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coverage path planning with Adaptive Hyperbolic Grid for Step-Stare Imaging System

DRONES 2024年第6期8卷 242-242页

作者： Zhao, Jiaxin Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Changchun Changguang Insight Vis Optoelect Technol Changchun 130102 Peoples R China

Step-stare imaging systems are widely used in aerospace optical remote sensing. In order to achieve fast scanning of the target region, efficient coverage path planning (CPP) is a key challenge. However, traditional CPP methods are mostly designed for fixed cameras and disregard the irregular shape of the sensor's projection caused by the step-stare rotational motion. To address this problem, this paper proposes an efficient, seamless CPP method with an adaptive hyperbolic grid. First, we convert the coverage problem in Euclidean space to a tiling problem in spherical space. A spherical approximate tiling method based on a zonal isosceles trapezoid is developed to construct a seamless hyperbolic grid. Then, we present a dual-caliper optimization algorithm to further compress the grid and improve the coverage efficiency. Finally, both boustrophedon and branch-and-bound approaches are utilized to generate rotation paths for different scanning scenarios. Experiments were conducted on a custom dataset consisting of 800 diverse geometric regions (including 2 geometry types and 40 samples for 10 groups). The proposed method demonstrates comparable performance of closed-form path length relative to that of a heuristic optimization method while significantly improving real-time capabilities by a minimum factor of 2464. Furthermore, in comparison to traditional rule-based methods, our approach has been shown to reduce the rotational path length by at least 27.29% and 16.71% in circle and convex polygon groups, respectively, indicating a significant improvement in planning efficiency.

关键词： coverage path planning hyperbolic grid step-stare imaging spherical tiling dual-caliper optimization

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Linking Field Decomposition and coverage path planning: A Coevolution Approach

Linking Field Decomposition and Coverage Path Planning: A Co...

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IEEE Conference on Artificial Intelligence (IEEE CAI)

作者： Bostelmann-Arp, Lukas Steup, Christoph Mostaghim, Sanaz Otto von Gliericke Univ Magdeburg Germany

ISBN: (纸本)9798350339840

coverage path planning (CPP) for complex fields in precision farming is often solved by splitting the problem: Finding a good field decomposition and optimizing the paths for each individual field. While various techniques have been proposed for both steps, they are performed sequentially, ignoring the knowledge gain from performing CPP for field decomposition. To overcome this limitation, an approach to link theses two steps and optimize them in parallel is proposed. A proof of concept is presented using coevolution to demonstrate the feasibility.

关键词： precision farming coevolution field decomposition coverage path planning

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Underactuated Unmanned Surface Vessel coverage path planning for Marine Pier Inspection

Underactuated Unmanned Surface Vessel Coverage Path Planning...

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OCEANS Conference

作者： Ng, Yu Hin Hou, Yujie Yuan, Yixing Chang, Ching Wei Hong Kong Ctr Construct Robot Hong Kong Peoples R China

ISBN: (纸本)9798350362077

Inspection for maritime infrastructures like piers is essential since they suffer from prolonged moisture penetration and corrosion of seawater especially their base part near water surface. This may threaten their structural integrity and pose risk to the public users. However, limited space and densely-packed pilings underneath the infrastructure raise difficulties in both manual inspection and data collection using unmanned aerial vehicles. Unmanned surface vessel (USV) is thus a more suitable mobile platform for data acquisition in this scenario. Safety issues arise due to the underactuation of conventional USV upon the presence of densely-packed obstacles. We thus propose a novel coverage path planning method to make the USV capable of scanning the inspection region with the onboard camera safely and efficiently with a simple hardware upgrade. Simulation experiments are performed to validate the feasibility and effectiveness of the proposed method. The advantages of this approach in other potential applications are also discussed.

关键词： unmanned surface vessel (USV) coverage path planning inspection

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Optimal Online coverage path planning with Energy Constraints 18

Optimal Online Coverage Path Planning with Energy Constraint...

