Alumni`s Testimonials
I learned from the serious yet enjoyable teamwork that no matter what limitations you may have, no unreachable goal ever exists in life.
ARAS for me is where I started my research career, fell in love with it, made a few good friends, saw real robots for the first time, and learned a lot.
ARAS is a great part of my academic experience. Very glad that I had the opportunity to work with great researchers in the field, participated in cool robotic competitions, and got a deep understanding of robotic theory.
ARAS is a great part of my academic experience. Very glad that I had the opportunity to work with great researchers in the field, participated in cool robotic competitions, and got a deep understanding of robotic theory.
Title | Abstract | Year | Type | Research Group | |
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Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network F Lotfi, HD Taghirad arXiv preprint arXiv:2103.15105 | Abstract: Object tracking becomes critical especially when similar objects are present in the same area. Recent state-of-the-art (SOTA) approaches are proposed based on taking a matching network with a heavy structure to distinguish the target from other objects in the area which indeed drastically downgrades the performance of the tracker in terms of speed. Besides, several candidates are considered and processed to localize the intended object in a region of interest for each frame which is time-consuming. In this article, a special architecture is proposed based on which in contrast to the previous approaches, it is possible to identify the object location in a single shot while taking its template into account to distinguish it from the similar objects in the same area. In brief, first of all, a window containing the object with twice the target size is considered. | 2021 | Preprint | Autonomous Robotics | |
Object Localization Through a Single Multiple-Model Convolutional Neural Network with a Specific Training Approach F Lotfi, F Faraji, HD Taghirad arXiv preprint arXiv:2103.13339 | Abstract: Object localization has a vital role in any object detector, and therefore, has been the focus of attention by many researchers. In this article, a special training approach is proposed for a light convolutional neural network (CNN) to determine the region of interest (ROI) in an image while effectively reducing the number of probable anchor boxes. Almost all CNN-based detectors utilize a fixed input size image, which may yield poor performance when dealing with various object sizes. | 2021 | Preprint | Autonomous Robotics | |
A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars F Lotfi, HD Taghirad arXiv preprint arXiv:2103.13430 | Abstract: Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside an OpenCV tracker. Subsequently, to obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation (SDRE) filter and a newly developed observation model. | 2021 | Preprint | Autonomous Robotics | |
Exploring Self-Attention for Visual Odometry Hamed Damirchi, Rooholla Khorrambakht, Hamid D. Taghirad arXiv preprint arXiv:2011.08634 | Abstract: Visual odometry networks commonly use pretrained optical flow networks in order to derive the ego-motion between consecutive frames. The features extracted by these networks represent the motion of all the pixels between frames. However, due to the existence of dynamic objects and texture-less surfaces in the scene, the motion information for every image region might not be reliable for inferring odometry due to the ineffectiveness of dynamic objects in derivation of the incremental changes in position. Recent works in this area lack attention mechanisms in their structures to facilitate dynamic reweighing of the feature maps for extracting more refined egomotion information. In this paper, we explore the effectiveness of self-attention in visual odometry. We report qualitative and quantitative results against the SOTA methods. Furthermore, saliency-based studies alongside specially designed experiments are utilized to investigate the effect of self-attention on VO. Our experiments show that using self-attention allows for the extraction of better features while achieving a better odometry performance compared to networks that lack such structures. | 2020 | Preprint | Autonomous Robotics | |
ARC-Net: Activity Recognition Through Capsules Hamed Damirchi, Rooholla Khorrambakht, Hamid Taghirad arXiv preprint arXiv:2007.03063 | Abstract: Human Activity Recognition (HAR) is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions through the iterative mechanism embedded in capsule networks. We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network. By using the proposed network, we were able to increase the accuracy of the state-of-the-art approaches by 2%. Furthermore, we investigate the directionality of the confusion matrices of our results and discuss the specificity of the activities based on the provided data. | 2020 | Preprint | Autonomous Robotics | |
Preintegrated IMU Features For Efficient Deep Inertial Odometry R. Khorrambakht, H. Damirchi, H. D. Taghirad arXiv preprint arXiv:2007.02929 | Abstract: MEMS Inertial Measurement Units (IMUs) are inexpensive and effective sensors that provide proprioceptive motion measurements for many robots and consumer devices. However, their noise characteristics and manufacturing imperfections lead to complex ramifications in classical fusion pipelines. While deep learning models provide the required flexibility to model these complexities from data, they have higher computation and memory requirements, making them impractical choices for low-power and embedded applications. This paper attempts to address the mentioned conflict by proposing a computationally, efficient inertial representation for deep inertial odometry. Replacing the raw IMU data in deep Inertial models, preintegrated features improves the model's efficiency. The effectiveness of this method has been demonstrated for the task of pedestrian inertial odometry, and its efficiency has been shown through its embedded implementation on a microcontroller with restricted resources. | 2020 | Preprint | Autonomous Robotics | |
