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Energy-Efficient UAVs coverage path planning Approach

Computer Modeling in Engineering & Sciences 2023年第9期136卷 3239-3263页

作者： Gamil Ahmed Tarek Sheltami Ashraf Mahmoud Ansar Yasar Computer Engineering Department Interdisciplinary Research Center of Smart Mobility and LogisticsKing Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Transportation Research Institute(IMOB) Hasselt UniversityHasselt3500Belgium

Unmanned aerial vehicles(UAVs),commonly known as drones,have drawn significant consideration thanks to their agility,mobility,and flexibility *** play a crucial role in modern reconnaissance,inspection,intelligence,and surveillance *** path planning(CPP)which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest(ROI).However,the flight time of the UAV is limited due to a battery limitation and may not cover the whole region,especially in large ***,energy consumption is one of the most challenging issues that need to be *** this paper,we propose an energy-efficient coverage path planning algorithm to solve the CPP *** objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole *** do so,the flight path is optimized and the number of turns is reduced to minimize the energy *** proposed approach first decomposes the ROI into a set of cells depending on a UAV camera ***,the coverage path planning problem is formulated,where the exact solution is determined using the CPLEX *** small-scale problems,the CPLEX shows a better solution in a reasonable ***,the CPLEX solver fails to generate the solution within a reasonable time for large-scale ***,to solve the model for large-scale problems,simulated annealing forCPP is *** results show that heuristic approaches yield a better solution for large-scale problems within amuch shorter execution time than the CPLEX ***,we compare the simulated annealing against the greedy *** results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

关键词： coverage path planning MILP CPLEX solver energy model optimization region of interest area of interest

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A multi-scroll chaotic system for a higher coverage path planning of a mobile robot using flatness controller

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CHAOS SOLITONS & FRACTALS 2019年 118卷 366-375页

作者： Nasr, Salah Mekki, Hassen Bouallegue, Kais Univ Sousse Natl Engn Sch Sousse Networked Objects Control & Commun Syst Lab Sousse Tunisia Higher Inst Appl Sci & Technol Sousse Dept Elect Engn Sousse Tunisia

In this paper, we present a study of higher-coverage path planning desired for mobile robots for some special tasks using a flatness controller based on the dynamic features of a multi-scroll chaotic system. The trajectory construction is envisaged for field exploration missions or for the specific purpose of research where fast scanning of a complete workplace of the robot is required. For these types of applications or missions, researchers have opted for using a chaotic trajectory. The chaotic systems that are often used are: the Lorenz system, the Standard-map system, the Arnold system, etc. The problem for this type of chaotic systems is the density of these orbits that appear in the robot trajectory, which begets a loss of energy and time. For that, we propose a multi-scroll chaotic system to avoid the redundancy of orbits. At the boundary conditions, in order not to leave the workspace, a mirror mapping method is utilized, which constrains all the mobile robot positions in the workspace and which can reflect all the overflow waypoints returning to it. The major problem of this type of chaotic trajectory is how to determine the control law which makes it possible to guarantee the system commendability. However, a flatness controller, which shows great advantages providing an integrated structure for planning and control, is utilized to ensure the commendability of the chaotic robot. Compared to simply using the double-scroll chaotic systems in the whole workspace, the suggested new multi-scroll chaotic system, combined with the mirror mapping method, shows good results that can achieve a higher coverage for a larger workplace of the mobile robot. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： coverage path planning Mobile robot Chaotic systems Multi-scroll Chua system Bounded workspace Flatness controller

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A Clustering-Based coverage path planning Method for Autonomous Heterogeneous UAVs

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 25546-25556页

作者： Chen, Jinchao Du, Chenglie Zhang, Ying Han, Pengcheng Wei, Wei Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

关键词： path planning Task analysis Unmanned aerial vehicles planning Training Clustering algorithms Search problems coverage path planning unmanned aerial vehicle clustering-base method autonomous heterogeneous UAVs

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An autonomous coverage path planning algorithm for maritime search and rescue of persons-in-water based on deep reinforcement learning

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OCEAN ENGINEERING 2024年 291卷

作者： Wu, Jie Cheng, Liang Chu, Sensen Song, Yanjie Nanjing Univ Sch Geog & Ocean Sci Nanjing Peoples R China Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing Peoples R China Nanjing Univ Collaborat Innovat Ctr South China Sea Studies Nanjing Peoples R China Nanjing Univ Collaborat Innovat Ctr Novel Software Technol & In Nanjing Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Nanjing Univ Sch Geog & Ocean Sci Nanjing 210023 Peoples R China

The prevalence of maritime transportation and operations is increasing, leading to a gradual increase in drowning accidents at sea. In the context of maritime search and rescue (SAR), it is essential to develop effective search plans to improve the survival probability of persons-in-water (PIWs). However, conventional SAR search plans typically use predetermined patterns to ensure complete coverage of the search area, disregarding the varying probabilities associated with the PIW distribution. To address this issue, this study has proposed a maritime SAR vessel coverage path planning framework (SARCPPF) suitable for multiple PIWs. This framework comprises three modules, namely, drift trajectory prediction, the establishment of a multilevel search area environment model, and coverage search. First, sea area-scale drift trajectory prediction models were employed using the random particle simulation method to forecast drift trajectories. A hierarchical probability environment map model was established to guide the SAR of multiple SAR units. Subsequently, we integrated deep reinforcement learning with a reward function that encompasses multiple variables to guide the navigation behavior of ship agents. We developed a coverage path planning algorithm aimed at maximizing the success rates within a limited timeframe. The experimental results have demonstrated that our model enables vessel agents to prioritize high-probability regions while avoiding repeated coverage.