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18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)

作者： Sharma, Gokarna Dutta, Ayan Kim, Jong-Hoon Kent State Univ Kent OH 44242 USA Univ North Florida Jacksonville FL USA

ISBN: (纸本)9781450363099

We consider the problem of covering an unknown polygonal environment possibly containing obstacles using a robot of square size L x L. The environment is structured as a grid with resolution proportional to the robot size L x L, imposed on it. The robot has a limited energy budget it has to visit a charging station before it runs out of its energy;there is a single charging station in the environment. In a single time step, the robot can move from one grid cell to one of its four adjacent cells. The energy budget B allows the robot to travel at most B distance, i.e., B grid cells. The objective of the robot is to minimize both total distance traveled to cover the environment (visit each cell of the environment not occupied by obstacles) and the number of visits to the charging station. In this paper, we present the first online coverage path planning algorithm that achieves 0(log(B/L))-approximation for both objectives. Our bound is optimal since there exists a lower bound of Q(log(B/L)) for this problem for both objectives. Simulation results show the efficiency of our approach.

关键词： Robotics coverage path planning Energy Constraint Unknown Environment Approximation

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A Reinforcement Learning Based Online coverage path planning Algorithm

A Reinforcement Learning Based Online Coverage Path Planning...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Carvalho, Jose Pedro Pedro Aguiar, A. Univ Porto Fac Engn SYSTEC ARISE Porto Portugal Univ Porto Fac Engn ECE Dept Porto Portugal

ISBN: (纸本)9798350301212

coverage path planning (CPP) is a common task in robotics that consists in computing collision-free paths that pass through all the specified points from an area of interest. This task is known to be NP-Hard, and increasingly complex when the agent relies exclusively on sensor information. Reinforcement Learning methods appear as an interesting solution to deal with the complexity of this problem and obtain efficient solutions. This paper presents an online CPP algorithm based on Tabular Temporal Difference Learning methods, for a generic robotic platform with a ranging sensor. The problem is formulated as a Partially Observed Markov Decision Process and an RL scheme that includes a modified policy with a heuristic method is proposed. The presented approach provides a way to mix the concepts of classical algorithms with RL, enabling the tabular algorithm to overcome the shortcomings of the inherent large state space of CPP, and accelerated the training process by optimizing and reducing the policy space. The proposed algorithm is tested and its performance is compared in simulation using different Temporal Difference Learning methods, showing that it can efficiently complete the task with no prior information, with different map sizes, starting positions, and a random number of obstacles.

关键词： coverage path planning Reinforcement Learning Temporal Differences Learning Q-Learning POMDP

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Multi - Objective Seed Curve Optimization for coverage path planning in Precision Farming 23

Multi - Objective Seed Curve Optimization for Coverage Path ...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Bostelmann-Arp, Lukas Steup, Christoph Mostaghim, Sanaz Otto von Guericke Univ Magdeburg Germany

ISBN: (纸本)9798400701191

coverage path planning is one of the main challenges in precision farming. Here the goal is to compute a continuous optimal working path by combining individual tracks such that the entire field can be cultivated in the most efficient way. Various 2D and 3D approaches have already been presented in the literature. However, these are lacking in two major aspects: In each case, a seed curve is determined and offsetted to generate the individual tracks, but the seed curve is usually selected from a predefined set of alternatives. Additionally, the utilized objectives do not consider the efficiency of the tools used to work on the field, which typically depends on the path angle and the field's inclination. In this paper, we propose a scheme to cope with both aspects through an evolutionary multiobjective optimization approach. Three objectives are used, namely coverage, energy based on anisotropic friction and gravity, and precession, which describes the angle between the path direction and the gradient of the terrain.

关键词： agriculture coverage path planning evolutionary multi-objective optimization

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Asynchronous Reliability-Aware Multi-UAV coverage path planning

Asynchronous Reliability-Aware Multi-UAV Coverage Path Plann...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Li, Mickey Richards, Arthur Sooriyabandara, Mahesh Univ Bristol Bristol Robot Lab Bristol Avon England Toshiba Res Europe Ltd Bristol Res & Innovat Lab Bristol Avon England

ISBN: (纸本)9781728190778

Graceful degradation is a potential advantage of Multi-Robot Systems over Single-Robot Systems. In aerial robotics applications, such as infrastructure inspection, this trait is desirable as it would improve mission reliability despite the use of failure-prone low-cost drones. The Reliability-Aware Multi-Agent coverage path planning (RA-MCPP) problem finds path plans for each robot to maximise the probability of mission completion by a given deadline. This paper proposes a path planner for RA-MCPP formulated in continuous time, enabling more complex realistic environments to be considered. The proposed method (i) extends a reliability evaluation framework to evaluate the Probability of Completion metric on asynchronous strategies on non-unit lattice graph environments, and (ii) introduces a greedy-genetic meta-heuristic optimisation method as a scalable and accurate RA-MCPP solver. This method is shown to provide plans with higher reliability when compared with existing approaches in three real inspection scenarios.