A switched SDRE filter for state of charge estimation of lithium-ion batteries Faraz Lotfi, Saeedeh Ziapour, Farnoosh Faraji, Hamid D. Taghirad International Journal of Electrical Power & Energy Systems | Abstract: Lithium-ion (Li-ion) batteries need very precise monitor of the state of charge (SOC) to ensure a long cycle life. | 2020 | Journal | Autonomous Robotics | |
A New Approach To Estimate Depth Of Cars Using A Monocular Image SMA Tousi, J Khorramdel, F Lotfi, AH Nikoofard, AN Ardekani 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS) | Abstract: In this paper,Predicting scene depth from RGB images is a challenging task. Since the cameras are the most available, least restrictive and cheapest source of information for autonomous vehicles; in this work, a monocular image has been used as the only source of data to estimate the depth of the car within the frontal view. | 2020 | Journal | Autonomous Robotics | |
Surgical Instrument Tracking for Vitreo-retinal Eye Surgical Procedures Using ARAS-EYE Dataset F Lotfi, P Hasani, F Faraji, M Motaharifar, HD Taghirad, SF Mohammadi 28th Iranian Conference on Electrical Engineering (ICEE) | Abstract: Real-time instrument tracking is an essential element of minimally invasive surgery and has several applications in computer-assisted analysis and interventions | 2020 | Conference | Autonomous Robotics | |
ARC-Net: Activity Recognition Through Capsules Hamed Damirchi, Rooholla Khorrambakht, Hamid Taghirad 19th IEEE International Conference on Machine Learning and Applications (ICMLA) | Abstract: Human Activity Recognition (HAR) is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions through the iterative mechanism embedded in capsule networks. We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network. By using the proposed network, we were able to increase the accuracy of the state-of-the-art approaches by 2%. Furthermore, we investigate the directionality of the confusion matrices of our results and discuss the specificity of the activities based on the provided data. | 2020 | Conference | Autonomous Robotics | |
System identification and H-infinity based control of quadrotor attitude Ali Noormohammadi-Asl, Omid Esrafilian, Mojtaba Ahangar Arzati, Hamid D. Taghirad Arxiv Optimization and Control | Abstract: The attitude control of a quadrotor is a fundamental problem, which has a pivotal role in a | 2019 | Journal | Autonomous Robotics | |
Position Estimation for Drones based on Visual SLAM and IMU in GPS-denied Environment Hamid Didari Khamseh Motlagh, Faraz Lotfi, Saeed Bakhshi Germi, Hamid D.Taghirad International Conference on Robotics and Mechatronics | Due to the increased rate of drone usage in various commercial and industrial fields, the need for their autonomous operation is rapidly increasing. One major aspect of autonomous movement is the ability to operate safely in an unknown environment. The majority of current works are persistently using a global positioning system (GPS) to directly find the absolute position of the drone. However, GPS accuracy might be not suitable in some applications and this solution is not applicable to all situations. In this paper, a positioning system based on monocular SLAM and inertial measurement unit (IMU) is presented. The position is calculated through the semi-direct visual odometry (SVO) method alongside IMU data, and is integrated with an extended Kalman filter (EKF) to enhance the efficiency of the algorithm. The data is then employed to control the drone without any requirement to any source of external input. The experiment results for longdistance flying paths is very promising | 2019 | Conference | Autonomous Robotics | |
Path Planning for a UAV by Considering Motion Model Uncertainty Hossein Sheikhi Darani, Ali Noormohammadi-Asl and Hamid D. Taghirad International Conference on Robotics and Mechatronics | Abstract: The primary purpose of path planning for unmanned aerial vehicles (UAVs), which is a necessary prerequisite toward an autonomous UAV, is to guide the robot to | 2019 | Conference | Autonomous Robotics | |
Robust Object Tracking Based on Recurrent Neural Networks F. Lotfi, V. Ajallooeian and H. D. Taghirad 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM) | Abstract: Object tracking through image sequences is one of the important components of many vision systems, and it has numerous applications in driver assistance systems such as pedestrian collision avoidance or collision mitigating systems. Blurred images produced by a rolling shutter camera or occlusions may easily disturb the object tracking system. In this article, a method based on convolutional and recurrent neural networks is presented to further enhance the performance and robustness of such trackers. It is proposed to use a convolutional neural network to detect an intended object and feed the tracker with found image. Moreover, by using this structure the tracker is updated every ' n ' frames. A recurrent neural network is designed to learn the object behavior for estimating and predicting its position in blurred frames or when it is occluded behind an obstacle. Real-time implementation of the proposed approach verifies its applicability for improvement of the trackers performance. | 2018 | Conference | Autonomous Robotics | |