关键词： Persons-in-water Drift trajectory prediction Maritime search and rescue coverage path planning Deep reinforcement learning

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Multi-robot coverage path planning for dimensional inspection of large free-form surfaces based on hierarchical optimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期127卷 5471-5486页

作者： Zhao, Wenzheng Liu, Yinhua Li, Yanzheng Hu, Chengwei Sun, Rui Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

The multi-robot coverage path planning problem (MRCPP), incorporating with the sub-problems of viewpoint sampling, viewpoint task allocation and robotic sequential planning generally, is a highly coupled problem with complex engineering constraints and is difficult to achieve the optimal result. Most coverage path planning studies in the field of industrial inspection focus on viewpoint sampling and sequential path planning for the individual robot. At the same time, the measurement uncertainty requirements of the inspected surface features for the candidate viewpoints are rarely considered in the process of CPP. To address this problem, a systematic MRCPP framework for free-form surface inspection is developed considering the measurement uncertainty requirements and full coverage of the surfaces to ensure the inspection efficiency and the scanning accuracy of to-be-inspect features. Specifically, a stepwise method for multi-robot optical coverage path planning is proposed. Firstly, a redundant viewpoint set is generated based on the geometric model of the product;additionally, considering the constraints of measurement uncertainty and full coverage constraint, an enhanced Random Inspection Tree Algorithm (RITA) is used to obtain the optimal viewpoint set. Secondly, an improved group Ant Colony optimization (IACO) algorithm is proposed to realize multi-robot viewpoint allocation and sequential planning on the optimal viewpoint set. Finally, in order to evaluate the effectiveness of the proposed method, a 4-robot optical inspection case of an auto body was used. Compared to the benchmark method, the number of viewpoints is reduced by 56.38%, and the bottleneck time in the station is reduced by 7.651%.

关键词： coverage path planning Dimensional quality inspection Industrial robot Task allocation Hierarchical optimization

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A co-optimal coverage path planning method for aerial scanning of complex structures

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 158卷 113535-113535页

作者： Shang, Zhexiong Bradley, Justin Shen, Zhigang Univ Nebraska Durham Sch Architectural Engn & Construct Lincoln NE 68588 USA Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

The utilization of unmanned aerial vehicles (UAVs) in survey and inspection of civil infrastructure has been growing rapidly. However, computationally efficient solvers that find optimal flight paths while ensuring high-quality data acquisition of the complete 3D structure remains a difficult problem. Existing solvers typically prioritize efficient flight paths, or coverage, or reducing computational complexity of the algorithm - but these objectives are not co-optimized holistically. In this work we introduce a co-optimal coverage path planning (CCPP) method that simultaneously co-optimizes the UAV path, the quality of the captured images, and reducing computational complexity of the solver all while adhering to safety and inspection requirements. The result is a highly parallelizable algorithm that produces more efficient paths where quality of the useful image data is improved. The path optimization algorithm utilizes a particle swarm optimization (PSO) framework which iteratively optimizes the coverage paths without needing to discretize the motion space or simplify the sensing models as is done in similar methods. The core of the method consists of a cost function that measures both the quality and efficiency of a coverage inspection path, and a greedy heuristic for the optimization enhancement by aggressively exploring the viewpoints search spaces. To assess the proposed method, a coverage path quality evaluation method is also presented in this research, which can be utilized as the benchmark for assessing other CPP methods for structural inspection purpose. The effectiveness of the proposed method is demonstrated by comparing the quality and efficiency of the proposed approach with the state-of-art through both synthetic and real-world scenes. The experiments show that our method enables significant performance improvement in coverage inspection quality while preserving the path efficiency on different test geometries. (c) 2020 Elsevier Ltd. All rights reser

关键词： coverage path planning Unmanned aerial vehicle Particle swarm optimization Viewpoint quality

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Complete coverage path planning of mobile robots for humanitarian demining

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2012年第5期39卷 484-493页

作者： Dakulovic, Marija Petrovic, Ivan Univ Zagreb Dept Control & Comp Engn Zagreb 41000 Croatia

Purpose - The purpose of this paper is to present a path planning algorithm for a non-circular shaped mobile robot to autonomously navigate in an unknown area for humanitarian demining. For that purpose the path planning problem comes down to planning a path from some starting location to a final location in an area so that the robot covers all the reachable positions in the area while following the planned path. Design/methodology/approach - The proposed algorithm uses occupancy grid map representation of the area. Every free cell in the grid map represents a node in the graph being searched to find the complete coverage path. The complete coverage path is followed by the dynamic window algorithm, which includes robot's kinematic and dynamic constraints. Findings - The proposed algorithm finds the complete coverage path in the graph accounting for the dimensions of the mobile robot, where non-circular shaped robots can be easily included. The algorithms are implemented under the ROS (robot operating system) and tested in the stage 3D simulator for mobile robots with a randomly generated simulation map of an unknown area. Research limitations/implications - Some parts of the area close to obstacles are hard to cover due to complex non-circular shaped robot and non-perfect path following. The future work should include better path following algorithm. Practical implications - The proposed algorithm has shown itself as effective and could meet the working demands of humanitarian demining. Originality/value - The algorithm proposed in the paper enables complete coverage path planning of non-circular shaped robots in unknown areas.

