arXiv subjects

Rohit V. Nanavati

Publications and source records attributed to Rohit V. Nanavati.

2 recordsLinked to original sources

arXiv2025

Mr.MSTE: Multi-robot Multi-Source Term Estimation with Wind-Aware Coverage Control

This paper presents a Multi-Robot Multi-Source Term Estimation (MRMSTE) framework that enables teams of mobile robots to collaboratively sample gas concentrations and infer the parameters of an unknown number of airborne releases. The framework is built on a hybrid Bayesian inference scheme that represents the joint multi-source probability density and incorporates physics-informed state transitions, including source birth, removal, and merging induced by atmospheric dispersion. A superposition-based measurement model is naturally accommodated, allowing sparse concentration measurements to be exploited efficiently. To guide robot deployment, we introduce a wind-aware coverage control (WCC) strategy that integrates the evolving multi-source belief with local wind information to prioritize regions of high detection likelihood. Unlike conventional coverage control or information-theoretic planners, WCC explicitly accounts for anisotropic plume transport when modelling sensor performance, leading to more effective sensor placement for multi-source estimation. Monte Carlo studies demonstrate faster convergence and improved separation of individual source beliefs compared to traditional coverage-based strategies and small-scale static sensor networks. Real-world experiments with CO2 releases using TurtleBot platforms further validate the proposed approach, demonstrating its practicality for scalable multi-robot gas-sensing applications.

cs.RO↗

arXiv2025

Robust Near-Optimal Nonlinear Target Enclosing Guidance

This paper proposes a nonlinear optimal guidance law that enables a pursuer to enclose a target within arbitrary geometric patterns, which extends beyond conventional circular encirclement. The design operates using only relative state measurements and formulates a target enclosing guidance law in which the vehicle's lateral acceleration serves as the steering control, making it well-suited for aerial vehicles with turning constraints. Our approach generalizes and extends existing guidance strategies that are limited to target encirclement and provides a degree of optimality. At the same time, the exact information of the target's maneuver is unnecessary during the design. The guidance law is developed within the framework of a state-dependent Riccati equation (SDRE), thereby providing a systematic way to handle nonlinear dynamics through a pseudo-linear representation to design locally optimal feedback guidance commands through state-dependent weighting matrices. While SDRE ensures near-optimal performance in the absence of strong disturbances, we further augment the design to incorporate an integral sliding mode manifold to compensate when disturbances push the system away from the nominal trajectory, and demonstrate that the design provides flexibility in the sense that the (possibly time-varying) stand-off curvature could also be treated as unknown. Simulations demonstrate the efficacy of the proposed approach.

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