Multi-goal motion planning using traveling salesman problem in belief space Ali Noormohammadi-Asl, Hamid D. Taghirad Information Sciences | Abstract: In this paper, the multi-goal motion planning problem of an environment with some background information about its map is addressed in detail. The motion planning goal is to find a policy in belief space for the robot to traverse through a number of goal points. This problem is modeled as an asymmetric traveling salesman problem (TSP) in the belief space using Partially Observable Markov Decision Process (POMDP) framework. Then, feedback-based information roadmap (FIRM) algorithm is utilized to reduce the computational burden and complexity. By generating a TSP-FIRM graph, the search policy is obtained and an algorithm is proposed for online execution of the policy. Moreover, approaches to cope with challenges such as map updating, large deviations and high uncertainty in localization, which are more troublesome in a real implementation, are carefully addressed. Finally, in order to evaluate applicability and performance of the proposed algorithms, it is implemented in a simulation environment as well as on a physical robot in which some challenges such as kidnapping and discrepancies between real and computational models and map are examined. | 2018 | Journal | Autonomous Robotics | |
Implementation of Multi-Goal Motion Planning Under Uncertainty on a Mobile Robot Ali Noormohammadi-Asl, Hamid D. Taghirad, Amirhossein Tamjidi 2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM) | Abstract: Multi-goal motion planning under motion and sensor uncertainty is the problem of finding a reliable policy for visiting a set of goal points. In this paper, the problem is formulated as a formidable traveling salesman problem in the belief space. To solve this intractable problem, we propose an algorithm to construct a TSP-FIRM graph which is based on the feedback-based information roadmap (FIRM) algorithm. Also, two algorithms are proposed for the online planning of the obtained policy in the offline mode and overcoming changes in the map of the environment. Finally, we apply the algorithms on a physical nonholonomic mobile robot in the presence of challenging situations like the discrepancy between the real and computation model, map updating and kidnapping. | 2017 | Conference | Autonomous Robotics | |
Reconstruction of B-spline curves and surfaces by adaptive group testing Alireza Norouzzadeh Ravari, Hamid D. Taghirad Computer-Aided Design | Abstract: Point clouds as measurements of 3D sensors have many applications in various fields such as object modeling, environment mapping and surface representation. Storage and processing of raw point clouds is time consuming and computationally expensive. In addition, their high dimensionality shall be considered, which results in the well known curse of dimensionality. Conventional methods either apply reduction or approximation to the captured point clouds in order to make the data processing tractable. B-spline curves and surfaces can effectively represent 2D data points and 3D point clouds for most applications. Since processing all available data for B-spline curve or surface fitting is not efficient, based on the Group Testing theory an algorithm is developed that finds salient points sequentially. The B-spline curve or surface models are updated by adding a new salient point to the fitting process iteratively until the Akaike Information Criterion (AIC) is met. Also, it has been proved that the proposed method finds a unique solution so as what is defined in the group testing theory. From the experimental results the applicability and performance improvement of the proposed method in relation to some state-of-the-art B-spline curve and surface fitting methods, may be concluded. | 2016 | Journal | Autonomous Robotics | |
NURBS-based Representation of Urban Environments for Mobile Robots Alireza Norouzzadeh Ravari and Hamid D. Taghirad 2016 4th International Conference on Robotics and Mechatronics (ICROM) | Abstract: Representation of the surrounding environment is a vital task for a mobile robot. Many applications for mobile robots in urban environments may be considered such as self-driving cars, delivery drones or assistive robots. In contrast to the conventional methods, in this paper a Non Uniform Rational B-Spline (NURBS) based technique is represented for 3D mapping of the surrounding environment. While in the state of the art techniques, the robot's environment is expressed in a discrete space, the proposed method is mainly developed for representation of environment in a continuous space. Exploiting the information theory, the generated representation has much lower complexity and more compression capability in relation to some state of the art techniques. In addition to representation in a lower dimensional space, the NURBS based representation is invariant against 3D geometric transformations. Furthermore, the NURBS based representation can be employed for obstacle avoidance and navigation. The applicability of the proposed algorithm is investigated in some urban environments through some publicly available data sets. It has been shown by some experiments that the proposed method has better visual representation and much better data compression compared to some state-of-the-art methods. | 2016 | Conference | Autonomous Robotics | |
