Zhe Liu, Zhijian Qiao, Chuanzhe Suo, Yingtian Liu and Kefan Jin
This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to…
Abstract
Purpose
This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.
Design/methodology/approach
The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.
Findings
Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.
Practical implications
This paper provides comprehensive experiments in real industrial environments.
Originality/value
The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.
Details
Keywords
Jiang Zhao, Ksenia Gerasimova, Yala Peng and Jiping Sheng
The purpose of this paper is to discuss characteristics of organic food value chain governance and policy tools that can increase the supply of good quality of agri-products.
Abstract
Purpose
The purpose of this paper is to discuss characteristics of organic food value chain governance and policy tools that can increase the supply of good quality of agri-products.
Design/methodology/approach
This paper discusses a national organic food supply system in China, identifying the link between an organization form with a social confidence crisis and information asymmetry as the main challenges. It develops an analytical model of the market structure of organic certification based on the contract theory, which considers the certification incentive driven by both farmers and processors. Two cases of raw milk producers and processors provide empirical data.
Findings
The argument which is brought forward is that product information asymmetry together with strict requirement for ensuring organic food integrity brings the organic milk value chain into a highly integrated organization pattern. A tight value chain is effective in the governance of organic food supply chain under third party certification (TPC), while a loose value chain discourages producing organic products because of transaction costs. TPC is found to be a positively correlation with a tight value chain, but it brings high organizational cost and it raises cost for consumers.
Originality/value
This is the first paper discussing the governance of organic food value chain in Chinese milk industry.