关键词： Programming and algorithm theory Robotics Explosives Autonomous mobile robots path planning coverage path planning Exploration

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Multi-AUV coverage path planning algorithm using side-scan sonar for maritime search

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OCEAN ENGINEERING 2024年 300卷

作者： Li, Liang Li, Yiping Wang, Yuliang Xu, Gaopeng Wang, Hailin Gao, Peiyan Feng, Xisheng Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Key Lab Marine Robot Shenyang 110169 Liaoning Peoples R China

coverage search is widely applied in the maritime military and civilian fields. To improve search efficiency and detection accuracy, we propose an effective and rapid multi-autonomous underwater vehicle (AUV) coverage path planning (ER-MCPP) algorithm using side-scan sonar (SSS). First, the given search region was optimized and modeled using grid decomposition. Next, a Voronoi diagram was used to divide and allocate the regions according to the initial position of the AUV. Subsequently, based on the idea of guidance, navigation, and control (GNC), a coverage search algorithm for a single AUV was proposed, including a path planning method combining improved lawn mower and outward-spiral methods, an integrated navigation method based on Kalman filtering, and a path tracking method considering ocean currents. In addition, we proposed a fault-tolerant control method. When an AUV fails unexpectedly, the other AUVs can accomplish a coverage search for the region through cooperation. Simulation experiments were performed using the MATLAB software. The results demonstrate that the proposed algorithm is valid. Compared with other methods, our algorithm has better robustness, finds targets more rapidly, and has a higher search efficiency. The practicality of the proposed algorithm is verified through field tests.

关键词： coverage path planning Autonomous underwater vehicle Side-scan sonar Maritime search

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Multi-Robot coverage path planning for the Inspection of Offshore Wind Farms: A Review

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DRONES 2024年第1期8卷 10-10页

作者： Foster, Ashley J. I. Gianni, Mario Aly, Amir Samani, Hooman Sharma, Sanjay Univ Plymouth Fac Sci & Engn Sch Engn Comp & Math Plymouth PL4 8AA England Univ Liverpool Sch Elect Engn Elect & Comp Sci Liverpool L69 3BX England Univ Arts London Creat Comp Inst London SE5 8UF England

Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation in inspections can reduce human effort and financial costs. Despite the benefits, research on automating inspection is sparse. This work proposes that OWT inspection can be described as a multi-robot coverage path planning problem. Reviews of multi-robot coverage exist, but to the best of our knowledge, none captures the domain-specific aspects of an OWT inspection. In this paper, we present a review on the current state of the art of multi-robot coverage to identify gaps in research relating to coverage for OWT inspection. To perform a qualitative study, the PICo (population, intervention, and context) framework was used. The retrieved works are analysed according to three aspects of coverage approaches: environmental modelling, decision making, and coordination. Based on the reviewed studies and the conducted analysis, candidate approaches are proposed for the structural coverage of an OWT. Future research should involve the adaptation of voxel-based ray-tracing pose generation to UAVs and exploration, applying semantic labels to tasks to facilitate heterogeneous coverage and semantic online task decomposition to identify the coverage target during the run time.

关键词： multi-robot coverage path planning UAV structural inspection offshore wind

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Predator-Prey Reward Based Q-Learning coverage path planning for Mobile Robot

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IEEE ACCESS 2023年 11卷 29673-29683页

作者： Zhang, Meiyan Cai, Wenyu Pang, Lingfeng Zhejiang Univ Water Resources & Elect Power Coll Elect Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou 310018 Peoples R China

coverage path planning (CPP in short) is a basic problem for mobile robot when facing a variety of applications. Q-Learning based coverage path planning algorithms are beginning to be explored recently. To overcome the problem of traditional Q-Learning of easily falling into local optimum, in this paper, the new-type reward functions originating from Predator-Prey model are introduced into traditional Q-Learning based CPP solution, which introduces a comprehensive reward function that incorporates three rewards including Predation Avoidance Reward Function, Smoothness Reward Function and Boundary Reward Function. In addition, the influence of weighting parameters on the total reward function is discussed. Extensive simulation results and practical experiments verify that the proposed Predator-Prey reward based Q-Learning coverage path planning (PP-Q-Learning based CPP in short) has better performance than traditional BCD and Q-Learning based CPP in terms of repetition ratio and turns number.

关键词： path planning Mobile robots Predator prey systems Q-learning planning Partitioning algorithms Behavioral sciences coverage path planning predator-prey model reinforcement learning Q-learning algorithm mobile robot

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