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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Rana Ashar Burhan Sadiq Hira Mohiuddin Saniya Ashraf Muhammad Imran Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan Department of Electronic Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Video Stabilization is the basic need for modern-day video capture. Many methods have been proposed throughout the years including 2D and 3D-based models as well as models that use optimization and deep neural networks. This work describes the implementation of cutting-edge Recurrent All-Pairs Field Transforms (RAFT) for optical flow estimation in video stabilization. we use a pipeline that accommodates the large motion. It then passes the results to the optical flow for better accuracy. After that, it satisfies the inaccuracies of the optical flow and makes it robust to occlusion, Parallax, and moving objects. Our approach yields better results (visually and quantitatively) compared to other optimization and deep learning-based visual stabilization techniques.

关键词： Industries Deep learning Visualization Pipelines Neural networks Estimation Transforms

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Odometry and Inertial Sensor-based Localization of a Snake Robot

Odometry and Inertial Sensor-based Localization of a Snake R...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Ramsha Shahid Anam Baloch Humayun Tahir Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan Department of Electronic Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Terrain adaptability gives snake robots an edge over wheeled mobile robots. Snake robot's applications expansion to disaster management is due to its limbless and modular design. Replicating the navigation and locomotion of a biological snake have been in the spotlight for many researchers. In this study, a robust localization algorithm of a snake robot is developed. A sensor fusion-based algorithm using probabilistic approaches: UKF (Unscented Kalman Filter) and EKF (Extended Kalman Filter) has been proposed. Odometry is fused with one and two IMUs (Inertial Measurement Units) with both probabilistic approaches. Evaluation of results in a simulation environment showed that the fusion of two IMUs and odometry using EKF outer performs in terms of accuracy when odometry is fused with one IMU using EKF. Furthermore, the fusion of two IMUs and odometry with UKF is computationally expensive resulting in a large convergence time which is not the best suitable approach that can be utilized for the snake robot.

关键词： Location awareness Industries Measurement units Snake robots Inertial navigation Disaster management Robot sensing systems

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Multilingual Transliteration based Urdu Chatbot using Rasa Framework

Multilingual Transliteration based Urdu Chatbot using Rasa F...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Hira Mohiuddin Bakhtiar Kasi Anayat Ullah Control Automotive and Robotics Lab National Center of Robotics and Automation Rawalpindi Pakistan Faculty of Information and Communication Technology Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Chatbots, which emulate human conversation, are meeting the rising demand for efficient customer service by reducing the need for manual intervention and alleviating the challenges of timeconsuming human interactions. This paper introduces Ubot, a domain-specific chatbot fluent in both Urdu and English transliteration, designed to reduce the need for human intervention and lower service costs. We address the challenge of low-resource languages by creating a dataset comprising 750 utterances, 28 intents, and 45 entities, validated by 36 participants. Ubot, powered by the Rasa framework, achieves remarkable accuracy in intent detection 94.1% and entity extraction 95.4%, offering efficient multilingual conversational AI solutions. Our approach holds promise for similar script-sharing languages such as Persian and Arabic, contributing to advanced language applications and intelligent virtual assistants.

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Exploration of Unknown Environment using Deep Reinforcement Learning

Exploration of Unknown Environment using Deep Reinforcement ...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Asad Ali Sarah Gul Tallat Mahmood Anayat Ullah Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) Rawalpindi Pakistan Department of Electrical Engineering Balochistan University of IT Engineering and Management Sciences Quetta Pakistan

Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods.

关键词： Deep learning Location awareness Industries Uncertainty Service robots Computational modeling Reinforcement learning

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur

Improving Road Safety With Enhanced Vehicle Detection for Ni...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Usama Younis Faisal Riaz Umar Raza Gokhan Atali Hamid Rabnawaz Control Automotive and Robotics Lab Affiliated Lab of National Centre of Robotics and Automation (NCRA) HEC Islamabad Pakistan Department of Engineering Manchester Metropolitan University Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya Turkey Selftech Pvt Ltd

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents a study on improving night-time vehicle detection in the Mirpur region through a modified neural network model. Given the challenges of low visibility at night, the research focused on optimizing a deep learning algorithm to enhance detection accuracy. A dataset of 10,000 images covering various vehicle types was collected and annotated to train and test the model. The study found a 7% increase in detection performance compared to the standard model, highlighting the potential for tailored algorithms in specific environmental conditions. The findings suggest significant implications for road safety and autonomous driving systems, particularly in regions with similar night-time driving challenges. The research contributes to the broader field of computer vision by showing the effectiveness of model optimization in real-world conditions.

