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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Ullah, Syed Izzat Mahmood, Tallat Anayatullah A&M University-Corpus Christi Department of Computing Sciences Texas Pakistan National Centre of Robotics and Automation Control Automotive and Robotics Lab Rawalpindi Pakistan

ISBN: (纸本)9781665464727

In an Urban Search and Rescue situation, under extreme time pressure, rescue workers have to locate and extract the trapped people in collapsed structures. Due to the lack of medical treatment, food, and water, the victim's mortality rate dramatically increases over time. Rescue operations for both rescue workers and victims might be as dangerous as the initial event. For such situations, snake robots which are inspired by their biological counterparts, are shown to be a good option in the literature, to help the rescue workers in positioning the victims or delivering life-saving drugs to extend the life of the victims for some time. However, current research mainly focuses on mechanical design, control mechanisms, and gait generation. To alleviate this concern, we have integrated state-of-the-art methods to develop an autonomous snake robot that can navigate in an unknown environment while also generating a 3D map, to provide a better idea of the environment to the rescue workers. A simulated maze environment is implemented and demonstrated by using the CoppeliaSim simulation, running on Robot Operation System (ROS) and Linux OS. The simulation result shows the effectiveness of the proposed autonomous navigation system for the snake robot to plan an obstacle-free path from the robot's current position to the goal position without an apriori knowledge of the environment. © 2023 IEEE.

关键词： Motion planning

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Video Stabilization using RAFT-based Optical Flow

Video Stabilization using RAFT-based Optical Flow

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

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

ISBN: (纸本)9781665464727

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. © 2023 IEEE.

关键词： Geometrical optics

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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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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

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

ISBN: (纸本)9781665464727

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. © 2023 IEEE.

关键词： Extended Kalman filters

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Multilingual Transliteration based Urdu Chatbot using Rasa Framework 17

Multilingual Transliteration based Urdu Chatbot using Rasa F...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

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

ISBN: (纸本)9798350381320

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. © 2023 IEEE.

关键词： Chatbot Dialogue Management Entities Intents Rasa Ubot

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Exploration of Unknown Environment using Deep Reinforcement Learning

Exploration of Unknown Environment using Deep Reinforcement ...

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Ali, Asad Gul, Sarah Mahmood, Tallat Ullah, Anayat Control Automotive and Robotics Lab Rawalpindi Pakistan Balochistan University of IT Engineering and Management Sciences Department of Electrical Engineering Quetta Pakistan

ISBN: (纸本)9781665464727

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. © 2023 IEEE.

关键词： Mobile robots

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An Energy-Efficient Communication Protocol for Power-Constrained IoT Networks: A Deep Reinforcement Learning Approach 6

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

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Ullah, Syed Asad Muhammad Khalid, Syed Korai, Umair Ahmed Ullah, Anayat Department of Electronic Engineering Quetta Pakistan Control Automotive and Robotics Lab Quetta Pakistan Mehran University of Engineering and Technology Department of Telecommunication Engineering Jamshoro Pakistan

ISBN: (纸本)9798350333718

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. © 2023 IEEE.

关键词： Internet of things

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Motion Planning for a Snake Robot using Double Deep Q-Learning

Motion Planning for a Snake Robot using Double Deep Q-Learni...

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2021 International Conference on Artificial Intelligence, ICAI 2021

作者： Khan, Semab Neimat Mahmood, Tallat Ullah, Syed Izzat Ali, Khawar Ullah, Anayat National Centre of Robotics and Automation Control Automotive and Robotics Lab Rawalpindi Pakistan

ISBN: (纸本)9781665432931

Motion planning for a snake robot in an unknown complex environment is a long-standing research problem because of the complex control of the modular mechanism. We propose deep reinforcement learning-based novel framework for motion planning. In this model-free framework, we propose a double deep Q-learning-based technique to learn the optimal policy for reaching the goal point from a random start point;in a minimum number of steps in various unknown environments. In this approach, the agent learns to minimize the distance between the current and goal positions by aligning its yaw angle to the goal points through controlling multiple locomotive gaits. For experimental evaluation, we trained and tested the model in obstacle-free terrains. For training, we selected the model on the mud-terrain and tested for 50 episodes on five different terrains concrete, default, metallic, mud, and wooden. From simulation results, we observe the learned-optimal policy shows promising results for all unknown environments with a performance efficiency of 100% for all terrains except the Wooden-terrain Where it fails for only one episode and achieves 98% efficiency. © 2021 IEEE.

关键词： Motion planning

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur 6

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

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6th International Conference on robotics and Automation in Industry, ICRAI 2024

作者： Younis, Usama Riaz, Faisal Raza, Umar Atali, Gokhan Rabnawaz, Hamid HEC Control Automotive and Robotics Lab Islamabad Pakistan Manchester Metropolitan University Department of Engineering Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya54050 Turkey Selftech Pvt Ltd Pakistan

ISBN: (纸本)9798350355703

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. © 2024 IEEE.

关键词： Vehicle safety

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Autonomous Navigation and Mapping of Snake Robot for Urban Search and Rescue

Autonomous Navigation and Mapping of Snake Robot for Urban S...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Syed Izzat Ullah Tallat Mahmood Anayatullah Department of Computing Sciences Texas A&M University-Corpus Christi Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan

关键词： Three-dimensional displays Simultaneous localization and mapping Navigation Service robots Linux Simulation Snake robots

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