Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

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Author: search.filters.author.Mohan, SudeeptSubject: search.filters.subject.Computer Science

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Now showing 1 - 10 of 25
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    HMR-ODTA: online diverse task allocation for a team of heterogeneous mobile robots
    (2025-05) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, Sudeept
    Coordinating 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.
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    CF-HMRTA: coalition formation for heterogeneous multi-robot task allocation
    (Springer, 2025-07) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, Sudeept
    This 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.
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    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, Sudeept
    Managing 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.
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    Visual assist system with enhanced localization for indoor navigation of visually impaired people
    (IEEE, 2024-10) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
    Large 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%.
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    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, Sudeept
    Large 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.
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    D-MRFTE: A Decentralized Relay-Based Approach for Multi-Robot Unknown Area Exploration
    (IEEE, 2023) Gautam, Avinash; Mohan, Sudeept; Shekhawat, Virendra Singh
    In 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.
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    A distributed algorithm for circle formation by multiple mobile robots
    (IEEE, 2013) Gautam, Avinash; Mohan, Sudeept
    This 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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    Positioning multiple mobile robots for geometric pattern formation: An empirical analysis
    (IEEE, 2014) Mohan, Sudeept; Gautam, Avinash
    This paper presents an experimental setup for absolute positioning of multiple mobile robots in an indoor environment using a low cost camera. Localization or positioning of mobile robot in its environment is crucial for deciding its future course of action. In this paper we have proposed to use an overhead camera for positioning multiple mobile robots which are required to act as a team. Also we have tested the efficacy of two existing distributed algorithms for circle formation using a team of five e-puck robots. The first algorithm is mathematically proven with many assumptions about the sensing and motion capabilities of mobile robots which are not feasible in the real world. In the second algorithm the authors have considered explicit inter robot communication and have utilized the distributed solution of a well known algorithm often discussed in distributed computing - the Dinning Philosophers Problem for the robots to synchronize during their activation cycle. The contribution of this paper is twofold i.e., first, a practical low cost, multi-robot positioning system is proposed and second, experimental evaluation of two distributed algorithms for circle formation by a team of mobile robots have been carried out. It is seen that the second algorithm outperforms the first.
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    A distributed algorithm for balanced multi-robot task allocation
    (IEEE, 2016) Gautam, Avinash; Mohan, Sudeept
    In this paper the problem of static multi-robot task allocation is addressed. It is concerned with the distribution of static tasks in an environment to robots such that the robots complete the tasks in an optimal fashion. The cost of completing a task is proportional to the distance travelled by a robot to visit that task. This problem is of particular importance in multi-robot systems because finding an optimal solution is NP-hard. Earlier work has paid less attention towards load balanced task allocation. In this paper, a completely distributed algorithm is proposed. A travelling salesman tour (TST) considering all task locations is computed using distributed genetic algorithm. The TST is partitioned into fragments that are distributed amongst the robots using a novel auction algorithm. The proposed algorithm is compared with a state of the art algorithm in simulation. The results thus obtained substantiate the fact that the proposed algorithm shows improved performance in terms of load balanced distribution of tasks to the individual robots in multi-robot system.
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    FAST: Synchronous Frontier Allocation for Scalable Online Multi-Robot Terrain Coverage
    (Springer, 2017-09) Gautam, Avinash; Mohan, Sudeept
    We propose Frontier Allocation Synchronized by Token passing (FAST), a distributed algorithm for online terrain coverage using multiple mobile robots, ensuring mutually exclusive selection of frontier cells. Many existing approaches cover the terrain in an irregular fashion, without considering the usability of the already covered region. For instance, in the task of floor cleaning in an office building, these approaches do not guarantee the cleanliness of large unbroken areas until a majority of the task is complete. FAST on the other hand, incrementally traverses the terrain generating structured trajectories for each robot. Following a structured trajectory for coverage path planning is proven to be a very powerful approach in literature. This renders large portions of the terrain usable even before the completion of the coverage task. The novel map representation techniques used in FAST render it scalable to large terrains, without affecting the volume of communication among robots. Moreover, the distributed nature of FAST allows incorporation of fault-tolerance mechanisms.