Autonomous Flight and Obstacle Avoidance of a Quadrotor By Monocular SLAM Omid Esrafilian and Hamid D. Taghirad 2016 4th International Conference on Robotics and Mechatronics (ICROM) | Abstract: In this paper, a monocular vision based autonomous flight and obstacle avoidance system for a commercial quadrotor is presented. The video stream of the front camera and the navigation data measured by the drone is sent to the ground station laptop via wireless connection. Received data processed by the vision based ORB-SLAM to compute the 3D position of the robot and the environment 3D sparse map in the form of point cloud. An algorithm is proposed for enrichment of the reconstructed map, and furthermore, a Kalman Filter is used for sensor fusion. The scaling factor of the monocular slam is calculated by the linear fitting. Moreover, a PID controller is designed for 3D position control. Finally, by means of the potential field method and Rapidly exploring Random Tree (RRT) path planning algorithm, a collision-free road map is generated. Moreover, experimental verifications of the proposed algorithms are reported. | 2016 | Conference | Autonomous Robotics | |
A Navigation System for Autonomous Robot Operating in Unknown and Dynamic Environment: Escaping Algorithm F. AdibYaghmaie, A. Mobarhani, H. D. Taghirad International Journal of Robotics | Abstract: In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in unknown environment and builds the map through SLAM procedure. Since the moving objects' location and properties are unknown, they are identified and tracked by Kalman filter. Kalman observer provides important information about next paths of moving objects which are employed in finding collision point and time in future. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads the robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. Besides, comparison between Escaping Algorithm and Probabilistic Velocity Obstacle, based on computational complexity and required steps for finishing the mission is provided in this paper. The results show Escaping Algorithm outperforms PVO in term of dynamic obstacle avoidance and complexity as a practical method for autonomous navigation. | 2016 | Journal | Autonomous Robotics | |
Reconstruction of B-spline curves and surfaces by adaptive group testing Alireza Norouzzadeh Ravari, Hamid D. Taghirad Computer-Aided Design | Abstract: Point clouds as measurements of 3D sensors have many applications in various fields such as object modeling, environment mapping and surface representation. Storage and processing of raw point clouds is time consuming and computationally expensive. In addition, their high dimensionality shall be considered, which results in the well known curse of dimensionality. Conventional methods either apply reduction or approximation to the captured point clouds in order to make the data processing tractable. B-spline curves and surfaces can effectively represent 2D data points and 3D point clouds for most applications. Since processing all available data for B-spline curve or surface fitting is not efficient, based on the Group Testing theory an algorithm is developed that finds salient points sequentially. The B-spline curve or surface models are updated by adding a new salient point to the fitting process iteratively until the Akaike Information Criterion (AIC) is met. Also, it has been proved that the proposed method finds a unique solution so as what is defined in the group testing theory. From the experimental results the applicability and performance improvement of the proposed method in relation to some state-of-the-art B-spline curve and surface fitting methods, may be concluded. | 2015 | Journal | Autonomous Robotics | |
Loop Closure Detection by Compressed Sensing for Exploration of Mobile Robots in Outdoor Environments Alireza Norouzzadeh Ravari and Hamid D. Taghirad 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM) | Abstract: In the problem of simultaneously localization and mapping (SLAM) for a mobile robot, it is required to detect previously visited locations so the estimation error shall be reduced. Sensor observations are compared by a similarity metric to detect loops. In long term navigation or exploration, the number of observations increases and so the complexity of the loop closure detection. Several techniques are proposed in order to reduce the complexity of loop closure detection. Few algorithms have considered the loop closure detection from a subset of sensor observations. In this paper, the compressed sensing approach is exploited to detect loops from few sensor measurements. In the basic compressed sensing it is assumed that a signal has a sparse representation is a basis which means that only a few elements of the signal are non-zero. Based on the compressed sensing approach a sparse signal can be recovered from few linear noisy projections by l1 minimization. The difference matrix which is widely used for loop detection has a sparse structure, where similar observations are shown by zero distance and different locations are indicated by ones. Based on the multiple measurement vector technique which is an extension of the basic compressed sensing, the loop closure detection is performed by comparison of few sensor observations. The applicability of the proposed algorithm is investigated in some outdoor environments through some publicly available data sets. It has been shown by some experiments that the proposed method can detect loops effectively. | 2015 | Conference | Autonomous Robotics | |
