Mobile robot multi-resolution full coverage path planning algorithm

Ma, Yunfei; Li, Chang

doi:10.1109/icsai.2018.8599478

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…Ma et al [318] presented the CPP algorithms to deal with the area coverage issues, especially in the dead zone and obstacle boundaries. A quadtree segmentation method could build a neuron map to split the map into different levels of subblocks before the Hilbert curve traversal algorithm traversed each mode to obtain the path.…”

Section: J Other Classical and Heuristic Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Section: J Other Classical and Heuristic Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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“…For example, Chowdhury et al [32] applied the Hilbert Curve for scheduling wireless charging vehicles to charge wireless sensor networks. Ma et al [33] used the Hilbert Curve in path recognition for mobile robots. Lawder and King et al [60] employed the Hilbert Curve for node-based localization in wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments

et al. 2020

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With the immensity of distributed Internet of Things (IoT) devices and the exponential increase in data generated from a variety of IoT-driven smart-world applications, how to effectively provide data driven service supported by IoT has become a critical issue. While the state-of-the-art technologies have been developed and network infrastructures with high capabilities have been designed to deal with the data collection problem, there are still application scenarios, in which network infrastructure is not available or appropriate in large target areas (e.g., farmlands deployed with IoT sensors in operation, providing precise agriculture; emergency responder with IoT sensors, providing public safety service). To address the issue of efficiently collecting data from IoT devices deployed in large areas without predeployed network infrastructure, we formalize the problem space in a three-dimensional model that considers task, resource, and methodology. Based on the designed problem space, we propose a novel solution that deploys an unmanned aerial vehicle (UAV), as a critical next generation mobile network, to achieve intermittent IoT device connections and enable data collection based on delay tolerant network (DTN) protocol. The UAV flight path is determined using a Hilbert Curve-based path planning algorithm. Through a series of quantitative experiments, we validate the effectiveness of our approach in a network emulation environment, and confirm its advantages in comparison with several baseline approaches. The results of our research shows the capability of quality and cost control in IoT applications such as smart agriculture, public safety disaster recovery and rescue. INDEX TERMS Internet of Things, delay tolerant networks, next generation mobile networks, unmanned aerial vehicles, path planning, data collection.

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“…CCPP aims to achieve a complete traversal of the map of an entire work region based on the known environment [8,9]. At present, there are two common methods for complete coverage path planning: random CCPP, hybrid CCPP [10].…”

Section: Introduction 1research Advances Of Complete Coverage Path Pl...mentioning

confidence: 99%

An Improved Complete Coverage Path Planning Method for Intelligent Agricultural Machinery Based on Backtracking Method

Han¹,

Shao

et al. 2022

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The advancement of society and technology has promoted the development of modern agriculture. It has become a trend to replace traditional manpower with intelligent agricultural machinery that operates independently. As the core technology of intelligent agricultural machinery, complete coverage path planning technology has become more important. At present, the complete coverage path planning algorithms still suffer from problems such as sacrificing the coverage rate to obtain the minimum energy consumption, taking a long time to calculate the algorithm, and destroying crops across the covered region. In view of the above problems, an improved complete coverage path planning algorithms based on backtracking is proposed combined with the actual needs of intelligent agricultural machinery for planting seedlings to improve four aspects: repeated coverage, search efficiency, path planning, and sub-regional crossing. Firstly, the Morse decomposition method is used to divide a complex farmland region into simple sub-regions. Then an improved backtracking method based on a greedy algorithm is proposed in order to reduce the computational efficiency of the current region connection algorithms. The priority principle and the strategy of moving along the boundary are used to solve the problems of region crossing and sacrificing the coverage rate, thereby improving the performance of the current complete coverage path planning method. Compared with the traditional backtracking method, the experimental results show that the number of backtracking points is decreased by about 70% and the occurrence of crossing sub-regions has been significantly reduced. This proposed method can improve the coverage and operating efficiency of intelligent agricultural machinery operations and provide technical support for agricultural operations such as sowing, tillage, and harvesting, thus improving the quality and efficiency of agricultural production.

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Mobile robot multi-resolution full coverage path planning algorithm

Cited by 7 publications

References 3 publications

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments

An Improved Complete Coverage Path Planning Method for Intelligent Agricultural Machinery Based on Backtracking Method

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