Browsing by Author "Mohan, Sudeept"

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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.
Balanced partitioning of workspace for efficient multi-robot coordination
(IEEE, 2017) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
Multi-robot terrain coverage approaches that are based on Voronoi partitioning produce unbalanced partitions of the workspace resulting in uneven distribution of the workload to the individual robots. The proposed approach creates partitions of the workspace such that the regions to be covered by individual robots are maximally balanced. This type of partitioning can be especially useful in tasks like floor cleaning, surveillance etc. The proposed approach is suitable for use in indoor environments like office buildings, hospitals etc. It is assumed that the grid map of the workspace is already known. The workspace is transformed into a topological weighted connected graph. Vertex weight is defined by the size of the area it represents. This graph is then partitioned into sub-graphs that are maximally balanced in terms of vertex weights using genetic algorithm. These sub-graphs thus obtained represent balanced partitions which are assigned to the individual robots for further processing.
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.
Cluster, Allocate, Cover: An Efficient Approach for Multi-robot Coverage
(IEEE, 2015) Mohan, Sudeept; Gautam, Avinash
This article presents an algorithm for online multirobot coverage that proceeds with minimal knowledge of the already explored region and the frontier cells. It creates clusters of frontier cells which are designated to robots using an optimal assignment scheme. Coverage is then performed using a novel path planning technique. Many approaches that use clustering for multi-robot coverage do not specify strict time criteria for re-clustering. Moreover, the motion plans they use result in redundant coverage. To overcome these limitations, an appropriate motion plan for the robots is chosen based on the context of already covered frontiers. Dispersion of robots is vital for efficient coverage and is an emergent behavior in our approach. The efficacy of the proposed approach is tested in simulation and on a multi-robot test-bed. The algorithm performs better than some state of the art approaches.
A Comparative Study of Few Conventional and Adaptive Control Algorithms for Manipulator Control.
(DBLP, 2005) Mohan, Sudeept; Bhanot, Surekha
Conventional adaptive and fuzzy control of robot manipulators
(BITS, Pilani, 2007) Mohan, Sudeept
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.
Design and Development of a Real Time Scheduling Algorithm for Mixed Task Set on Multi-core Processors
(IEEE, 2014-08) Mohan, Sudeept
This paper presents a real time scheduling algorithm for mixed task set on homogeneous multi-core platform. Periodic tasks are scheduled using Partitioned Earliest Deadline First (P-EDF) technique. Aperiodic tasks are assigned globally to different processor cores and scheduled using Total Bandwidth Server (TBS) on each core. In the proposed algorithm, the excess processing capacity of the cores left unused by the periodic tasks can be utilized by assigning aperiodic task to each core. This improves the overall utilization of individual core. Work conserving nature of global assignment reduces response time of aperiodic task. The proposed algorithm is implemented using java based simulator and tested on large number of synthetic test data. Results show improvement in utilization of individual processing core and improvement in response time of aperiodic tasks.
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.
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.
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.
Dynamicvoltage and frequency scaling scheduling algorithm for mixed task set
(IEEE, 2013) Mohan, Sudeept
Dynamic voltage and frequency scaling (DVFS) is one of the popular energy conservation techniques for battery operated real-time and embedded systems. This paper presents EEDVFS, an energy efficient DVFS scheduling algorithm for mixed task set. EEDVFS is a variant of Earliest Deadline First (EDF) based Deferrable Server (DS). Experimental results show that EEDVFS reduces energy consumption without compromising on periodic task deadlines and aperiodic task responsiveness. The results of EEDVFS are compared with non-DVFS EDF version of DS upon various performance metrics such as energy consumption, average response time, latency, preemption count and number of decision points. EEDVFS offers 54% energy saving in comparison with the non-DVFS EDF based DS algorithm.
