Enhanced Center Constraint Weighted A* Algorithm for Path Planning of Petrochemical Inspection Robot

Lai, Xin; Li, JiaHe; Chambers, Jonathon A.

doi:10.1007/s10846-021-01437-8

Cited by 31 publications

(13 citation statements)

References 44 publications

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“…The initial position of AMR is taken as I = (12,350) and the end point of AMR, E =(650,7). The performance of the proposed ABCDLR model has been compared with other existing path planning approaches such as VFH [40], FLC [41], A* [42], and ASGDLR [3]. Simulation environment has been created on MATLAB 2022a, considering all real time scenarios.…”

Section: Avoidance Of Single Obstaclementioning

confidence: 99%

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

Thakur¹,

Das²,

Kumari

et al. 2023

Preprint

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In this paper, a unique Machine Learning (ML) model namely, Adaptive Block Coordinate Descent Logistic Regression (ABCDLR), is proposed for segregating the movement of an Autonomous Mobile Robot (AMR) by framing it as three class problem, i.e., no, left, and right turn. The velocities of the left and right wheels, as well as the distance of the obstacle from AMR, are collected in real time by two Infrared (IR) and one Ultrasonic (US) sensors, respectively. The performance of the proposed algorithm is compared with three other state-of-the-art ML algorithms, such as, K-Nearest Neighbour (KNN), Naïve Baiyes, and Gradient Boosting, for obstacle avoidance by AMR; considering the accuracy, sensitivity, specificity, precision values for three different speed conditions, i.e., low, medium, and high. Various Logistic Regression (LR) model parameters, such as, pseudo R-squared (R2), Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), LL-null, and Log-Likelihood Ratio (LLR) are considered to investigate the performance of the proposed ABCDLR model. Furthermore, the proposed model has been applied for path planning in three different types of dense environments, and its performance is compared with four other competitive path planning approaches, such as, A*, Fuzzy Logic Controller(FLC), Vector Field Histogram(VFH) and ASGDLR.

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Section: Avoidance Of Single Obstaclementioning

confidence: 99%

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

Thakur¹,

Das²,

Kumari

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…D represents the minimum distance between the simulated track and the obstacle, and D represents the preset maximum distance value. The farther the simulated trajectory is from the obstacle, the greater the value of this index and the greater the value of the evaluation function [ 15 ]; then, the path is suitable for the current motion direction of the robot. Finally, the velocity vector size of the simulated path is calculated as shown in Formula (14):

where v represents the linear velocity of the current motion track, and

represents the maximum linear velocity in the dynamic window.…”

Section: Mapping and Navigationmentioning

confidence: 99%

The Navigation System of a Logistics Inspection Robot Based on Multi-Sensor Fusion in a Complex Storage Environment

Zhang

Zhou

et al. 2022

Sensors

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To reliably realize the functions of autonomous navigation and cruise of logistics robots in a complex logistics storage environment, this paper proposes a new robot navigation system based on vision and multiline lidar information fusion, which can not only ensure rich information and accurate map edges, but also meet the real-time and accurate positioning and navigation in complex logistics storage scenarios. Simulation and practical verification showed that the robot navigation system is feasible and robust, and overcomes the problems of low precision, poor robustness, weak portability, and difficult expansion of the mobile robot system in a complex environment. It provides a new idea for inspection in an actual logistics storage scenario and has a good prospective application.

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“…In recent years, a large number of research results have been achieved in the path planning of mobile robots. e conventional methods have been widely used such as the grid method [2], A * algorithm [3], and arti cial potential eld method [4]. In order to adapt mobile robots to di erent application elds and complex and changing industrial environments, various intelligent algorithms have emerged to provide new solutions for mobile robot path planning.…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning of Mobile Robot with a Novel Improved Artificial Potential Field Algorithm

Guo

Wang

Wang³

et al. 2022

Mathematical Problems in Engineering

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For the path planning and obstacle avoidance problem of mobile robots in unknown surroundings, a novel improved artificial potential field (IAPF) model was proposed in this study. In order to overcome the shortages of low efficiency, local optimization trap, and unreachable target in the classical artificial potential field (APF) method, the new adaptive step length adjustment strategy was proposed in IAPF, which improved the path planning and obstacle avoidance efficiency. A new triangular navigation method was designed to solve the local optimization trap in joint force zero condition for a variety of path planning. In order to solve the target unreachable problem, a new target attraction model was established based on the distance of obstacle to improve convergence rate, and the new method was designed such as adding the aim factor to optimize the rejection force function and so on. The two methods of IAPF and APF are compared using MATLAB simulation, the average path planning efficiency of IAPF is increased by 42.8% compared with APF, the average path length is reduced by 8.6%, and the average target convergence rate is increased by 26.1%. Finally, the physical test of the mobile robot verified the effectiveness and accuracy of IAPF.

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Enhanced Center Constraint Weighted A* Algorithm for Path Planning of Petrochemical Inspection Robot

Cited by 31 publications

References 44 publications

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

Machine Learning based Intelligent Model for Path Planning Obstacle Avoidance in Dense Environments for Autonomous Mobile Robot

The Navigation System of a Logistics Inspection Robot Based on Multi-Sensor Fusion in a Complex Storage Environment

Research on Path Planning of Mobile Robot with a Novel Improved Artificial Potential Field Algorithm

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