关键词： Accuracy Computational modeling Vehicle detection Neural networks Road safety Reliability robotics and automation Optimization Autonomous vehicles Standards

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Fuzzy Logic Based Brake Failure Detection & Collision Avoidance Model for Automated Driving System

Fuzzy Logic Based Brake Failure Detection & Collision Avoida...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Amil Roohani Dar Faisal Riaz Department of CS&IT University of Kotli (AJ&K) Kotli AJ&K Pakistan Control Automotive and Robotics Lab Affiliated Lab of National Centre of Robotics and Automation (NCRA) HEC Islamabad Pakistan Department of Computer Science & Information Technology Mirpur University of Sceince & Technology (MUST) Mirpur Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

The manual and automated braking system is intended to prevent rear-end collisions. However, unforeseen malfunctions within the vehicle's system during braking could potentially cause accidents. Collision Warning (CW) systems are safety technologies in vehicles that aid drivers in preventing collisions. This paper introduces a fuzzy-assisted pre-warning system for brake testing that considers multiple factors contributing to brake failure, along with a post-failure CW algorithm tailored for heavy commercial vehicles. Simulations conducted using NetLogo demonstrate that accidents can be prevented during brake failure through appropriate adjustments in vehicle speed. The proposed collision avoidance system has been validated for its effectiveness, and the results are encouraging and enhance road user safety.

关键词： Robot sensing systems Software Road safety Safety Brakes Collision avoidance robotics and automation Accidents Vehicles Testing

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An Energy-Efficient Communication Protocol for Power-Constrained IoT Networks: A Deep Reinforcement Learning Approach

An Energy-Efficient Communication Protocol for Power-Constra...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Syed Asad Ullah Syed Muhammad Khalid Umair Ahmed Korai Anayat Ullah Department of Electronic Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) BUITEMS Quetta Pakistan Department of Telecommunication Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

Power-limited devices (or sensors) constrain the deployment of modern IoT networks, such as Next-Generation Industrial IoT (NG-IIoT). These networks are envisioned as the key enablers to facilitate connectivity to billions of devices for applications like smart industries, smart healthcare, etc. This paper presents an optimal up-link communication protocol for a power-limited unlicensed sensor operating among numerous licensed sensors communicating in a time division multiple access (TDMA)-based scheme. The transmission of the power-limited sensor is ensured by employing the non-orthogonal multiple access (NOMA) technique during the time slot of a licensed sensor. To ensure energy-efficient communication, we maximize the throughput of the power-limited sensor using a deep reinforcement learning (DRL) framework recognized as a combined experience replay deep deterministic policy gradient (CER-DDPG) algorithm. Our simulation results demonstrated that the CER-DDPG-based communication protocol outperforms the benchmark schemes, such as DDPG and stochastic algorithms, in terms of throughput.

关键词： Optical losses NOMA Protocols Simulation Reinforcement learning Throughput Energy efficiency

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Nonlinear Model Predictive control Approximation: Applications to Truck-Trailer control System

Nonlinear Model Predictive Control Approximation: Applicatio...

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International Conference on control, Automation and Systems ( ICCAS)

作者： Suyong Park Duc Giap Nguyen Yongsik Jin Jinrak Park Dohee Kim Jeong Soo Eo Kyoungseok Han Department of Automotive Engineering Hanyang University Seoul Republic of Korea Department of Mechanical Engineering Kyungpook National University Daegu Republic of Korea Robotics IT Convergence Research Section Electronics and Telecommunications Research Institute (ETRI) Daegu Republic of Korea Department of Electrified Systems Control Research Lab Hyundai Motor Company Hwaseong Republic of Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.

关键词： Training Tracking Simulation Training data Artificial neural networks Predictive models control systems Real-time systems Safety Predictive control

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Advanced Collision Warning and Management Strategies for Brake Failure in Commercial Vehicles

Advanced Collision Warning and Management Strategies for Bra...

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Frontiers of Information Technology (FIT)

作者： Amil Roohani Dar Faisal Riaz Sohail Razzaq Munam Ali Shah Sheeba Razzaq Department of CS&IT University of Kotli (AJ&K) Kotli AJ&K Pakistan Control Automotive and Robotics Lab Affiliated Lab of NCRA HEC Mirpur University of Sceince & Technology (MUST) Mirpur Pakistan Faculty of Information Technology Majin University College Muscat Sultanate of Oman Department of Computer Networks & Communication King Faisal University AlAhsa Suadi Arabia

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

关键词： Manuals Software Road safety Safety Brakes Collision avoidance Monitoring Vehicles Accidents Testing

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GFPNet: A deep network for learning shape completion in generic fitted primitives

arXiv

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arXiv 2020年

作者： Cocias, Tiberiu Razvant, Alexandru Grigorescu, Sorin Elektrobit Automotive Robotics Vision and Control Lab [ROVISLab Transilvania University of Brasov Romania

—In this paper, we propose an object reconstruction apparatus that uses the so-called Generic Primitives (GP) to complete shapes. A GP is a 3D point cloud depicting a generalized shape of a class of objects. To reconstruct the objects in a scene we first fit a GP onto each occluded object to obtain an initial raw structure. Secondly, we use a model-based deformation technique to fold the surface of the GP over the occluded object. The deformation model is encoded within the layers of a Deep Neural Network (DNN), coined GFPNet. The objective of the network is to transfer the particularities of the object from the scene to the raw volume represented by the GP. We show that GFPNet competes with state of the art shape completion methods by providing performance results on the ModelNet and KITTI benchmarking datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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