Modified Fast-SLAM For 2D Mapping And 3D Localization Soheil Gharatappeh, Mohammad Ghorbanian, Mehdi Keshmiri, Hamid D. Taghirad 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM) | Abstract: Fast Simultaneous Localization and Mapping (SLAM) algorithm is capable of real-time implementation due to logarithmic time complexity which results in decrease of computational cost. In this algorithm state vector of a robot merely includes planar location of the robot and its angle to the horizontal plane. It has fewer components comparing to state vector in extended Kalman filter method which consists of location of all environmental features. In existing methods for implementing this algorithm, robot movement is considered to be totally in planar movement; while if moving on a slope changes the pitch angle of the robot, it causes errors in the algorithm. Correcting these errors will lead to a precise 2D mapping and 3D localization. This paper details the modification added to conventional Fast-Slam algorithm to accommodate this requirement by using an IMU. Simulation and experimental results shows the effectiveness of such modification. | 2015 | Conference | Autonomous Robotics | |
3D Scene and Object Classification Based on Information Complexity of Depth Data A. Norouzzadeh, H. D. Taghirad Mathematics | Abstract: In this paper the problem of 3D scene and object classification from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic information theory, a new definition for the Kolmogorov complexity is presented based on the Earth Moverâs Distance (EMD). Finally the classification of 3D scenes and objects is accomplished by means of a normalized complexity distance, where its applicability in practice is proved by some experiments on publicly available datasets. Also, the experimental results are compared to some state-of-the-art 3D object classification methods. Furthermore, it has been shown that the proposed method outperforms FAB-Map 2.0 in detecting loop closures, in the sense of the precision and recall. | 2015 | Journal | Autonomous Robotics | |
Transformation Invariant 3D Object Recognition Based On Information Complexity Alireza Norouzzadeh Ravari and Hamid D. Taghirad 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) | Abstract: The 3D representation of objects and scenes as a point cloud or range image has been made simple by means of sensors such as Microsoft Kinect, stereo camera or laser scanner. Various tasks, such as recognition, modeling and classification can not be performed on raw measurements because of the curse of high dimensionality, computational and algorithm complexity. Non Uniform Rational Basis Splines (NURBS) are a widely used representation technique for 3D objects in various robotics and Computer Aided Design (CAD) applications. In this paper, a similarity measurement from information theory is employed in order to recognize an object sample from a set of objects. From a NURBS model fitted to the observed point cloud, a complexity based representation is derived which is transformation invariant in the sense of Kolmogorov complexity. Experimental results on a set of 3D objects grabbed by a Kinect sensor indicates the applicability of the proposed method for object recognition tasks. Furthermore, the results of the proposed method is compared to that of some state of the art algorithms. | 2014 | Conference | Autonomous Robotics | |
An Online Implementation of Robust RGB-D SLAM M. A. Athari, H. D. Taghirad 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) | Abstract: This paper presents an online robust RGB-D SLAM algorithm which uses an improved switchable constraints robust pose graph slam alongside with radial variance based hash function as the loop detector. The switchable constraints robust back-end is improved by initialization of its weights according to information matrix of the loops and is validated using real world datasets. The radial variance based hash function is combined with an online image to map comparison to improve accuracy of loop detection. The whole algorithm is implemented on K. N. Toosi University mobile robot with a Microsoft Kinect camera as the RGB-D sensor and the whole algorithm is validated using this robot, while the map of the environment is generated in an online fashion. | 2014 | Conference | Autonomous Robotics | |
An Improved Optimization Method for iSAM2 Rana Talaei Shahir and Hamid D. Taghirad 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) | Abstract: There is an issue called maximum likelihood estimation in SLAM that corresponds to a nonlinear least-square problem. It is expected to earn an accurate solution for large-scale environments with high speed of convergence. Although all the applied optimization methods might be accepted in terms of accuracy and speed of convergence for small datasets, their solutions for large-scale datasets are often far from the ground truth. In this paper, a double Dogleg trust region method is proposed and adjusted with iSAM2 to level up performance and accuracy of the algorithm especially in large-scale datasets. Since the trust region methods are sensitive to their own parameters, Gould parameters are chosen to obtain better performance. Simulations are done on some large-scale datasets and the results indicate that the proposed method is more efficient compared to the conventional iSAM2 algorithm. | 2014 | Conference | Autonomous Robotics | |
Loop Closure Detection By Algorithmic Information Theory: Implemented On Range And Camera Image Data Alireza Norouzzadeh Ravari and Hamid D. Taghirad IEEE Transactions on Cybernetics | Abstract: In this paper the problem of loop closing from depth or camera image information in an unknown environment is investigated. A sparse model is constructed from a parametric dictionary for every range or camera image as mobile robot observations. In contrast to high-dimensional feature-based representations, in this model, the dimension of the sensor measurements' representations is reduced. Considering the loop closure detection as a clustering problem in high-dimensional space, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In this paper, a representation is developed from a sparse model of images, with a lower dimension than original sensor observations. Exploiting the algorithmic information theory, the representation is developed such that it has the geometrically transformation invariant property in the sense of Kolmogorov complexity. A universal normalized metric is used for comparison of complexity based representations of image models. Finally, a distinctive property of normalized compression distance is exploited for detecting similar places and rejecting incorrect loop closure candidates. Experimental results show efficiency and accuracy of the proposed method in comparison to the state-of-the-art algorithms and some recently proposed methods. | 2014 | Journal | Autonomous Robotics | |
