Deep Reinforcement Learning for Real-Time Assembly Planning in Robot-Based Prefabricated Construction

The adoption of robotics is promising to improve the efficiency, quality, and safety of prefabricated construction. Besides technologies that improve the capability of a single robot, the automated assembly planning for robots at construction sites is vital for further improving the efficiency and promoting robots into practices. However, considering the highly dynamic and uncertain nature of a construction environment, and the varied scenarios in different construction sites, it is always challenging to make appropriate and up-to-date assembly plans. Therefore, this paper proposes a Deep Reinforcement Learning (DRL) based method for automated assembly planning in robot-based prefabricated construction. Specifically, a re-configurable simulator for assembly planning is developed based on a Building Information Model (BIM) and an open game engine, which could support the training and testing of various optimization methods. Furthermore, the assembly planning problem is modelled as a Markov Decision Process (MDP) and a set of DRL algorithms are developed and trained using the simulator. Finally, experimental case studies in four typical scenarios are conducted, and the performance of our proposed methods have been verified, which can also serve as benchmarks for future research works within the community of automated construction. Note to Practitioners—This paper is conducted based on the comprehensive analysis of real-life assembly planning processes in prefabricated construction, and the methods proposed could bring many benefits to practitioners. Firstly, the proposed simulator could be easily re-configured to simulate diverse scenarios, which can be used to evaluate and verify the operations’ optimization methods and new construction technologies. Secondly, the proposed DRL-based optimization methods can be directly adopted in various robot-based construction scenarios, and can also be tailored to support the assembly planning in traditional human-based or human-robot construction environments. Thirdly, the proposed DRL methods and their performance in the four typical scenarios can serve as benchmarks for proposing new advanced construction technologies and optimization methods in assembly planning

A MIG Welding Clamping Scheme for Power Battery Enclosure’s Deformation Restrain

Aiming at the inward shrinkage between the frame and the bottom plate of the power battery enclosure after MIG (melt inert-gas) welding, a welding clamping scheme with hook-pull devices was designed. By adjusting the clamping force of the hook-pull device, the MIG welding deformation force was counteracted, so the inward shrinkage deformation of the frame after welding was restrained. To obtain the deformation force during the MIG welding, the welding simulation model of power battery enclosure was established. Gaussian heat source model was selected as temperature load. Through the numerical simulation and experimental analysis of the temperature field, the numerical simulation results are in good agreement with the measured thermal cycle curve in terms of temperature value and change trend. The error between numerical simulation result curve and measured thermal cycle curve at the measurement point is no more than 10%, which can meet the simulation requirements. Based on the simulation model and the load of the temperature field, deformation force curves were obtained by simulating the welding process. To counteract the MIG welding deformation force, a pneumatic servo control system of the welding clamp was designed, which can generate equal and reversed welding deformation force. The experiments show that the actual output force of the system has a tiny time delay and fluctuates with the varies of the pneumatic servo control system. The maximum fluctuation error is 6.96 N, which is within the permitted error range. The welding experiments were carried out to verify effectiveness of the control system and the welding clamp. The field results have shown that the maximum inward shrinkage deformation after welding is 0.6 mm, which is less than 1.2 mm required by the MIG welding process

