Peng Wu, Shaorong Xie, Hengli Liu, Ming Li, Hengyu Li, Yan Peng, Xiaomao Li and Jun Luo
Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent…
Abstract
Purpose
Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance.
Design/methodology/approach
The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer.
Findings
The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials.
Originality/value
The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.
Details
Keywords
Shaorong Xie, Peng Wu, Hengli Liu, Peng Yan, Xiaomao Li, Jun Luo and Qingmei Li
This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path…
Abstract
Purpose
This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning.
Design/methodology/approach
A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment.
Findings
The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment.
Originality/value
This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.
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Usman Sufi, Arshad Hasan and Khaled Hussainey
The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and…
Abstract
Purpose
The purpose of this study is to test whether the prediction of firm performance can be enhanced by incorporating nonfinancial disclosures, such as narrative disclosure tone and corporate governance indicators, into financial predictive models.
Design/methodology/approach
Three predictive models are developed, each with a different set of predictors. This study utilises two machine learning techniques, random forest and stochastic gradient boosting, for prediction via the three models. The data are collected from a sample of 1,250 annual reports of 125 nonfinancial firms in Pakistan for the period 2011–2020.
Findings
Our results indicate that both narrative disclosure tone and corporate governance indicators significantly add to the accuracy of financial predictive models of firm performance.
Practical implications
Our results offer implications for the restoration of investor confidence in the highly uncertain Pakistani market by establishing nonfinancial disclosures as reliable predictors of future firm performance. Accordingly, they encourage investors to pay more attention to these disclosures while making investment decisions. In addition, they urge regulators to promote and strengthen the reporting of such nonfinancial information.
Originality/value
This study addresses the neglect of nonfinancial disclosures in the prediction of firm performance and the scarcity of corporate governance literature relevant to the use of machine learning techniques.
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Jaruwan Songsang, Kamonchanok Suthiwartnarueput and Pongsa Pornchaiwiseskul
The purposes of this paper are 1) to develop model of long term financial health for logistics companies in Thailand 2) to identify factors that determine long term financial…
Abstract
The purposes of this paper are 1) to develop model of long term financial health for logistics companies in Thailand 2) to identify factors that determine long term financial stability. Many researchers currently provide factors affecting financial health. Most factors refer to financial ratios, not many non-financial ratios such as age and size have been mentioned. This paper considers both financial and non-financial ratios that affect financial performance of Logistics companies in Thailand. The study has covered some interesting non-financial ratios such as Nationality of Shareholders, type of network in Logistics Company, growth rate (consisted of sales growth rate/profit growth rate/asset growth rate / Liability growth rate) and variable of growth rates. The target group is 110 logistics companies in Thailand enlisted from Department of International Trade Promotion Ministry of Commerce, Royal Thai Government. The group is divided into three categories according to financial health of company; Healthy financial, Unhealthy (Distress) and normal situation. The Multidiscriminant Analysis (MDA) is applied to analyze the differentiations among the three categories. Significant variables from MDA will be used as the independent variables for Multimonial Logistic Regression Analysis (MLRA) to identify factors that determine long terms financial stability. This paper find CF/D, RE/TA, BE/TL, Size, Age, Type of network, Nationality of Shareholders and Number of Shareholders are significant factors determine long term financial stability of Logistics company in Thailand.