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

Component-based robot prefabricated construction simulation using IFC-based building information models

Despite the widespread usage of Building Information Modeling (BIM) based simulation in the construction industry, robot construction simulation environments are built on particular modeling tools and can only be simulated with certain robot models. This not only limits construction robot development and also makes it difficult to directly share building information with robots, which impedes the study of robotic construction. To overcome this problem, we propose a new framework for robotic construction simulation which integrates open standard formats and tools to achieve a commonly used robot simulation environment. This provides a new approach to automatically convert IFC to simulation description format (SDF) and generate building components as smart components, which eliminates platform-specific limitations on robotic construction simulation, which further expands the utilization of BIM models

Smart component-oriented method of construction robot coordination for prefabricated housing

Although achievements have been made in research on robotic construction for specific construction tasks, robots are still not capable of working together to accomplish multiple construction tasks. To achieve this goal it is necessary to study how to realize robot coordination in prefabricated construction. In this paper, we propose a component-oriented robot construction approach. Using the smart construction object (SCO) approach, diverse construction tasks are assigned to robots by assigning state and requirements to the components to drive multiple robots for the assembly of prefabricated housing. Within a prototype BIM simulation environment, we implemented multiple different robots to complete the construction of a steel frame based on the SCOs. For more realistic robot-base construction design, the next step is the introduction of more complicated BIM models and more accurate robot models to enable collaborative simulation of a wider variety of prefabricated construction processes

Ab initio calculations of the formation energies of lithium intercalations in SnSb

SnSb has attracted a great attention in recent investigations as an anode material for Li ion batteries. The formation energies and electronic properties of the Li intercalations in SnSb have been calculated within the framework of local density functional theory and the first-principles pseudopotential technique. The changes of volumes, band structures, charge density analysis and the electronic density of states for the Li intercalations are presented. The results show that the average Li intercalation formation energy per Li atom is around 2.7 eV

Formation energies of the lithium intercalations in MoS2

First-principles calculations have been performed to study the lithium intercalations in MoS2. The formation energies, changes of volumes, electronic structures and charge densities of the lithium intercalations in MoS2 are presented. Our calculations show that during lithium intercalations in MoS2, the lithium intercalation formation energies per lithium atom are between 2.5 eV to 3.0 eV. The volume expansions of MoS2 due to lithium intercalations are relatively small

Predicting Treatment Response of Breast Cancer to Neoadjuvant Chemotherapy Using Ultrasound-Guided Diffuse Optical Tomography

PURPOSE: To prospectively investigate ultrasound-guided diffuse optical tomography (US-guided DOT) in predicting breast cancer response to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Eighty-eight breast cancer patients, with a total of 93 lesions, were included in our study. Pre– and post–last chemotherapy, size and total hemoglobin concentration (THC) of each lesion were measured by conventional US and US-guided DOT 1 day before biopsy (time point t0, THC THC0, SIZE S0) and 1 to 2 days before surgery (time point tL, THCL, SL). The relative changes in THC and SIZE of lesions after the first and last NAC cycles were considered as the variables ΔTHC and ΔSIZE. Receiver operating characteristic curve was performed to calculate ΔTHC and ΔSIZE cutoff values to evaluate pathologic response of 93 breast cancers to NAC, which were then prospectively used to predicate response of 61 breast cancers to NAC. RESULTS: The cutoff values of ΔTHC and ΔSIZE for evaluation of breast cancers NAC treatment response were 23.9% and 42.6%. At ΔTHC 23.9%, the predicted treatment response in 61 breast lesions for the time points t1 to t3 was calculated by area under the curve (AUC), which were AUC1 0.534 (P = .6668), AUC2 0.604 (P = .1893), and AUC3 0.674(P =. 0.027), respectively; for ΔSIZE 42.6%, at time points t1 to t3, AUC1 0.505 (P = .9121), AUC2 0.645 (P = .0115), and AUC3 0.719 (P = .0018). CONCLUSION: US-guided DOT ΔTHC 23.9% and US ΔSIZE 42.6% can be used for the response evaluation and earlier prediction of the pathological response after three rounds of chemotherapy

Antiproliferative Effect of Androgen Receptor Inhibition in Mesenchymal Stem-Like Triple-Negative Breast Cancer

Background/Aims: Androgen receptor (AR), a steroid hormone receptor, has recently emerged as prognostic and treatment-predictive marker in breast cancer. Previous studies have shown that AR is widely expressed in up to one-third of triple-negative breast cancer (TNBC). However, the role of AR in TNBC is still not fully understood, especially in mesenchymal stem-like (MSL) TNBC cells. Methods: MSL TNBC MDA-MB-231 and Hs578T breast cancer cells were exposed to various concentration of agonist 5-α-dihydrotestosterone (DHT) or nonsteroidal antagonist bicalutamide or untreated. The effects of AR on cell viability and apoptosis were determined by MTT assay, cell counting, flow cytometry analysis and protein expression of p53, p73, p21 and Cyclin D1 were analyzed by western blotting. The bindings of AR to p73 and p21 promoter were detected by ChIP assay. MDA-MB-231 cells were transplanted into nude mice and the tumor growth curves were determined and expression of AR, p73 and p21 were detected by Immunohistochemistry (IHC) staining after treatment of DHT or bicalutamide. Results: We demonstrate that AR agonist DHT induces MSL TNBC breast cancer cells proliferation and inhibits apoptosis in vitro. Similarly, activated AR significantly increases viability of MDA-MB-231 xenografts in vivo. On the contrary, AR antagonist, bicalutamide, causes apoptosis and exerts inhibitory effects on the growth of breast cancer. Moreover, DHT-dependent activation of AR involves regulation in the cell cycle related genes, including p73, p21 and Cyclin D1. Further investigations indicate the modulation of AR on p73 and p21 mediated by direct binding of AR to their promoters, and DHT could make these binding more effectively. Conclusions: Our study demonstrates the tumorigenesis role of AR and the inhibitory effect of bicalutamide in AR-positive MSL TNBC both in vitro and in vivo, suggesting that AR inhibition could be a potential therapeutic approach for AR-positive TNBC patients