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Rahul Kumar

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7 papers
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7

TIST Journal 2026 Journal Article

PHtNN: Prediction of Heart Disease Risk Using Twin Neural Network

  • Ankur Gupta
  • Rahul Kumar
  • Balasubramanian Raman
  • Harkirat Singh Arora

Cardiovascular disease is one of the primary causes of increasing mortality rates globally, spanning various types of ailments. Aside from a healthy lifestyle, prediction, prognostication, and early diagnosis can all contribute to lower mortality rates. The massive variation in the economic growth and development of countries worldwide, accompanied by the irregular availability of medical experts and radiologists, is a major impediment to early diagnosis. Researchers are working on prediction systems that will aid doctors and radiologists in prognostication and assessment by providing diagnostics to the human race without regard to geographical, economic, or financial inequalities. The use of a computational intelligence-based medical imaging prediction system to either prognosticate or detect and further diagnose the disease is becoming more popular. In this work, a computational intelligence-based prediction system, PHtNN, for heart disease diagnosis has been proposed. PHtNN uses the multiple factor analysis (MFA) to extract features from the heart disease multi-datasets, VA Long Beach, Switzerland, Hungarian, Cleveland, and Z-Alizadeh Sani, and train the model by using twin neural network. The system, PHtNN, is validated using the hold-out validation scheme with a ratio of 3:1. Experimental results reveal that PHtNN outperforms several previous baseline approaches in terms of accuracy and improves the system's efficiency; as a result, it can assist medical experts in diagnosing cardiac patients.

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IROS Conference 2025 Conference Paper

meSch: Multi-Agent Energy-Aware Scheduling for Task Persistence

  • Kaleb Ben Naveed
  • An Dang
  • Rahul Kumar
  • Dimitra Panagou

This paper develop a scheduling protocol for a team of autonomous robots that operate on long-term persistent tasks. The proposed framework, called meSch, accounts for the limited battery capacity of the robots and ensures that the robots return to charge their batteries one at a time at the single charging station. The protocol is applicable to general nonlinear robot models under certain assumptions, does not require robots to be deployed at different times, and can handle robots with different discharge rates. We further consider the case when the charging station is mobile and its state information is subject to uncertainty. The feasibility of the algorithm in terms of ensuring persistent charging is given under certain assumptions, while the efficacy of meSch is validated through simulation and hardware experiments. [Code] a [Video] b

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ICRA Conference 2025 Conference Paper

THAMP-3D: Tangent-Based Hybrid A* Motion Planning for Tethered Robots in Sloped 3D Terrains

  • Rahul Kumar
  • Vishnu S. Chipade
  • Sze Zheng Yong

This paper introduces a novel motion planning algorithm designed for a team of curvature-constrained tethered robots operating on sloped 3D terrains. Our approach addresses the critical issues of tether-terrain interaction, robot stability, and tether entanglement avoidance. The study focuses on a two-robot system, where stability is primarily dependent on tether tension, which is in turn limited by wheel traction. We propose a path-planning method that strategically utilizes terrain features (e. g. , rocks) to augment tether tension through additional friction, thereby enhancing overall system stability. Our algorithm employs a modified tangent graph as the underlying structure for a hybrid A* search, incorporating stability constraints throughout the planning process. The proposed method is extensively evaluated through various simulation experiments, demonstrating its effectiveness in planning safe and efficient paths.

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IROS Conference 2024 Conference Paper

Stability of Tethered Ground Robots on Extreme Terrains

  • Rahul Kumar
  • Vishnu S. Chipade
  • Sze Zheng Yong

In the absence of a tether attachment mechanism that can provide infinitely large tension to the tethered robots moving on extreme planetary terrains, there is a limit on how much tension can be realistically generated or supported by the tether. In this paper, we consider a team of two robots tethered together moving on extreme terrains. The traction on the wheels of the robot and the friction between the tether and the tether attachment surfaces/objects (e. g. , rocks) is the only way to support the tether tension. Given a path for the robots to navigate, we provide a systematic algorithm to check if the robots will be stable along the given path while considering the maximum constraints on the tension generated or supported by the tether. The results are validated via simulation experiments.

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ICRA Conference 2024 Conference Paper

WiTHy A *: Winding-Constrained Motion Planning for Tethered Robot using Hybrid A *

  • Vishnu S. Chipade
  • Rahul Kumar
  • Sze Zheng Yong

In this paper, a variant of hybrid A* is developed to find the shortest path for a curvature-constrained robot, that is tethered at its start position, such that the tether satisfies user-defined winding angle constraints. A variant of tangent graphs is used as an underlying graph for searching a path using A * in order to reduce the overall computation and define appropriate cost metrics to ensure winding angle constraints are satisfied. Conditions are provided under which the proposed algorithm is guaranteed to find a winding angle constrained path. The effectiveness and performance of the proposed algorithm are studied in simulation.

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IROS Conference 2019 Conference Paper

LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning

  • Rahul Kumar
  • Aditya Mandalika
  • Sanjiban Choudhury
  • Siddhartha S. Srinivasa

We consider the problem of leveraging prior experience to generate roadmaps in sampling-based motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low- cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. State-of-the-art is to train a conditional variational auto-encoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along near-optimal paths that are otherwise difficult- to-sample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.

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JMLR Journal 2003 Journal Article

A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section)

  • Inderjit S. Dhillon
  • Subramanyam Mallela
  • Rahul Kumar

High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new information-theoretic divisive algorithm for feature/word clustering and apply it to text classification. Existing techniques for such "distributional clustering" of words are agglomerative in nature and result in (i) sub-optimal word clusters and (ii) high computational cost. In order to explicitly capture the optimality of word clusters in an information theoretic framework, we first derive a global criterion for feature clustering. We then present a fast, divisive algorithm that monotonically decreases this objective function value. We show that our algorithm minimizes the "within-cluster Jensen-Shannon divergence" while simultaneously maximizing the "between-cluster Jensen-Shannon divergence". In comparison to the previously proposed agglomerative strategies our divisive algorithm is much faster and achieves comparable or higher classification accuracies. We further show that feature clustering is an effective technique for building smaller class models in hierarchical classification. We present detailed experimental results using Naive Bayes and Support Vector Machines on the 20Newsgroups data set and a 3-level hierarchy of HTML documents collected from the Open Directory project (www.dmoz.org).

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v2026.09.13