrophedon aerial survey coverage path in wind, for a given convex polygon, at a given sweep angle. It is shown that there exists no easy way to define a sweep angle relative to the wind that minimises flight time. This method is validated by comparing the numerical simulated path and times with a number of surveys run in the high fidelity X-Plane simulator.

关键词： coverage path planning UAVs Trochoids

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Unit Circles Decomposition-based coverage path planning for UAV 34

Unit Circles Decomposition-based Coverage Path Planning for ...

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34th Chinese Control and Decision Conference (CCDC)

作者： Qian Hanyu Li Huang Xiao Bing Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Aerosp Automat Control Inst Beijing 100070 Peoples R China

ISBN: (纸本)9781665478960

In this paper, a coverage path planning (CPP) scheme for an unmanned aerial vehicle (UAV) in coverage observation is proposed. The algorithms begin by decomposing the task region into several unit circles. The positions of the unit circles are optimized by the improved Particle-Glowworm Swarm Optimization algorithm based on the area size of the unit circle and task region. Then the connection sequence of all unit circles is solved by the Traveling Salesman Problem (TSP) algorithm according to the shortest path principle. Further, the cubic Bezier curve is used to fit the centers of all unit circles to get a smooth coverage path. Finally, two curvature adjustment algorithms are proposed to optimize curvature. The main contribution of the paper is to present a scheme that generates a smooth coverage path while providing coverage optimization and path length optimization. Computer simulations validate the effectiveness of the algorithms.

关键词： coverage path planning UAV coverage Observation Unit circles Cubic Bezier curve

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Balanced Multi-Region coverage path planning for Unmanned Aerial Vehicles

Balanced Multi-Region Coverage Path Planning for Unmanned Ae...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Yu, Xiaoxiao Jin, Songchang Shi, Dianxi Li, Lin Kang, Ying Zou, Junbo Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Innovat Inst Def Technol Beijing Peoples R China Tianjin Artificial Intelligence Innovat Ctr Tianjin Peoples R China Peoples Liberat Army China 31401 Changchun Peoples R China

ISBN: (纸本)9781728185262

Nowadays, Unmanned Aerial Vehicles (UAVs) are playing increasingly important roles in agriculture, rescuing and surveillance due to their small size, low cost and strong adaptability. coverage path planning (CPP) is a fundamental problem for UAV applications, which means to find a path covering all the targets or regions of interest. Researches on CPP in a single region have been studied for decades, but rare to be devoted to covering multiple scattered regions of multiple UAVs. This paper proposes an attempt to solve this problem in a short time and take the task balance of multiple UAVs into account meanwhile. This work constructs a model for multiple UAVs to cover scattered regions firstly. In view of the high computational complexity of the precise solution, we keep innovating a heuristic measure based on the model to make the solving process feasible. To settle the problem of imbalanced time consumption caused by super regions, we improve the previously built model furthermore. A series of experiments validated that the presented approaches exhibit the effectiveness and balance of time consumption in diverse scenarios.

关键词： multi-region coverage path planning balance

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Hierarchical Oriented Genetic Algorithms for coverage path planning of Multi-robot Teams with Load Balancing 09

Hierarchical Oriented Genetic Algorithms for Coverage Path P...

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World Summit on Genetic and Evolutionary Computation (GEC 09)

作者： Ozkan, Metin Yazici, Ahmet Kapanoglu, Muzaffer Parlaktuna, Osman Eskisehir Osmangazi Univ Dept Comp Eng TR-26480 Batimeselik Eskisehir Turkey

ISBN: (纸本)9781605583266

Multi-robot coverage path planning problems require every point in a given area to be covered by at least one member of the robot team using their sensors. For a time-efficient coverage, the environment needs to be partitioned among robots in a balanced manner. So the problem can be modeled as task assignment problem with load balancing. In this study, we propose two oriented genetic algorithms working in a hierarchical manner to deal with this problem. In the First phase, a previously proposed oriented genetic algorithm is used to find a single route with minimum repeated coverage. In the following phase, a directed genetic algorithm is used to partition the route among robots considering load balancing. The algorithm is coded in C++ and simulations are conducted using P3-DX mobile robots, in the MobileSim environment. The hierarchical oriented genetic algorithm (HOGA) is also compared to the multi-robot spanning tree coverage (STC) approach in terms of load balancing. The comparison indicates competitive results over multi-robot STC.

关键词： Multi-robot coverage path planning Genetic algorithm Load balancing

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coverage path planning and Point-of-Interest Detection Using Autonomous Drone Swarms

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SENSORS 2022年第19期22卷 7551页

作者： Bezas, Konstantinos Tsoumanis, Georgios Angelis, Constantinos T. Oikonomou, Konstantinos Univ Ioannina Dept Informat & Telecommun Campus Arta Arta 47100 Greece Ionian Univ Dept Informat Corfu 49100 Greece

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness;thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. coverage path planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm's effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here-parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.

关键词： drones coverage path planning point-of-interest detection

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