Browsing by Author "Gu, Chengzhen"

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    Exploring the social impacts of adopting autonomous vehicles in the supply chain
    (CILT, 2022-09-09) Gu, Chengzhen; Reefke, Hendrik; Yates, Nicky
    Autonomous vehicles (AVs) have served the logistics sector in the form of automated guided vehicles (AGVs) for decades. With the advent of Industry 4.0 (In 4.0 – the Fourth Industrial Revolution) in 2011, significant advances have been witnessed (Schwab, 2016). Rapid development of innovations such as robots and drones indicates wider adoption across the industry (Tang and Veelenturf, 2019). Logistics giants such as Alibaba and JD.com in China, and DHL and Amazon in Europe and the USA are applying or testing autonomous vehicles for use in supply chain processes including distribution and storage (Merlino and Sproģe, 2017; Mohamed et al., 2020). Further, Zipline is a successful drone delivery service provider in medical supplies for African countries (Scott and Scott, 2017). However, compared with the rapid progress of technology, current academic research and development of knowledge in this area is lagging behind (Van Meldert and De Boeck, 2016; Monios and Bergqvist, 2020), especially in freight transport (Flämig, 2016; Van Meldert and De Boeck, 2016). Previous studies have focussed particularly on developing the drone Vehicle Routine Problems (VRP) or Travelling Salesman Problem (TSP), to minimise costs and negative environmental externalities from a number of perspectives (Murray and Chu, 2015; Ha et al., 2018). These studies have demonstrated significant positive economic and environmental sustainability performance (Tang and Veelenturf, 2019). The social perspective has received less focus.
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    Semantic terrain segmentation in the navigation vision of planetary rovers – a systematic literature review
    (MDPI, 2022-11-01) Kuang, Boyu; Gu, Chengzhen; Rana, Zeeshan A.; Zhao, Yifan; Sun, Shuang; Nnabuife, Somtochukwu Godfrey
    Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.