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coverage path planning Using Actor-Critic Deep Reinforcement Learning

SENSORS 2025年第5期25卷 1592-1592页

作者： Garrido-Castaneda, Sergio Isahi Vasquez, Juan Irving Antonio-Cruz, Mayra Inst Politecn Nacl IPN Ctr Innovac & Desarrollo Tecnol Comp CIDETEC Mexico City 07700 Mexico Inst Politecn Nacl IPN Unidad Profes Interdisciplinaria Ingn & Ciencias S Secc Estudios Posgrad Invest Mexico City 08400 Mexico

One of the main capabilities a mobile robot must demonstrate is the ability to explore its environment. The core challenge in exploration lies in planning the route to fully cover the environment. Despite recent advances, this problem remains unsolved. This study proposes an approach to address the coverage path planning problem, where the mobile robot is tasked with exploring and completely covering a terrain using a deep reinforcement learning framework. The environment is divided into cells, with obstacles designated as prohibited areas. The robot is trained using two state-of-the-art reinforcement learning algorithms based on actor-critic methods: Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO). By defining a set of observations, states, and a reward function tailored to characteristics of the environment and the desired behavior of the robot, the training process is conducted, resulting in optimized policies for each algorithm. Then, these policies are evaluated to determine the most effective approach to accomplish the proposed task. Our findings demonstrate that actor-critic methods can produce policies capable of guiding a robot to efficiently explore and cover new environments.

关键词： coverage path planning deep reinforcement learning proximal policy optimization advantage actor-critic

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A UAV coverage path planning Method Based on a Diameter-Height Model for Mountainous Terrain

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1988-1988页

作者： Zhang, Nan Yue, Linkai Zhang, Qiang Gao, Chaojun Zhang, Bingbing Wang, Yonghuan Zhengzhou Univ Sch Phys Lab Zhongyuan Light Zhengzhou 450001 Peoples R China Zhengzhou Univ Lab Zhongyuan Light Zhengzhou 450001 Peoples R China

Most unmanned aerial vehicles (UAVs) nowadays engage in coverage missions using coverage paths with simple patterns that are limited in two-dimensional space, ignoring the elevation features of mountainous terrain intentionally, resulting in losing details and increasing the coverage path density and the UAV energy consumption. To address these problems, a coverage path planning method based on the DH (Diameter-Height) model for mountainous terrain is proposed. Firstly, a DH model is proposed for simplifying the 3D map construction of mountainous terrain. Secondly, in virtue of the DH model, the 3D map is partitioned into sub-regions. The equal-interval spiral ascent path and parallel circular coverage path are generated for covering the mountainous terrain with conformal coverage effects. A coverage path connection method with trajectory minimization and obstacle avoidance is proposed in the final path connection stage. The simulation experiments have demonstrated the feasibility and effectiveness of the proposed method and verified that the generated coverage path has the advantages of conformal coverage effects, trajectory minimization, and obstacle avoidance. The comparative experiment has also demonstrated the advantages of the proposed method in terms of UAV energy savings.

关键词： path planning coverage path planning conformal coverage obstacle avoidance energy saving UAV

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An algorithm with exact bounds for coverage path planning in UAV-based search and rescue under windy conditions

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Kazemdehbashi, Sina Liu, Yanchao Wayne State Univ Dept Ind & Syst Engn 4815 4th St Detroit MI 48201 USA

Unmanned aerial vehicles (UAVs) are increasingly utilized in global search and rescue efforts, enhancing operational efficiency. In these missions, a coordinated swarm of UAVs is deployed to efficiently cover expansive areas by capturing and analyzing aerial imagery and footage. Rapid coverage is paramount in these scenarios, as swift discovery can mean the difference between life and death for those in peril. This paper focuses on planning the flight paths for multiple UAVs in windy conditions to efficiently cover rectangular search areas in minimal time. We address this challenge by dividing the search area into a grid network and formulating it as a mixed-integer program (MIP). We derive a precise lower bound for the objective function and develop a fast algorithm with a proven capability of finding either the optimal solution or a near-optimal solution with a constant absolute gap to optimality. Notably, as the problem complexity increases, our solution exhibits a diminishing relative optimality gap while maintaining negligible computational costs compared to the MIP approach. The fast execution speed of the algorithms is demonstrated by numerical experiments with area sizes up to 10,000 grid cells.

