Department of Computer Science and Information Systems
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Item HMR-ODTA: online diverse task allocation for a team of heterogeneous mobile robots(2025-05) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, SudeeptCoordinating time-sensitive deliveries in environments like hospitals poses a complex challenge, particularly when managing multiple online pickup and delivery requests within strict time windows using a team of heterogeneous robots. Traditional approaches fail to address dynamic rescheduling or diverse service requirements, typically restricting robots to single-task types. This paper tackles the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where autonomous mobile robots are capable of handling varied service requests. The objective is to minimize late delivery penalties while maximizing task completion rates. To achieve this, we propose a novel framework leveraging a heterogeneous robot team and an efficient dynamic scheduling algorithm that supports dynamic task rescheduling. Users submit requests with specific time constraints, and our decentralized algorithm, Heterogeneous Mobile Robots Online Diverse Task Allocation (HMR-ODTA), optimizes task assignments to ensure timely service while addressing delays or task rejections. Extensive simulations validate the algorithm's effectiveness. For smaller task sets (40-160 tasks), penalties were reduced by nearly 63%, while for larger sets (160-280 tasks), penalties decreased by approximately 50%. These results highlight the algorithm's effectiveness in improving task scheduling and coordination in multi-robot systems, offering a robust solution for enhancing delivery performance in structured, time-critical environments.Item CF-HMRTA: coalition formation for heterogeneous multi-robot task allocation(Springer, 2025-07) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, SudeeptThis paper introduces a novel approach, Coalition Formation for Heterogeneous Multi-Robot Task Allocation (CF-HMRTA), to address the challenge of multi-robot task allocation. The problem, inherently NP-Hard, is tackled using bipartite graph matching. CF-HMRTA forms heterogeneous robot coalitions with unique service skills to complete tasks collaboratively, using a heuristic algorithm for optimal robot-task pairing while preventing task overlap. Recent research work using bipartite graph matching for multi-robot coalition formation and task allocation often assumes homogeneity across tasks and robots, where any robot can be assigned to any task. In contrast, the solution proposed in this paper explicitly considers the diversity of robots with varying service skills. Additionally, tasks demand different sets of skills, such as sensing, monitoring, and data collection, making certain tasks unsuitable for some robots due to hardware constraints. For instance, tasks requiring aerial footage are assigned to drones, while ground robots handle close-ground monitoring. Furthermore, we incorporate task-specific time constraints into our problem formulation, enhancing its realism. Considerably less research has been conducted on heterogeneous robot teams solving tasks that require multiple service skills and temporal constraints, making our work a significant contribution to the field. The algorithm achieves a worst-case time complexity of , where represents the edges in the bipartite graph, and guarantees perfect matching. Simulation results highlight its scalability, successfully allocating up to 2000 robots to 400 tasks in approximately 11 seconds.Item DTA-HMR-TT: dynamic task allocation for a heterogeneous team of mobile robots with task transfer(IEEE, 2024-11) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, SudeeptManaging time-sensitive deliveries in settings like hospitals is a challenging task, especially when multiple pickup and delivery requests need to be coordinated efficiently within strict time windows. This paper focuses on the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where a fleet of autonomous mobile robots works together to fulfill client requests that involve picking up items from specified origins and delivering them to designated destinations. Our objective is to minimize penalties associated with late deliveries while maximizing the number of successfully completed requests. To address this, we introduce a novel approach using a heterogeneous team of robots equipped with an efficient and cost-effective scheduling algorithm. Users submit requests with specific time constraints, and our proposed decentralized algorithm-Dynamic Task Allocation for Heterogeneous Mobile Robots with Task Transfer (DTA-HMR-TT)–optimizes task sharing between robots, ensuring timely service. The algorithm dynamically adjusts to handle rejected or delayed tasks and manages the complex transfer of tasks between robots to improve delivery efficiency. Extensive simulations have demonstrated that our approach significantly outperforms state-of-the-art methods. For smaller task sets (50 to 150 tasks), penalties were reduced by 27%, while for larger sets (150 to 300 tasks), penalties were lowered by 36%. Our results highlight the effectiveness of DTA-HMR-TT in enhancing task scheduling and coordination in multi-robot systems, offering