Complete Coverage Path Planning Based on Ant Colony Algorithm

Zhang, Chibin; Wang, Xingsong; Du, Youwei

doi:10.1109/mmvip.2008.4749559

Cited by 17 publications

(3 citation statements)

References 5 publications

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“…Wong and MacDonald [76] extended the discovery of important cell breakdown sites to any type of topological landmark. Chibin et al [77] used the ACO method to tackle a comprehensive coverage path planning problem. Galceran and Carreras [78] have summarized and discussed the majority of the significant work in the topic of coverage path planning.…”

Section: Coverage Routingmentioning

confidence: 99%

A Comprehensive Review of Path Planning for Agricultural Ground Robots

et al. 2022

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The population of the world is predicted to reach nine billion by 2050, implying that agricultural output must continue to rise. To deal with population expansion, agricultural chores must be mechanized and automated. Over the last decade, ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. This research examines several agricultural applications as well as the path planning approach used. The purpose of this study is to investigate the current literature on path/trajectory planning aspects of ground robots in agriculture using a systematic literature review technique, to contribute to the goal of contributing new information in the field. Coverage route planning appears to be less advanced in agriculture than point-to-point path routing, according to the finding, which is due to the fact that covering activities are usually required for agricultural applications, but precision agriculture necessitates point-to-point navigation. In the recent era, precision agriculture is getting more attention. The conclusion presented here demonstrates that both field coverage and point-to-point navigation have been applied successfully in path planning for agricultural robots.

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Section: Coverage Routingmentioning

confidence: 99%

A Comprehensive Review of Path Planning for Agricultural Ground Robots

et al. 2022

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“…Thus, [234] proposed an improved ACO algorithm using a pheromone updating rule to avoid trapping into the local minimum. Chibin et al [235] used the ACO algorithm to optimize the coverage of the subarea following the distance matrix. Zhou et al [ 265] introduced an ACO algorithm by optimizing block sequence to solve TSP.…”

Section: ) Swarm Intelligencementioning

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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“…Although intriguing algorithms, swarm-based algorithms can take a large number of iterations before it converges on a solution, and the algorithm can be trapped in a local minimum. Chibin, Xingsong, and Yong (2008) attempted to reduce the size of the traveling salesman problem by combining it with cellular decomposition approaches. The workspace was first decomposed into regions as in Choset (1997Choset ( , 2000.…”

Section: Prior Researchmentioning

confidence: 99%

Complete coverage path planning in an agricultural environment

Driscoll

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Complete Coverage Path Planning Based on Ant Colony Algorithm

Cited by 17 publications

References 5 publications

A Comprehensive Review of Path Planning for Agricultural Ground Robots

A Comprehensive Review of Path Planning for Agricultural Ground Robots

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

Complete coverage path planning in an agricultural environment

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