关键词： Multi-Robot Systems coverage path planning Reliability Analysis

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Fixed Wing UAV Survey coverage path planning in Wind for Improving Existing Ground Control Station Software 37

Fixed Wing UAV Survey Coverage Path Planning in Wind for Imp...

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37th Chinese Control Conference (CCC)

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

ISBN: (纸本)9789881563958

UAV technology is becoming increasingly mature and cheap. This recent cost reduction and performance improvement means that these systems have become reliable and cheap enough to be viable for mass use by farmers and operators for precision agriculture activities;such as disease identification, yield estimation and plant nitrogen monitoring. A critical stage of this is the planning of the flight path which ensures complete image coverage of the region of interest. If the field is a simple convex shape, then generating an optimal coverage path is hugely simplified by using a hack and forth Boustrophedon path. However, most fields have complex polygonal shapes, where planning the coverage path manually is nontrivial, as operators may not have the correct skills and experience. This is why in this paper, we outline an algorithm to improve the performance of survey path generation on complex ROI for mission planning software. The tools implemented in this paper take into account environmental factors and aircraft dynamics. By decomposing these complex survey regions into many smaller arrangements of manageable convex polygon survey regions, Boustrophedon paths can be used to cover them. By using a survey model for calculation of flight time in a wind field, it is used to optimise the decomposition in order to lower flight time. The fastest survey path is used to generate waypoints files to be used with a number of popular mission planning software including: DM PC Ground Control, Mission Planner, QGroundControl.

关键词： Aerial Surveying Remote Sensing Polygon Decomposition coverage path planning Boustrophedon paths mission planning and ground control station

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Energy-aware coverage path planning of UAVs 9

Energy-aware Coverage Path Planning of UAVs

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9th IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Di Franco, Carmelo Buttazzo, Giorgio Scuola Super Sant Anna Pisa Italy

ISBN: (纸本)9781467369916

coverage path planning is the operation of finding a path that covers all the points of a specific area. Thanks to the recent advances of hardware technology, Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. However, most of the research focused on finding the optimal path taking only geometrical constraints into account, without considering the peculiar features of the robot, like available energy, weight, maximum speed, sensor resolution, etc. This paper proposes an energy-aware path planning algorithm that minimizes energy consumption while satisfying a set of other requirements, such as coverage and resolution. The algorithm is based on an energy model derived from real measurements. Finally, the proposed approach is validated through a set of experiments.

关键词： Energy consumption coverage path planning optimal speed uav path planning optimal velocity energy consumption Unmanned Aerial Vehicle coverage ENERGY MODELS optimal path

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A Multi-robot Balanced coverage path planning Strategy for Patrol Missions 21

A Multi-robot Balanced Coverage Path Planning Strategy for P...

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21st International Conference on Control, Automation and Systems (ICCAS)

作者： Lee, SeungHwan Kumoh Natl Inst Technol Sch Elect Engn Gumi 39177 South Korea

ISBN: (纸本)9788993215212

This paper presents a multi-robot balanced coverage path planning (MCPP) strategy for patrol missions. In the entire path-based multi-robot path assignment problem, conventional studies allow path redundancy and divide the path itself according to the number of robots. However, more collisions between robots can happen due to path redundancy. Therefore, a strategy of equally assigning a path to each robot is proposed without path redundancy. First, given a map, a graph for robot movement is constructed. A problem similar to the traveling salesman problem is solved in the graph. As a result, an entire path is generated. Subsequently, unique paths are extracted from the whole path and combined according to the number of robots. Since the combined paths are not evenly divided, the paths are compensated in a way that minimizes the maximum length of ones. Simulations were performed by varying the number of robots. Through performance comparison, it showed that the paths were divided well using the proposed approach.

关键词： coverage path planning Patrol Mission Genetic Algorithm Multi-robot System

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