An intelligent UFastSLAM with MCMC move step Ramazan Havangi, Mohammad Ali Nekoui, Hamid D. Taghirad and Mohammad Teshnehlab Advanced Robotics | Abstract: FastSLAM is a framework for simultaneous localization and mapping (SLAM). However, FastSLAM algorithm has two serious drawbacks, namely the linear approximation of nonlinear functions and the derivation of the Jacobian matrices. For solving these problems, UFastSLAM has been recently proposed. However, UFastSLAM is inconsistent over time due to the loss of particle diversity that is caused mainly by the particle depletion in the resampling step and incorrect a priori knowledge of process and measurement noises. To improve consistency, intelligent UFastSLAM with Markov chain Monte Carlo (MCMC) move step is proposed. In the proposed method, the adaptive neuro-fuzzy inference system supervises the performance of UFastSLAM. Furthermore, the particle impoverishment caused by resampling is restrained after the resample step with MCMC move step. Simulations and experiments are presented to evaluate the performance of algorithm in comparison with UFastSLAM. The results show the effectiveness of the proposed method. | 2013 | Journal | Autonomous Robotics | |
Unsupervised 3D Object Classification from Range Image Data by Algorithmic Information Theory Alireza Norouzzadeh Ravari and Hamid D. Taghirad 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM) | Abstract: The problem of unsupervised classification of 3D objects from depth information is investigated in this paper. The range images are represented efficiently as sensor observations. Considering the high-dimensionality of 3D object classification, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In order to remedy this problem, a low-dimensional representation is defined here. The sparse model of every range image is constructed from a parametric dictionary. Employing the algorithmic information theory, a universal normalized metric is used for comparison of Kolmogorov complexity based representations of sparse models. Finally, most similar objects are grouped together. Experimental results show efficiency and accuracy of the proposed method in comparison to a recently proposed method. | 2013 | Conference | Autonomous Robotics | |
A New Method for Mobile Robot Navigation in Dynamic Environment: Escaping Algorithm F. Adib Yaghmaie, A. Mobarhani and H. D. Taghirad Robotics and Mechatronics (ICRoM) | Abstract: This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and is used for collision detection purpose. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. The results show the proper performance of Escaping Algorithm in term of dynamic obstacle avoidance as a practical method for autonomous navigation. | 2013 | Conference | Autonomous Robotics | |
A New Method for Mobile Robot Navigation in Dynamic Environment: Escaping Algorithm Farnaz Adib Yaghmaie, Amir Mobarhani, and Hamid D. Taghirad Robotics and Mechatronics | Abstract: This paper addresses a new method for navigation in dynamic environment. The proposed method is based on force field method and it is supposed that the robot performs SLAM and autonomous navigation in dynamic environment without any predefined information about dynamic obstacles. The movement of dynamic obstacles is predicted by Kalman filter and is used for collision detection purpose. In the time of collision detection, a modifying force is added to repulsive and attractive forces corresponding to the static environment and leads robot to avoid collision. Moreover, a safe turning angle is defined to assure safe navigation of the robot. The performance of proposed method, named Escaping Algorithm, is verified through different simulation and experimental tests. The results show the proper performance of Escaping Algorithm in term of dynamic obstacle avoidance as a practical method for autonomous navigation. | 2013 | Conference | Autonomous Robotics | |
Feedback Error learning Control of Trajectory Tracking of Non-Holonomic Mobile Robot Farnaz Adib Yaghmaie, Fateme Bakhshande and Hamid D.Taghirad 20th Iranian Conference on Electrical Engineering | Abstract: In this paper a new controller for nonholonomic system is introduced. This feedback error learning controller benefits from both nonlinear and adaptive controller properties. The nonlinear controller is used to stabilize the nonholonomic behavior of the systems. This controller is a sliding mode controller which is designed based on backstepping method. The adaptive controller tries to face with uncertainty and unknown dynamic of the mobile robot. This part uses neural network controller for adaptation. The experimental results show the effectiveness of proposed controller and suitable and robust tracking performance of a mobile robot, which is significantly better than traditional controllers. | 2012 | Conference | Autonomous Robotics | |
