Adaptive path finding algorithm in dynamic environment for warehouse robot
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S.I. : GREEN AND HUMAN INFORMATION TECHNOLOGY 2019
Adaptive path finding algorithm in dynamic environment for warehouse robot Mun-Kit Ng1 • Yung-Wey Chong2
•
Kwang-man Ko3 • Young-Hoon Park4 • Yu-Beng Leau5
Received: 2 April 2019 / Accepted: 24 January 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Warehouse robots have been widely used by manufacturers and online retailer to automate good delivery process. One of the fundamental components when designing a warehouse robot is path finding algorithm. In the past, many path finding algorithms had been proposed to identify the optimal path and improve the efficiency in different conditions. For example, A* path finding algorithm is developed to obtain the shortest path, while D* obtains a complete coverage path from source to destination. Although these algorithms improved the efficiency in path finding, dynamic obstacle that may exist in warehouse environment was not considered. This paper presents AD* algorithm, a path finding algorithm that works in dynamic environment for warehouse robot. AD* algorithm is able to detect not only static obstacle but also dynamic obstacles while operating in warehouse environment. In dynamic obstacle path prediction, image of the warehouse environment is processed to identify and track obstacles in the path. The image is pre-processed using perspective transformation, dilation and erosion. Once obstacle has been identified using background subtraction, the server will track and predict future path of the dynamic object to avoid the obstacle. Keywords Path finding Dynamic obstacle avoidance Warehouse robot
1 Introduction & Yung-Wey Chong [email protected] Mun-Kit Ng [email protected] Kwang-man Ko [email protected] Young-Hoon Park [email protected] Yu-Beng Leau [email protected] 1
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
2
National Advanced IPv6 Centre, Universiti Sains Malaysia, George Town, Malaysia
3
Department of Computer Science and Engineering, Sangji University, Wonju, South Korea
4
Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea
5
Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
In recent years, warehouse robots have been used as e-commerce bloom. The state-of-the-art innovation has reduced the number of workforce required and increased the efficiency of warehouse management system. E-commerce companies such as Amazon and Alibaba utilised warehouse robot to automate the process of picking, sorting and navigation assistance of goods. Thus, the robots have replaced human workloads especially in performing repetitive tasks. According to Dubois and Hamilton [1], the demand of warehouse robot is increasing and expect to growth 12% in 2018 in the USA. These warehouse robots helped to pick and pack USD 394.8 billion worth of goods in 2017. Markets and Markets [2] projected that the value will further increase to
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