Cloud asset management for urban flood control

Urban flooding, frequently occurring in metropolitan areas, is one of the worst natural disasters worldwide that brings severe impacts. Albeit a large number of measures have been designed for urban flood control, the ineffective and inefficiency management of physical assets involved has greatly impeded their deployment, and the performance of these assets has already become one of the major determinant success factors for urban flood control. However, managing physical assets in urban flood control is extremely challenging as there are a huge amount of diverse physical assets involved, which are distributed throughout the city, owned by different agencies, and operated in diverse scenarios. This research proposes an integrated platform for the physical asset management in urban flood control to improve its management effectiveness and efficiency, and finally guarantee the success of urban flood control. Through analyzing the physical assets involved and their management scenarios, a cloud-based asset management platform is proposed, which could manage all the physical assets throughout the whole process of urban flood control. Besides, the concept of cloud asset is introduced, with its technical architecture and corresponding management services. Cloud asset enables automatic data collection and remote control of physical assets, and makes the management of them much more flexible. Furthermore, as a typical application built upon the cloud-based platform, the lifecycle management system for cloud asset is also developed. Finally, a case on emergency fleet management is conducted to verify how the proposed asset management platform could benefit typical management activities in urban flood control. Several major contributions can be summarized in the following. Firstly, this work innovatively proposes an asset-centric approach for managing the physical assets in urban flood control. Meanwhile, to implement this idea into reality, an integrated cloud-based asset management platform is proposed by integrating various technologies. The platform could provide management services for all the physical assets involved, and be flexible enough to work in diverse scenarios of urban flood control. Secondly, the concept of cloud asset is first introduced in this research. It is the key enabler to realize asset-centric management in urban flood control. Meanwhile, this concept makes it possible for physical assets to work in diverse scenarios and be shared among agencies. Besides, cloud asset shifts the management of physical asset from managing its physical status and functions to managing its capabilities and services in the cloud, which is much more flexible and efficient. Thirdly, a data-centric lifecycle management system for cloud asset is proposed. It could not only extract rich lifecycle information from complex connections among diverse physical assets, but also well handle the data collisions caused by multiple data sources and the instability of networks. Fourthly, based on the advantages of cloud asset and its management platform, this research proposes a data-driven dynamic management mechanism for emergency fleet management in urban flood control. It enables real-time monitoring and controlling of various vehicles, and makes the management of emergency fleet resilient to cope with disruptions to sustain a required service level.published_or_final_versionIndustrial and Manufacturing Systems EngineeringDoctoralDoctor of Philosoph

Identifying Spatial–Temporal Characteristics and Significant Factors of Bus Bunching Based on an eGA and DT Model

Bus bunching is a common phenomenon caused by irregular bus headway, which increases the passenger waiting time, makes the passenger capacity uneven, and severely reduces the reliability of bus service. This paper clarified the process of bus bunching formation, analyzed the variation characteristics of bus bunching in a single day, in different types of periods, and at different bus stops, then concluded twelve potential factors. A hybrid model integrating a genetic algorithm with elitist preservation strategy (eGA) and decision tree (DT) was proposed. The eGA part constructs the model framework and transforms the factor identification into a problem of selecting the fittest individual from the population, while the DT part evaluates the fitness. Model verification and comparison were conducted based on real automatic vehicle location (AVL) data in Shenzhen, China. The results showed that the proposed eGA–DT model outperformed other frequently used single DT and extra tree (ET) models with at least a 20% reduction in MAE under different bus routes, periods, and bus stops. Six factors, including the sequence of the bus stop, the headway and dwell time at the previous bus stop, the travel time between bus stops, etc., were identified to have a significant effect on bus bunching, which is of great value for feature selection to improve the accuracy and efficiency of bus bunching prediction and real-time bus dispatching

Identifying Spatial–Temporal Characteristics and Significant Factors of Bus Bunching Based on an eGA and DT Model

Bus bunching is a common phenomenon caused by irregular bus headway, which increases the passenger waiting time, makes the passenger capacity uneven, and severely reduces the reliability of bus service. This paper clarified the process of bus bunching formation, analyzed the variation characteristics of bus bunching in a single day, in different types of periods, and at different bus stops, then concluded twelve potential factors. A hybrid model integrating a genetic algorithm with elitist preservation strategy (eGA) and decision tree (DT) was proposed. The eGA part constructs the model framework and transforms the factor identification into a problem of selecting the fittest individual from the population, while the DT part evaluates the fitness. Model verification and comparison were conducted based on real automatic vehicle location (AVL) data in Shenzhen, China. The results showed that the proposed eGA–DT model outperformed other frequently used single DT and extra tree (ET) models with at least a 20% reduction in MAE under different bus routes, periods, and bus stops. Six factors, including the sequence of the bus stop, the headway and dwell time at the previous bus stop, the travel time between bus stops, etc., were identified to have a significant effect on bus bunching, which is of great value for feature selection to improve the accuracy and efficiency of bus bunching prediction and real-time bus dispatching

