Complete Coverage Path Planning Based on Bioinspired Neural Network and Pedestrian Location Prediction

Yang, Chunyu; Tang, Yaohan; Zhou, Linna; Ma, Xiaoping

doi:10.1109/cyber.2018.8688311

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…Adaptive coverage path planning [ 30 ] is aiming to achieve complete coverage with minimal path length and it is efficient in dynamic environments. Complete coverage path planning based on biologically inspired neural network [ 31 ] plans collision-free trajectory for real-time coverage task in dynamic environments. The energy constrained online coverage path planning [ 32 ] is based on contour following, which causes sharp turns in rectangular environments.…”

Section: Related Workmentioning

confidence: 99%

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

Šelek

Seder

Brezak

et al. 2022

Sensors

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The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot.

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Section: Related Workmentioning

confidence: 99%

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

Šelek

Seder

Brezak

et al. 2022

Sensors

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show abstract

“…Although the multi-robot system increases the time efficiency of the area coverage, the system has a high deployment cost. Yang et al [276] employed the BINN approach with pedestrian and obstacle avoidance strategy to optimize the collision-free CPP trajectory. Singha et al [277] applied the BINN algorithm by modifying the backtracking technique to improve the computing efficiency of neural activities, overcoming the dead-lock issue.…”

Section: ) Neural Networkmentioning

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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“…A target search algorithm and a multi-target search algorithm [12], [13] were proposed based on the BNN to solve the hunting problem of multi-AUV (Autonomous Underwater Vehicle) with obstacles and non-obstacles in an unknown environment, and in [14], [15], improved dynamic BNN algorithms were proposed to deal with real-time path planning for AUV in various 3D underwater environments. An improved BNN-based pedestrian location prediction approach, which was called ''active obstacle avoidance strategy'' was presented to plan the collision-free complete coverage path planning (CCPP) trajectory for mobile robot in [16], and in [17], a BNN-based dynamics approach was proposed to solve CCPP problem for multiple robots. The BNN technology was used in [18] to assist a robot to patrol an unknown working environment, and faster R-CNN methods were used to find scattered nails and screws in real time, and this paper enabled the robot to automatically retrieve nails and screws.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

2019

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With the wide application of Bioinspired Neural Network in the field of robot path planning, the environmental scale of robot path planning is getting larger, and the environmental resolution requirements are getting higher. However, with the increase of the environment size and resolution requirement, the neuronal activity value calculation cost and the time cost of the Bioinspired Neural Network will increase sharply. Aiming at this problem, this paper proposes an improved Bioinspired Neural Network path planning method based on Scaling Terrain. Using a Multi-Scale Map method and Dijkstra algorithm, the optimal path of a Coarse Scale Map is calculated. The optimal path obtained from the Coarse Scale Map is used to guide the neural network planning weights of the Fine Scale Map from the same terrain. Thus, the optimal path of the Fine Scale Map can be calculated by the improved BNN algorithm. Introducing this Multi-Scale Map Method into the Bioinspired Neural Network can greatly reduce the time cost of the Bioinspired Neural Network path planning algorithm and reduce the mathematical complexity. Simulation results in some computer integrated virtual environments further demonstrate the superiority of this method and the experimental results are encouraging.INDEX TERMS Multi-scale map method, path planning, bioinspired neural network, Dijkstra algorithm.

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Complete Coverage Path Planning Based on Bioinspired Neural Network and Pedestrian Location Prediction

Cited by 5 publications

References 9 publications

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

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

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

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