arXiv subjects

Rudrashis Majumder

Publications and source records attributed to Rudrashis Majumder.

6 recordsLinked to original sources

arXiv2026

EROAS: 3D Efficient Reactive Obstacle Avoidance System for Autonomous Underwater Vehicles using 2.5D Forward-Looking Sonar

Autonomous Underwater Vehicles (AUVs) have advanced significantly in obstacle detection and path planning through sonar, cameras, and learning-based methods. However, safe and efficient navigation in cluttered environments remains challenging due to partial observability, turbidity, the limited field-of-view of forward-looking sonar (FLS), and occlusions that obscure obstacle geometry. To address these issues, we propose the Efficient Reactive Obstacle Avoidance Strategy (EROAS), a lightweight framework that augments a standard 2D FLS with a pivoting mechanism, effectively transforming it into a cost-efficient \emph{2.5D sonar}. This design provides vertical information on demand, extending situational awareness while minimizing computational overhead. EROAS integrates three complementary modules: first, Sonar Profile-guided Directional Decision Control (SPD2C) for rapid gap detection and generation of reference commands in both horizontal and vertical planes. Secondly, the Spatial Context Generator (SCG), which maintains a short-term obstacle memory of the past to mitigate partial observability, and finally, a Spatio-Temporal Control Barrier Function (ST-CBF) that enforces forward-invariance of safety constraints by filtering nominal references. Together, these components enable robust, reactive avoidance of obstacles in uncertain and cluttered 3D underwater settings. Simulation and hardware-in-the-loop (HIL) experiments validate the efficacy of the proposed EROAS algorithm, demonstrating improved trajectory efficiency, reduced travel time, and enhanced safety compared to conventional methods such as the Dynamic Window Approach (DWA) and Artificial Potential Fields (APF). https://github.com/AIRLabIISc/EROAS

cs.RO↗

arXiv2025

NeuroHJR: Hamilton-Jacobi Reachability-based Obstacle Avoidance in Complex Environments with Physics-Informed Neural Networks

Autonomous ground vehicles (AGVs) must navigate safely in cluttered environments while accounting for complex dynamics and environmental uncertainty. Hamilton-Jacobi Reachability (HJR) offers formal safety guarantees through the computation of forward and backward reachable sets, but its application is hindered by poor scalability in environments with numerous obstacles. In this paper, we present a novel framework called NeuroHJR that leverages Physics-Informed Neural Networks (PINNs) to approximate the HJR solution for real-time obstacle avoidance. By embedding system dynamics and safety constraints directly into the neural network loss function, our method bypasses the need for grid-based discretization and enables efficient estimation of reachable sets in continuous state spaces. We demonstrate the effectiveness of our approach through simulation results in densely cluttered scenarios, showing that it achieves safety performance comparable to that of classical HJR solvers while significantly reducing the computational cost. This work provides a new step toward real-time, scalable deployment of reachability-based obstacle avoidance in robotics.

cs.RO↗

arXiv2022

Cyclone Preparedness, Rescue Operations and Damage Assessment using UAVs

UAV's capability to access remote and inaccessible areas within a quick time can be utilized for effective cyclone management. This paper presents the possible application of UAVs at different stages of cyclone mitigation. The overall system architecture necessary for preparedness, rescue operation, resource allocation, and damage assessment using UAVs during cyclones is described. Although general commercial UAVs are reported to be used in cyclone operations, UAV systems should be planned specifically for cyclone operations to improve efficiency. Here, the specification required for effective and safe UAV operations in the post-cyclone scenario is presented. Mission planning required for various rescue, relief, and damage assessment missions related to cyclone management is discussed. A case study of deploying UAV in Amphan cyclone operation in West Bengal is also presented. This paper can help disaster management authorities to develop UAV systems specifically to cater to cyclone operations.

eess.SY↗

arXiv2022

Development of Decision Support System for Effective COVID-19 Management

This paper discusses a Decision Support System (DSS) for cases prediction, allocation of resources, and lockdown management for managing COVID-19 at different levels of a government authority. Algorithms incorporated in the DSS are based on a data-driven modeling approach and independent of physical parameters of the region, and hence the proposed DSS is applicable to any area. Based on predicted active cases, the demand of lower-level units and total availability, allocation, and lockdown decision is made. A MATLAB-based GUI is developed based on the proposed DSS and could be implemented by the local authority.

eess.SY↗

arXiv2022

Critical Medical Resource Allocation during COVID-19 Pandemic

In this paper, an optimal resource allocation framework is proposed for the allocation of critical medical resources among different units during a pandemic. The framework is developed by considering the dynamics of Pandemic, hierarchical government structure, and non-uniformity of unit resource requirement among different units. The cost function is designed to minimize the difference between the demand, actual allocation, and ideal allocation, where ideal allocation for a region is considered based on the predicted active cases in a fraction of predicted total active cases of all regions. Different cost functions are used at a different level of organization based on the available information. The model can also accommodate severity of disaster in a region in this framework. A sample allocation case study is presented for the allocation of oxygen for different states of India.

eess.SY↗

arXiv2021

Game-Theoretic Model Based Resource Allocation During Floods

For multiple emergencies caused by natural disasters, it is crucial to allocate resources equitably to each emergency location, especially when the availability of resources is limited in quantity. This paper has developed a multi-event crisis management system using a non-cooperative, complete information, strategic form game model. In the proposed system, each emergency event is assumed to occur in different locations simultaneously. These locations are represented as the players in the game, competing with the other players for an optimal allocation of scarce resources available at different resource stations. The players incur a non-monetary cost for obtaining resource units. The objective of the proposed game is to derive optimal strategies for an effective and fair allocation of resources to the respective players.

cs.GT↗