Complete coverage path planning for an Arnold system based mobile robot to perform specific types of missions

Li, Caihong; Fang, Chun; Wang, Fengying; Xia, Bin; Song, Yong

doi:10.1631/fitee.1800616

Cited by 22 publications

(13 citation statements)

References 27 publications

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“…A controller is designed and built with a combination of chaotic dynamic variables and kinematic equations of the mobile robot to construct chaotic motion. This system could also perform surveillance tasks by achieving the highest coverage rate without the requirement of obstacle avoidance along the boundaries [51].…”

Section: B Chaotic Coverage Path Plannermentioning

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: B Chaotic Coverage Path Plannermentioning

confidence: 99%

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

Mohd‐Mokhtar²,

2021

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“…It is quite clear that there is a significantly increased interest in research on UAV cooperation. In addition, considering that path planning is one of the core UAV technologies, most of the studies focus on the path planning of a single UAV (Li CH et al, 2019). However, the relevant research on CPP of multiple UAVs, as opposed to that of UAV cooperation, is still fairly limited.…”

Section: Introductionmentioning

confidence: 99%

A review of cooperative path planning of an unmanned aerial vehicle group

Zhang

Xin

Dou

et al. 2020

Front Inform Technol Electron Eng

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As a cutting-edge branch of unmanned aerial vehicle (UAV) technology, the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors, due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks, e.g., search and rescue, fire-fighting, reconnaissance, and surveillance. Cooperative path planning (CPP) is a key problem for a UAV group in executing tasks collectively. In this paper, an attempt is made to perform a comprehensive review of the research on CPP for UAV groups. First, a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements, i.e., task, UAV group, and environment, as a basis for a comprehensive classification of different types of CPP problems. By following the proposed framework, a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified way. Then, a review and a statistical analysis are presented based on the taxonomy, emphasizing the coordinative elements in the existing CPP research. In addition, a collection of challenging CPP problems are provided to highlight future research directions.

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“…A rapid progress of the science and technology has witnessed the wide applications of the mobile robots in various fields [1][2][3][4][5], such as the industry, agriculture, service industry, military field, medical industry, space exploration, national security, and so on. To execute these diverse difficult tasks, an excellent path planning ability is one of the prerequisites that the mobile robots must have.…”

Section: Introductionmentioning

confidence: 99%

Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

Wang

Chen

Zhang

et al. 2020

Artif Life Robotics

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Path planning in dynamic environment is a great challenge for mobile robot. A large number of approaches have been used to deal with it. Since the neural network algorithm has the ability to find the optimal solution at high speed and self-learning function, it has achieved extensive applications in the path planning tasks. Considering that the optimization performance of the neural network heavily depends on the quality of the training sample, this paper proposes a novel way to provide the training samples for the neural network. Work space of the robot is divided into two parts: global safe area and local dangerous area. In the global safe area, the robot only receives the attraction force from the target and it moves towards the target directly. In the dangerous area, except the attraction force, the robot also receives the repulsion force from the obstacle(s). The repulsion force and the angle between the obstacle and the target (origin of the coordinate is in the position of the robot) are used to be the inputs of the fuzzy inferencing system, and the deflection angle of the robot is the output. The final moving direction of the robot is determined by summing this deflection angle and the direction of the attraction force. The coordinates of the target and obstacle, and the moving direction of the robot corresponding to this position relationship, constitute the training samples for the neural network. Benefited from the precise moving direction obtained by the fuzzy artificial potential field algorithm, the neural network gets excellent path optimization ability. Simulation and physical experiment results demonstrate the potential of the proposed algorithm.

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Complete coverage path planning for an Arnold system based mobile robot to perform specific types of missions

Cited by 22 publications

References 27 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

A review of cooperative path planning of an unmanned aerial vehicle group

Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

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