a promising solution for improving delivery performance in structured environments.Item Visual assist system with enhanced localization for indoor navigation of visually impaired people(IEEE, 2024-10) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, SudeeptLarge indoor spaces having complex layouts are often difficult to navigate. Indoor spaces in hospitals, universities, shopping complexes, etc., carry multi-modal information through text and symbols. Hence, it is difficult for Visually Impaired people to independently navigate such spaces. Indoor environments are usually GPS-denied; therefore, Bluetooth-based, WiFi-based, or Range-based methods are used for localization. These methods incur high setup costs, lack good accuracy, and sometimes need specialized sensing equipment. We propose a Visual Assist (VA) system for the indoor navigation of BVI individuals using visual fiducial markers for localization. State-of-the-art (SOTA) approaches for localization using visual fiducial markers use fixed cameras having a limited field of view. We employ a Pan-Tilt turret-mounted camera, which provides a 360° field of view for enhanced marker tracking. We, therefore, need fewer markers for mapping and navigation. We further use our localization model for enhancing existing SLAM methods, namely, Hector SLAM, ORBSLAM and UCOSLAM. The efficacy of the proposed system is measured on three metrics, i.e., Root Mean Square Error(RMSE), Average Distance to Nearest Neighbours (ADNN), and Absolute Trajectory Error (ATE). The proposed system offers accurate trajectory tracking upto ±8cm . ADNN and RMSE of Hector SLAM, ORB-SLAM, and UcoSLAM improve by 9.1%, 8.9%, and 7%, respectively while ATE is reduced by 6.7%, 4.5%, and 5.2%.Item Model Compression Based Lightweight Online Signature Verification Framework(Springer, 2022-11) Gautam, AvinashAdvances in networking and digital technologies have led to the widespread usage of Online Signature Verification (OSV) frameworks in real-time settings to validate a user's identity. Because of the superior performance of Deep Learning frameworks, CNN-based models have been widely used for solving difficult computer vision tasks such as Object Detection, Object Segmentation, and so on. The biggest impediment to OSV adoption of CNN-based models is the growing size of CNN models. This prohibits OSV frameworks from being widely used in devices with minimal computational resources, such as mobile/ embedded devices. The newly popular topic of CNN model pruning aims to solve this problem by deleting filters and neurons that do not contribute to the model's learning. Optimal CNN-based OSV frameworks are obtained by removing the less important filters and neurons and fine-tuning the pruned networks. In line with this, we offer a novel light weight OSV architecture in which a detailed ablation research is performed to examine the contribution of each layer, and non-contributive layers are deleted based on the analysis. As a result, ideal low weight models with improved classification accuracies and the ability to be applied in real-time devices emerge. Our model's performance is thoroughly tested on three commonly used datasets: MCYT-100 (DB1), SVC, and SUSIG. In MCYT-100, SVC, and SUSIG datasets, the pruned model achieves a state-of-the-art EER of 7.98%, 3.65%, and 12.39% in the skilled-1 group, respectively. The efficiency of pruning-based OSV frameworks has been demonstrated in experiments.Item TSOSVNet: Teacher-Student Collaborative Knowledge Distillation for Online Signature Verification(CVF International Conference, 2023) Gautam, AvinashOnline signature verification (OSV) is a standardized personal authentication scheme with wide social acceptance in critical real-time applications include access control, m-commerce, etc. Even though the current advances in Deep learning (DL) technologies catalysed state-of-theart frameworks for challenging domains like computer vision, speech recognition, etc., the DL-based frameworks are voluminous with huge trainable parameters and are hard to deploy in real-time systems demanding faster inference. To adopt DL into OSV for improved performance, we propose an OSV framework made up of teacher-student collaborative knowledge distillation (TSKD) technique. A heavy Transformer based teacher is trained first and the teacher knowledge is distilled into a very lightweight Convolutional Neural Network (CNN) based student. A well trained teacher network results in an efficient deep representative feature learning by the student and results in a performance improvement. In a thorough set of experiments with three popular and standard datasets, ie, the MCYT-100, SUSIG, and SVC, TSOSVNet framework, with a CNN based student model requiring only 3266 trainable parameters results in an EER of 12.42% compared to the recent SOTA 13.38% by a model with 206277 parameters in skilled 01 category of MCYT-100 dataset. In comparison to cutting-edge CNN-based OSV models, the proposed TSOSVNet produced a state-of-the-art EER in the most of the test categories with an average of 90% lesser trainable parameters.Item OSVConTramer: A Hybrid CNN and Transformer based Online Signature Verification(IEEE, 2023) Gautam, AvinashThe advances in Deep Learning (DL) resulted in the development Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) based Online Signature Verification (OSV) frameworks. The main drawback with LSTM based networks is the limited parallelization of model training. The CNN based frameworks are efficient in learning local feature dependencies, but fail to apprehend long term feature dependencies. The current works confirmed the success of Transformer based models in long term time series classification (LTTSC) problems due to efficient capturing of context-dependent global feature interactions. Hence, to achieve higher classification accuracy, in this work, we propose a first of its kind of an attempt, in which, we combine CNN and Transformer for Online Signature Verification, named OSVConTramer. The proposed OSVConTramer efficiently learns optimal local and global dependencies of an input signature feature vector and outperforms previous CNN and LSTM based OSV frameworks achieving state-of-the-art classification accuracy. On the widely used MCYT-100, SVC, and SUSIG datasets, specific to one shot learning, our model achieves a SOTA EER of 10.85%, 5.45%, and 6.32%, respectively. The results of the experimental analysis confirms that the accuracy outcomes of OSV frameworks is improved significantly by the optimal learning of the relationships between local and global feature dependency.Item Autonomous Mapping and Navigation using Fiducial Markers and Pan-Tilt Camera for Assisting Indoor Mobility of Blind and Visually Impaired People(2023-10) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, SudeeptLarge indoor spaces have complex layouts making them difficult to navigate. Indoor spaces in hospitals, universities, shopping complexes, etc., carry multi-modal information in the form of text and symbols. Hence, it is difficult for Blind and Visually Impaired (BVI) people to independently navigate such spaces. Indoor environments are usually GPS-denied; therefore, Bluetooth-based, WiFi-based, or Range-based methods are used for localization. These methods have high setup costs, lesser accuracy, and sometimes need special sensing equipment. We propose a Visual Assist (VA) system for the indoor navigation of BVI individuals using visual Fiducial markers for localization. State-of-the-art (SOTA) approaches for visual localization using Fiducial markers use fixed cameras having a narrow field of view. These approaches stop tracking the markers when they are out of sight. We employ a Pan-Tilt turret-mounted camera which enhances the field of view to 360° for enhanced marker tracking. We, therefore, need fewer markers for mapping and navigation. The efficacy of the proposed VA system is measured on three metrics, i.e., RMSE (Root Mean Square Error), ADNN (Average Distance to Nearest Neighbours), and ATE (Absolute Trajectory Error). Our system outperforms Hector-SLAM, ORB-SLAM3, and UcoSLAM. The proposed system achieves localization accuracy within ±8cm compared to ±12cm and ±10cm for ORB-SLAM3 and UcoSLAM, respectively.Item D-MRFTE: A Decentralized Relay-Based Approach for Multi-Robot Unknown Area Exploration(IEEE, 2023) Gautam, Avinash; Mohan, Sudeept; Shekhawat, Virendra SinghIn this paper, a decentralized relay-based approach (D-MRFTE) for unknown area exploration using a team of autonomous mobile robots is proposed under communication constraints. Using the relay robots, the multi-robot system forms a high-latency decentralized network with distributed copies of exploration information for which eventual consistency and completeness are ensured through meetups. The meetups act as a safety net and set a bound on latency by ensuring data transfer at periodic intervals whenever the multi-robot network gets fragmented. The information exchange related to the robot’s state and the ongoing exploration is facilitated by the relay robots. The robots use timestamps to assimilate the latest available information by using version vectors. To achieve a consistent state of explorer robots, the relays schedule meetups with other relays they come in contact with, creating a tightly-knit group. Our approach, under two communication models, i.e., Disk-based and Line-of-Sight-based, exhibits superior performance compared with two state-of-the-art algorithms in terms of completion time and distance traveled by the robot team. The simulations are conducted in a Player/Stage simulator with different robot team sizes.Item A distributed algorithm for circle formation by multiple mobile robots(IEEE, 2013) Gautam, Avinash; Mohan, SudeeptThis paper suggests a distributed, decentralized approach for positioning multiple mobile robots in a circular formation in a semi synchronous setting. The problem of the circle formation with multiple robots which are arbitrarily placed on a 2D plane requires all robots to be uniformly positioned (i.e., at an equal angular distance of 2ŏ/N, where N = number of robots) on the circle circumference. The suggested approach uses explicit inter robot communication by way of message passing and forms a token ring based network. It uses the distributed solution of one of the classical synchronization problem often used in distributed systems, the Dining Philosopher Problem, for the robots to synchronize during their activation cycles.
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