关键词： coverage path planning Unmanned aerial vehicle Mixed-integer programming

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Station-viewpoint joint coverage path planning towards mobile visual inspection

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2025年 91卷

作者： Kong, Feifei Du, Fuzhou Zhao, Delong Beihang Univ Sch Mech Engn & Automat 37 Coll Rd Beijing 100191 Peoples R China

coverage path planning (CPP) has been widely studied due to its significant impact on the efficiency of automated surface quality inspection. However, these researches mostly concentrate on fixed-base visual robotic schemes, with limited focus on the widely utilized mobile-base schemes which require considerations of inherent constraints between stations (base positions) and viewpoints. Therefore, this article models a station-viewpoint joint coverage path planning problem and proposes a workflow to solve it. Within this workflow, firstly, a viewpoint selection genetic algorithm based on alternating evolution strategy is presented to optimize both the viewpoint quantity and view quality;secondly, a novel genetic algorithm is devised to accomplish joint assignment and sequence planning for stations and viewpoints. Several experimental studies are conducted to validate the effectiveness and efficiency of the proposed methods, and the proposed genetic algorithms exhibit notable superiorities compared to the benchmark methods in terms of viewpoint quantity, mean view quality, motion cost, and computational efficiency.

关键词： Mobile visual inspection Station-viewpoint joint planning coverage path planning Genetic algorithm

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Large-Area coverage path planning Method Based on Vehicle-UAV Collaboration

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1247-1247页

作者： Zhang, Nan Zhang, Bingbing Zhang, Qiang Gao, Chaojun Feng, Jiahao Yue, Linkai Zhengzhou Univ Sch Phys Zhengzhou 450001 Peoples R China Zhengzhou Univ Lab Zhongyuan Light Zhengzhou 450001 Peoples R China

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle-UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle-UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency.

关键词： path planning coverage path planning vehicle-UAV collaboration large-scale coverage region segmentation

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TIMtrace: coverage path planning for Thermal Interface Materials

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 61卷

作者： Baeuerle, S. Steimer, A. Mikut, R. Karlsruhe Inst Technol KIT Inst Automat & Appl Informat IAI Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Robert Bosch GmbH Bosch Ctr Artificial Intelligence BCAI Robert Bosch Campus 1 D-71272 Renningen Germany Robert Bosch GmbH Mobil Elect Markwiesenstr 46 D-72770 Reutlingen Germany

Thermal Interface Materials are used to transfer heat from a semiconductor to a heatsink. They are applied along a dispense path onto the semiconductor and spread over its entire surface once the heatsink is joined. To plan this application path, design engineers typically perform an iterative trial-and-error procedure of elaborate simulations and manual experiments. State-of-the-art path planning approaches are mainly focused on applications with a constant path width. They cannot be transferred to Thermal Interface Materials since the resulting area coverage depends on their complex flow behavior. We propose the fully automated optimization approach TIMtrace, which clearly outperforms the current manual path planning and respects all relevant manufacturing constraints. Generally, the design of an optimization problem highly influences its convergence properties. Our chosen path parameterization keeps the dimensionality of the independent variables reasonably low. We analyze different loss function components and respective weighting strategies with regard to both optimization convergence and result quality. TIMtrace enables an automated planning of dispense paths that do not contain voids, do not violate taboo zones and can be transferred easily to automated dispensing machines. An optimum dispense path helps to increase the reliability of the thermal interface and to make manufacturing more sustainable by reducing material waste. We show results on multiple real products from automotive series production, including experimental validation on actual series manufacturing equipment.

关键词： Thermal management coverage path planning Optimization Automated design

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Efficient coverage path planning for a Drone in an Urban Environment

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DRONES 2025年第2期9卷 98-98页

作者： Sabag, Joanne Pinkovich, Barak Rivlin, Ehud Rotstein, Hector Andrew & Erna Viterbi Fac Elect & Comp Engn IL-3200003 Haifa Israel Technion Israel Inst Technol Fac Educ Sci & Technol IL-3200003 Haifa Israel

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global path planning (GPP) problem, which uses probabilistic information and the camera's field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms' performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions;further, in particular, the GPP problem has not been addressed previously.