An End-of-Line Test System for Pneumatic ABS Controllers in Commercial Vehicles

To remedy the defects of conventional pneumatic ABS (anti-lock Braking system) controller testing equipment for commercial vehicles, such as low efficiency and difficult testing condition configurations, a workflow was proposed based on the unit integration test mode. The relationship among the operation logic of the ABS valves, test conditions, and wheel speed was derived through the simulation of a multi-condition configuration. Then, a wheel speed simulation model was set up by taking the test conditions as inputs, and it was implanted into an embedded control unit. Next, the hardware module and the underlying drivers as well as an application algorithm for the application layer were designed. A prototype of the EOL (end-of-line) test system was built. The test results show that the prototype can not only achieve a rapid test condition configuration and accurate fault location but that it can also meet the requirements for test efficiency

A memetic algorithm for energy-efficient scheduling of integrated production and shipping

Energy-efficient manufacturing is critical as the industrial sector accounts for a substantial portion of global energy consumption. This research aims to address an energy-efficient scheduling problem of production and shipping for minimizing both makespan and energy consumption. It contributes to an integrated energy-efficient production and shipping system, which is separately studied in most existing research. The production stage allocates jobs onto unrelated parallel machines that can be shut off and adjust their cutting speed to save energy. The shipping stage aims to allocate jobs to vehicles of various sizes with varied unit energy consumption. The problem is modelled as a mixed-integer quadratic program. Considering its complexity, a memetic algorithm (MA) is proposed to incorporate a knowledge-driven local search strategy considering the balance between exploration and exploitation. Two dominance rules are derived from the characteristics of the specific problem and embedded into the proposed MA to enhance its performance. Experimental results demonstrate that the proposed MA outperforms two other population-based algorithms, genetic algorithm and traditional MA, in terms of performance and computing time. This research practically contributes to improving productivity and energy efficiency for the production-shipping supply chain of make-to-order products.Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [52075259, 51705250, 72174042, 71871117]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2021T140320, 2019M661839]</p

Cloud asset-enabled integrated IoT platform for lean prefabricated construction

Prefabricated construction has become increasingly popular over the recent years, given its benefits including higher construction speed, lower cost, and improved quality. To facilitate the operations of prefabricated construction, various technologies have in parallel been introduced. However, due to its project-based feature and the involvement of numerous Small and Medium Enterprises (SMEs), the adoption of information technologies is insufficient and varies between SMEs, thereby hindering the improvement of the efficiency of prefabricated construction. Considering these issues and aiming at realizing lean prefabricated construction, this paper proposes an integrated cloud-based Internet of Things (IoT) platform through exploiting the concept of cloud asset. Its operation model has also been worked out to enable SMEs to adopt IoT technologies economically and flexibly. Besides, to make the platform compatible and scalable on managing diverse physical assets in different companies and scenarios, a unified cloud asset data model is proposed. Furthermore, an IoT service-sharing module is developed to support different levels of service-sharing on the platform. Finally, a real-life prefabricated construction project in Hong Kong and several lab-based LEGO construction models are adopted to verify the feasibility and effectiveness of the proposed platform

Optimal pricing for ferry services with a new entrant: a game-theoretic perspective

This paper investigates the pricing model between an incumbent ferry firm and a new-entrant sea bus firm. First, we study the influences of sea buses entering the ferry market. Next, based on the differences in power structures, we analyze the impact of weather on both companies’ operations in Bertrand and two Stackelberg models, and we consider a case in which both firms belong to the same parent firm. Finally, we study the strategies adopted by the ferry firm to protect its market share against the invasion of sea buses. We find that the sea bus firm’s entrance into the ferry market will increase the number of passengers taking ferries. Both companies’ profits are greater in the two Stackelberg models than in the Bertrand model. The two companies’ profits in the Stackelberg models partially rely on the weather. Finally, vicious price competition will lead to losses on both sides