Stereo-Based Visual Navigation of Mobile Robots in Unknown Environments H. Soltani, H. D. Taghirad and A.R. Norouzzadeh Ravari 20th Iranian Conference on Electrical Engineering (ICEE2012) | Abstract: In this paper a stereo vision-based algorithm for mobile robots navigation and exploration in unknown outdoor environments is proposed. The algorithm is solely based on stereo images and implemented on a nonholonomic mobile robot. The first step for exploration in unknown environments is construction of the map of circumference in real-time. By getting disparity image from rectified stereo images and translating its data to 3D-space, point cloud model of environments is constructed. Then by projecting points to XZ plane and put local maps together based on visual odometry, global map of environment is constructed in real-time. A* algorithm is used for investigating optimal path and nonlinear back-stepping controller guides the robot to follow the identified path. Finally, the mobile robot explores for a desired object in an unknown environment through these steps. Experimental results verify the effectiveness of the proposed algorithm in real-time implementations. | 2012 | Conference | Autonomous Robotics | |
Histogram Based Frontier Exploration Amir Mobarhani, Shaghayegh Nazari, Amir H. Tamjidi, Hamid D. Taghirad 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems | Abstract: This paper proposes a method for mobile robot exploration based on the idea of frontier exploration which suggests navigating the robot toward the boundaries between free and unknown areas in the map. A global occupancy grid map of the environment is constantly updated, based on which a global frontier map is calculated. Then, a histogram based approach is adopted to cluster frontier cells and score these clusters based on their distance from the robot as well as the number of frontier cells they contain. In each stage of the algorithm, a sub-goal is set for the robot to navigate. A combination of distance transform and A* search algorithms is utilized to generate a plausible path toward the sub-goal through the free space. This way keeping a reliable distance from obstacles is guaranteed while searching for the shortest path toward the sub-goal. When such a path is generated, a B-spline interpolated and smoothed trajectory is produced as the control reference for the mobile robot to follow. The whole process is iterated until no unexplored area remains in the map. The efficiency of the method is shown through simulated and real experiments. | 2011 | Conference | Autonomous Robotics | |
Vision-Based Fuzzy Navigation of Mobile Robots in Grassland Environments A. R. Norouzzadeh Ravari, H. D. Taghirad, A. H. Tamjidi Advanced Intelligent Mechatronics | Abstract: In this paper a vision-based algorithm for mobile robot navigation in unknown outdoor environments is proposed. It is based on a simple phenomenon, that when the robot moves forward, projected images of the near obstacles grow in captured frames faster than that of the far objects. The proposed algorithm takes advantage of this property and extracts features from each grabbed frame of the camera and tracks the vertical position of the features and their speed along the Y axis of the image plane over multiple frames as the robot moves. The relative height of the features and their distance from the robot in 3D is inferred based on this data and they are fed into a fuzzy reasoning system which marks the features from safe to unsafe according to their suitability for navigation. Then a second fuzzy system summarizes these scores in different image regions and directs the robot toward the area containing more features marked as safe. Simulation and implementation results confirm the efficacy of the proposed simple algorithm for mobile robot navigation in outdoor environments. | 2009 | Conference | Autonomous Robotics | |
Line Matching Localization and Map Building with Least Square E. Mihankhah, H.D. Taghirad, A. Kalantari, E. Aboosaeedan, H. Semsarilar 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics | Abstract: We introduce a very fast and robust localization and 2D environment representation algorithm in this paper. This innovative method matches lines extracted from the LASER range finder distance data with the lines that construct the map, in order to calculate the local translation and rotation. This matching is done with a simple least square with no iterations. The algorithm is suitable for any indoor environment with mostly polygonal structure and has proven high speed and robustness in the experimental tests on our innovatively designed tracked mobile rescue robot ldquoSilverrdquo. One experimental test is presented in the last section of this paper where the outputs are presented. These outputs are: 1-drift free raster map made of points and 2- A gallery of lines providing a linear ground truth. | 2009 | Conference | Autonomous Robotics | |
A novel hybrid Fuzzy-PID controller for tracking control of robot manipulators A. R. Norouzzadeh Ravari, H.D. Taghirad 2008 IEEE International Conference on Robotics and Biomimetics | Abstract: In this paper, a novel hybrid fuzzy proportional-integral-derivative (PID) controller based on learning automata for optimal tracking of robot systems including motor dynamics is presented. Learning automata is used at the supervisory level for adjustment of the parameters of hybrid Fuzzy-PID controller during the system operation. The proposed method has better convergence rate in comparison with standard back-propagation algorithms, less computational requirements than adaptive network based fuzzy inference systems (ANFIS) or neural based controllers and having the ability of working in uncertain environments without any previous knowledge of environments' parameters. The proposed controller has been successfully applied in simulation to control a 6-DOF Puma 560 manipulator using robotic toolbox, and has satisfactory results. In this simulation also, external disturbance and noise are addressed. The result of simulation has also shown that the rate of convergence and robustness of the designed controller guarantees practical stability. | 2009 | Conference | Autonomous Robotics | |