Energy aware real time scheduling algorithm for mixed task set
(IEEE, 2013-09) Mohan, Sudeept
Energy consumption is one of the major limiting factors of battery operated real-time systems. Optimizing energy consumption without affecting performance and schedulability is the major topic to be researched. In this paper, an energy aware real time scheduling algorithm is proposed for a system with mixed task set consisting of both periodic and aperiodic tasks. Dynamic energy reduction techniques like Dynamic Voltage and Frequency Scaling (DVFS) is used for energy optimization without affecting the responsiveness of aperiodic tasks. Performance of the proposed algorithm is compared with non-DVS algorithm. Experimental evaluation reveals that the proposed algorithm saves 54.44% of energy in comparison with non-DVS algorithms. It achieves this with no degradation in responsiveness of the aperiodic tasks.
Energy efficient real-time scheduling algorithm for mixed task set on multi-core processors
(Inder Science, 2017) Mohan, Sudeept
Energy optimisation is gaining greater significance in a wide range of systems from mobile devices to datacentres. Specifically, in battery powered real-time embedded systems where tasks are executed under hard timing constraints, energy optimisation poses a big challenge. This paper focuses on dynamic energy optimisation using a well-established technique namely dynamic voltage and frequency scaling (DVFS). This work presents a real-time scheduling algorithm that uses DVFS on mixed task system containing periodic as well as aperiodic tasks on homogeneous multi-core processor. The proposed algorithm guarantees periodic task deadlines and offers minimum aperiodic task response times. Simulation analysis shows that the proposed scheme saves more energy as compared to cycle conserving, static FVS and non-DVFS scheduling algorithms. Further, it does not result in any response time degradation of aperiodic tasks as compared to other algorithms.
Experimental Evaluation of Multi-Robot Online Terrain Coverage Approach
(IEEE, 2018) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
This paper presents a empirical evaluation of some approaches suggested in the literature for solving the online terrain coverage task. Our first contribution is that, we have implemented in simulation four state-of-the-art approaches. The first two approaches are based on structured trajectories and use backtracking mechanism for task allocation. The other two are based on the behavior of ants. Also, we have modified one of the state-of-the-art approaches and improved its performance in terms of computation time. The second contribution is that, we have developed a practical test-bed comprising of multiple differential drive robots that are able to coordinate with each other in a distributed fashion by wirelessly communicating with their team-mates. We have implemented the representative set of approaches on our test-bed. The same test-bed can be leveraged for validating multi-robot coordination approaches for solving other tasks like patrolling, foraging, etc.
FAST Synchronous Frontier Allocation for Scalable Online Multi-Robot Terrain Coverage
(Springer, 2016-09) Mohan, Sudeept; Gautam, Avinash
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.
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.
A Graph Partitioning Approach for Fast Exploration with Multi-Robot Coordination
(IEEE, 2019) Shekhawat, Virendra Singh; Mohan, Sudeept; Gautam, Avinash
A multi-robot exploration approach is suggested in this paper that works on the premise that the topo-metric map of the indoor environment is known a priori. Genetic Algorithms (GAs) are used for spatial partitioning of the topo-metric graph of the environment. Each spatial partition, which represents the sub-graph, is apportioned to a unique robot by using the Hungarian method for task assignment in conjunction with Bully Algorithm for leader election. In the case of robot(s) failure, graph re-partitioning and single item auctions are used for re-assigning the remaining task(s) of the failed robot(s) to other robots. The proposed approach performs better than a recent state-of-the-art strategy that employs Delaunay triangulation and multi-prim algorithm for multi-robot exploration. Empirical results obtained in simulation by varying the number of robots in two different and complex environments prove the efficacy of the proposed approach.
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.
Multi-Robot Online Terrain Coverage under Communication Range Restrictions – An Empirical Study
(IEEE, 2021) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
Communication in a multi-robot system is vital as it facilitates coordination. The performance of a multi-robot system improves with coordination. Many state-of-the-art approaches ignore intermittent connectivity, which is inevitable due to communication range restrictions. In this paper, the assumption of global communication is dropped, and the robots are restricted to communicate in a pre-specified communication range as in a realistic scenario. A comparative empirical study of five different state-of-the-art approaches which assume that the communication is omnipresent is conducted. The performance of each algorithm is evaluated by varying the communication range with a different sized robot team both in simulation and on a physical multi-robot test-bed. Finally, the impact of communication range restrictions on the performance of the approaches under evaluation is discussed.