This paper aims to enlighten stakeholders about critical success factors (CSFs) in developing intelligent autonomous systems (IASs) by integrating artificial intelligence (AI) with robotics. It suggests a prioritization hierarchy model for building sustainable ecosystem for developing IASs.,This paper is based on the existing literature and on the opinion of 15 experts. All the experts have minimum of eight years of experience in AI and related technologies. The CSF theory is used as a theoretical lens and total interpretative structure modelling (TISM) is used for the prioritization of CSFs.,Developing countries like India could leverage IASs and associated technologies for solving different societal problems. Policymakers need to develop basic policies regarding data collection, standardized hardware, skilled manpower, funding and start-up culture that can act as building blocks in undertaking sustainable ecosystem for developing IASs and implementing national AI strategy. Clear-cut regulations need to be in place for the proper functioning of the ecosystem. Any technology that can function properly in India has better chances of working at the global level considering the size of the population.,This paper had all its experts from India only, and that makes the limitation of this paper, as there is a possibility that some of the factors identified may not hold same significance in other countries.,Stakeholders will understand the critical factors that are important in developing sustainable ecosystem for IASs and what should be the possible order of activities corresponding to each CSF.,The paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs. Further, eight significant factors, that is, emerging economy multinational enterprises (EMNEs), governance, utility, manpower, capital, software, data and hardware, have come up as the most important factors in integrating AI with robotics in India.

Critical success factors for integrating artificial intelligence and robotics

Military target detection technology is the foundation and key to perceive and analyze the battlefield situation, and it is also the premise of target tracking technology. Aiming at the task of military target detection, the detection performance of traditional detection algorithms is poor in complex environment. We realize automatic detection of military targets in complex environment through deep learning. In this research, we improve the components of YOLOv3 and propose a novel military target detection algorithm (YOLO-G). We have built a military target dataset composed of armed men with different weapons, which provides a test environment for various object detection algorithms. In the YOLOv3 network structure, by introducing the lightweight convolutional neural network GhostNet as the feature extraction network, the accuracy and speed of military target detection are improved. Then, the attention mechanism based on the coordinate attention block is introduced to enhance the representation ability of target features, suppress interference and improve the detection accuracy. Finally, the loss function of the target detector is redesigned by using DIOU loss function and Focal loss function, which further improves the detection accuracy of our detection model for military targets. We tested YOLO-G on the military target dataset. Experimental results show that our method improves the mAP by 2.9% and the detection speed by 25.9 frames/s compared with the original YOLOv3 algorithm, and the size of the proposed model is reduced to 1/6 of that of YOLOv3. In addition, we also compare our method with state-of-the-art object detection algorithms. The results show that YOLO-G also has superior detection performance, and the mAP index obtained by our method is 1.2% higher than that of the latest YOLOv5 on the premise of meeting the application requirements, which plays an important auxiliary role in the generation and analysis of battlefield situation. 

/pdf/yolo-g-a-lightweight-network-model-for-improving-the-1u4l4ntk.pdf

YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection

Military target detection technology is the basis and key for reconnaissance and command decision-making, as well as the premise of target tracking. Current military target detection algorithms involve many parameters and calculations, prohibiting deployment on the weapon equipment platform with limited hardware resources. Given the above problems, this paper proposes a lightweight military target detection method entitled SMCA-α-YOLOv5. Specifically, first, the Focus module is replaced with the Stem block to improve the feature expression ability of the shallow network. Next, we redesign the backbone network of YOLOv5 by embedding the coordinate attention module based on the MobileNetV3 block, reducing the network parameter cardinality and computations, thus improving the model’s average detection accuracy. Finally, we propose a power parameter loss that combines the optimizations of the EIOU loss and Focal loss, improving further the detection accuracy and convergence speed. According to the experimental findings, when applied to the self-created military target data set, the developed method achieves an average precision of 98.4% and a detection speed of 47.6 Frames Per Second (FPS). Compared with the SSD, Faster-RCNN, YOLOv3, YOLOv4, and YOLOv5 algorithms, the mAP values of the improved algorithm surpass the competitor methods by 8.3%, 9.9%, 2.1%, 1.6%, and 1.9%, respectively. Compared with the YOLOv5 algorithm, the parameter cardinality and computational burden are decreased by 85.7% and 95.6%, respectively, meeting mobile devices' military target detection requirements.

https://www.mdpi.com/2079-9292/11/20/3263/pdf?version=1666694327

A Lightweight Military Target Detection Algorithm Based on Improved YOLOv5

The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder.

/pdf/deep-learning-based-question-answering-system-for-134hfc3bzn.pdf

Deep learning-based question answering system for intelligent humanoid robot

Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain’s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.

https://www.mdpi.com/2504-2289/6/4/109/pdf?version=1666337621

Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

Indonesia is a country whose economy is developing very fast. According to World Bank data, Indonesia is the country with the largest economic development in Southeast Asia. Unfortunately, the threat of terrorism in Indonesia is very large, therefore it requires a smart system such as military robots. Today’s vision-based robot technology has also been used in the development of autonomous military robots. A sophisticated military robot is a robot that is needed by the military/police because it can be deployed to the battlefield or the eradication of terrorism in a remote or autonomous manner. This system is needed to reduce the remaining casualties from the army, and this combat robot system can also be operated at any time with more numbers than regular soldiers and with minimal operator needs. In this paper, we propose a prototype of a tank-based military robot with object detection and tracking and turrets for simulation of shooting the enemy target based on the computer vision. The object tracker will detect an upper body of the target using a library in OpenCV and the tank will track until it reaches the best position to shoot the target. The methods explained, and experimental results were presented.

Development of Tank-Based Military Robot and Object Tracker

Research and development on Intelligent humanoid robot for general purpose focuses on speech recognition systems to be able to interact with people naturally. In this research, we would like to propose humanoid robot with the self-learning capability based on Indonesian language and small vocabularies for accepting and giving response from people based on Natural Language Processing (NLP). This kind of robot can be used widely in schools, universities and public services. The humanoid robot should consider the style of questions and conclude the answer through conversation under unreliable automatic speech in noisy environment. In our scenario, robot will detect user’s face and accept commands from the user to do an action, where the answer from user will be processed to the Google Translator and the result will be compared with vocabularies defined on the system. We describe how this formulation establish a promising framework by empirical results using subjects randomly. The comparative experiments with samples from user were presented.

Anita: Intelligent Humanoid Robot with Self-Learning Capability Using Indonesian Language

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Deep learning-based question answering system for intelligent humanoid robot

Development of Tank-Based Military Robot and Object Tracker

Anita: Intelligent Humanoid Robot with Self-Learning Capability Using Indonesian Language