Autonomous Staircase Detection and Stair Climbing for a Tracked Mobile Robot using Fuzzy Controller E. Mihankhah, A. Kalantari, E. Aboosaeedan, H.D. Taghirad, and S.Ali.A. Moosavian 2008 IEEE International Conference on Robotics and Biomimetics | Abstract: Theoretical analysis and implementation of autonomous staircase detection and stair climbing algorithms on a novel rescue mobile robot are presented in this paper. The main goals are to find the staircase during navigation and to implement a fast, safe and smooth autonomous stair climbing algorithm. Silver is used here as the experimental platform. This tracked mobile robot is a tele-operative rescue mobile robot with great capabilities in climbing obstacles in destructed areas. Its performance has been demonstrated in rescue robot league of international RoboCup competitions. A fuzzy controller is applied to direct the robot during stair climbing. Controller inputs are generated by processing the range data from two laser range finders which scan the environment one horizontally and the other vertically. The experimental results of stair detection algorithm and stair climbing controller are demonstrated at the end. | 2009 | Conference | Autonomous Robotics | |
SLAM Using Single Laser Range Finder AliAkbar Aghamohammadi, Amir H. Tamjidi, Hamid D. Taghirad IFAC Proceedings Volumes | Abstract: Presented method in this paper aims to develop an accurate motion model and SLAM algorithm, which is only based on the Laser Range Finder (LRF) data. Proposed method tries to overcome some practical problems in traditional motion models and SLAM approaches, such as robot slippage, and inaccuracy in parameters related to robot's hardware. Novel insights specific to process and measurement model, and making use of them in the IEKF framework, give rise to the real time method with drift-free performance in restricted environments. Furthermore, uncertainty measures, calculated through the method, are valuable information for fusion purposes and also an accurate motion model, derived in this method, can be used as a robust and an accurate localization procedure in different structured environments. These issues are validated through experimental implementations; experiments verify method's efficiency both in pure localization and in SLAM scenarios in the restricted environments, involving loop closures. | 2008 | Conference | Autonomous Robotics | |
A Solution for SLAM through Augmenting Vision and Range Information Ali A. Aghamohammadi, Amir H. Tamjidi, Hamid D. Taghirad 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems | Abstract: This paper proposes a method for augmenting the information of a monocular camera and a range finder. This method is a valuable step towards solving the SLAM problem in unstructured environments free from problems of using encoderspsila data. Proposed algorithm causes the robot to benefit from a feature-based map for filtering purposes, while it exploits an accurate motion model, based on point-wise raw range scan matching rather than unreliable feature-based range scan matching, in unstructured environments. Moreover, robust loop closure detection procedure is the other consequence of this method. Experiments with a low-cost IEEE 1394 webcam and a range finder illustrate the effectiveness of the proposed method in drift-free SLAM at loop closing motions in unstructured environments. | 2008 | Conference | Autonomous Robotics | |
Feature-Based Laser Scan Matching For Accurate and High Speed Mobile Robot Localization A. A. Aghamohammadi, H. D. Taghirad, A. H. Tamjidi, and E. Mihankhah Proceedings of the 3rd European Conference on Mobile Robots | Abstract: This paper introduces an accurate and high speed pose tracking method for mobile robots based on matching of extracted features from consecutive scans. The feature extraction algorithm proposed in this paper uses a global information of the whole scan data and local information around feature points. Uncertainty of each feature is represented using covariance matrices determined due to observation and quantization error. Taking into account each feature's uncertainty in pose shift calculation leads to an accurate estimation of robot pose. Experiments with low range URG_X002 laser range scanner illustrate the effectiveness of the proposed method for mobile robot localization. | 2007 | Conference | Autonomous Robotics | |
New Wavelet Based Algorithm for Real Time Visual Tracking Akram Bayat, Hamid R. Taghirad, Seyyed Sadegh Mottaghian | Abstract: in this paper, we propose a new technique in wavelet domain for real time object detection and tracking in a sequence of images. The object to be tracked is identified in the first frame. Our proposed algorithm consists of two phases: the first, wavelet based edge detection is used to form ground boundary map. Then, Object dimensions estimation is implemented to determine probabilistic object areas. finally, target detection based on finding best match using feature vectors is applied. We defined dispersion of wavelet detail coefficient in object area as feature to be matched. Also we proposed a new color model for images to be used in processing algorithm. Our experimental results show that the algorithm is robust and fast. It is also insensitive to changing illumination condition and size of target | 2006 | Conference | Autonomous Robotics |