关键词： unmanned aerial vehicles (UAVs) autonomous systems coverage path planning

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Development and Application of the coverage path planning Based on a Biomimetic Robotic Fish

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JOURNAL OF FIELD ROBOTICS 2025年第0期

作者： Liu, Jincun Zhao, Jian Liu, Zhenna Liu, Yang Ren, Yinjie An, Dong Wei, Yaoguang China Agr Univ Natl Innovat Ctr Digital Fishery Beijing Peoples R China China Agr Univ Key Lab Smart Farming Technol Aquat Anim & Livesto Minist Agr & Rural Affairs Beijing Peoples R China China Agr Univ Beijing Engn Technol Res Ctr Internet Things Agr Beijing Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Shandong Lab Vocat & Tech Coll Jinan Peoples R China

This paper addresses the coverage path planning (CPP) and path-following challenges for an underwater biomimetic robotic fish, aimed at performing water quality monitoring in deep-sea net cages. First, a novel CPP strategy is proposed to minimize total path length, repetition rate, and turning frequency, thereby enhancing path coverage efficiency and reducing energy consumption. This strategy integrates the Deep Q-Network (DQN) method, a tailored reward function, and an RRT*-inspired rewrite mechanism. Second, a high-performance "LOS-PID" controller is developed to enable precise path following by the robotic fish. Finally, simulation experiments and field tests with the untethered robotic fish validate the effectiveness of the proposed CPP and path-following strategies, highlighting their practical applicability in aquaculture management. The proposed algorithm provides valuable insights into optimizing coverage path planning for underwater robotic systems in real-world scenarios.

关键词： coverage path planning deep-sea net cage DQN robotic fish

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A Comprehensive Survey on coverage path planning for Mobile Robots in Dynamic Environments

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IEEE ACCESS 2025年 13卷 60158-60185页

作者： Jayalakshmi, K. P. Nair, Vishnu G. Sathish, Dayakshini Manipal Acad Higher Educ Manipal Inst Technol Dept Aeronaut & Automobile Engn Manipal 576104 Karnataka India St Joseph Engn Coll Dept Elect & Commun Engn Mangaluru 575028 Karnataka India

coverage path planning (CPP) is a fundamental aspect of mobile robotics, enabling robots to navigate dynamic environments efficiently while ensuring thorough coverage of all areas of interest and avoiding obstacles. CPP is pivotal in diverse applications, from everyday tasks like vacuum cleaning, lawn mowing, and window cleaning to specialized operations such as demining hazardous areas, autonomous underwater imaging, and inspecting complex structures. This survey reviews recent advancements in CPP, focusing on algorithms and methodologies tailored for dynamic environments. It highlights the challenges posed by environmental variability, obstacle dynamics, and real-time computational demands. By synthesizing insights from existing research, this survey aims to guide future developments in CPP, paving the way for smarter and more adaptive robotic systems capable of handling the complexities of real-world scenarios.

关键词： Robots Heuristic algorithms Robot kinematics planning path planning Surveys Mobile robots Cleaning Vehicle dynamics Service robots coverage path planning path planning dynamic environment obstacle avoidance autonomous systems

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Improved genetic algorithm based on bi-level co-evolution for coverage path planning in irregular region

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SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Chen, Guanzhong Du, Yufeng Xi, Xiaoming Zhang, Kai Yang, Jichong Xu, Longsheng Ren, Chunxiao Shandong Jianzhu Univ Sch Comp Sci & Technol Fengming Rd Jinan 250101 Shandong Peoples R China Shandong Jianzhu Univ Inst Resources & Environm Innovat Fengming Rd Jinan 250101 Shandong Peoples R China State Grid China Technol Coll Erhuannan Rd Jinan 250101 Shandong Peoples R China Kailin Environm Protect Equipment Corp Dept Endocrinol Fenghuang Rd Heze 274900 Shandong Peoples R China Shandong Int Talent Exchange Serv Ctr Shunhua Rd Jinan 250101 Shandong Peoples R China

Irregular region coverage path planning has been widely applied in robot search tasks and has attracted a lot of attention. This study proposes an improved genetic algorithm based on bi-level co-evolution for coverage path planning (IGA-CPP) in irregular region. The processes of the method are region decomposition, sub-region selection and coverage path generation. The difficult problem is to plan the coverage path of sub-regions after decomposition. The key idea is to design a bi-level co-evolutionary strategy for planning optimized order and coverage path of sub-regions. The genetic algorithm has been improved by introducing the bi-level co-evolutionary strategy into the genetic framework and linearly reducing the population size in each iteration to achieve fast convergence. The improved genetic algorithm is used as computing engine for path length optimization. The performance of IGA-CPP is evaluated by simulation experiments. Optimal control parameters are obtained through multiple comparison experiments. Statistical analysis with other algorithms shows that IGA-CPP is more efficient. It can be concluded that IGA-CPP is workable to obtain optimized coverage path of irregular region.

关键词： coverage path planning Irregular region Genetic algorithm Co-evolutionary strategy

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