Selected work

Representative Papers

Spatiotemporal Tube-Based Safety-Certificate for Autonomous Navigation of Articulated Vehicles

Aug 14, 2026

This study addresses the safety challenges of autonomous navigation for articulated vehicles in narrow environments by proposing a spatiotemporal tube planning method that integrates kinematic and swing constraints. The approach employs an admissibility correction mechanism to generate route safety certificates, rigorously ensuring that the entire trailer sequence remains within the designated road corridor and effectively mitigating boundary violation risks under complex physical constraints. Simulation experiments validate the framework’s effectiveness in truck-trailer systems. Consequently, this work provides a solution for safe motion planning of articulated vehicles in confined spaces that balances theoretical rigor with engineering practicality, offering a robust methodology for navigating constrained operational domains while maintaining strict safety guarantees throughout the vehicle's articulation dynamics.

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Decoupled Thrust-Axis Attitude Control Using Quaternions for Chandrayaan-3 Lunar Landing Mission

May 28, 2026

This study addresses the adverse guidance–control interactions in lunar landing caused by three-axis coupling inherent in conventional quaternion-based attitude control. To resolve this issue, the work proposes a novel decoupled control method that, for the first time within a quaternion framework, enables independent control of the thrust-axis orientation. This approach retains the singularity-free advantage of quaternions while effectively circumventing the guidance–control coupling induced by the shortest-path property of standard quaternion interpolation. Integrated with a polynomial guidance algorithm and state estimation in the selenocentric coordinate frame, the proposed scheme successfully enabled the Chandrayaan-3 mission to achieve a high-precision soft landing near the lunar south pole, thereby demonstrating its effectiveness and robustness.

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Real-Time Retargeting Using Controllability Boundary for Chandrayaan-3 Lunar Landing

May 28, 2026

This work proposes a data-driven, real-time retargeting guidance strategy for lunar landing missions, which was successfully deployed for the first time in an actual mission—Chandrayaan-3—to address scenarios where the nominal landing site becomes infeasible. The approach integrates a near-fuel-optimal descent trajectory with a high-level decision-making mechanism to rapidly redirect the lander toward an alternative safe zone. Feasibility of candidate targets is efficiently assessed using a convex representation of controllability boundaries, enabling swift evaluation without compromising computational tractability. The framework combines convex optimization with real-time trajectory replanning to ensure responsive and robust guidance updates. Both pre-flight simulations and in-flight telemetry from Chandrayaan-3 confirm the effectiveness and reliability of the proposed architecture under operational constraints.

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Elastic Scheduling of Intermittent Query Processing in a Cluster Environment

May 08, 2026

This work addresses the challenge of meeting deadlines while minimizing costs for concurrent streaming queries in cluster environments under dynamic workloads and unpredictable query arrivals. The paper proposes an intermittent query scheduling framework tailored for elastic parallel execution, which, to the best of our knowledge, is the first to integrate elastic resource provisioning into intermittent query processing. By dynamically scaling cluster nodes up or down, the approach simultaneously satisfies windowed query deadlines and reduces resource expenditure. Implemented on Apache Spark and deployed on AWS EMR, the system combines elastic computing with batch-oriented scheduling algorithms. Experimental evaluations on TPC-H and Yahoo Streaming Benchmark datasets demonstrate significant improvements over both static configurations and Spark Streaming, achieving superior timeliness and cost efficiency.

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Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data

Apr 28, 2026

This study addresses the challenge of radiometric inconsistency in multi-mission lunar orbital imagery caused by variations in illumination, sensors, and imaging conditions, which hinders seamless mosaicking. The work proposes the first cross-mission radiometric normalization framework based on conditional generative adversarial networks (cGANs), mapping Chandrayaan-2 Terrain Mapping Camera (TMC) data—augmented with SELENE imagery—as inputs to the Lunar Reconnaissance Orbiter Camera Wide Angle Camera (LROC WAC) reference standard. The approach employs a U-Net generator and a PatchGAN discriminator, combined with a patch-based training scheme and an overlap-aware inference strategy to preserve large-scale structural continuity while effectively eliminating visible seams. Experimental results demonstrate that the method significantly outperforms conventional histogram matching in terms of SSIM, PSNR, and RMSE metrics, substantially improving tonal uniformity and structural consistency across multi-source lunar images.

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Recent publications

Latest Papers

Spatiotemporal Tube-Based Safety-Certificate for Autonomous Navigation of Articulated Vehicles

Aug 14, 2026

This study addresses the safety challenges of autonomous navigation for articulated vehicles in narrow environments by proposing a spatiotemporal tube planning method that integrates kinematic and swing constraints. The approach employs an admissibility correction mechanism to generate route safety certificates, rigorously ensuring that the entire trailer sequence remains within the designated road corridor and effectively mitigating boundary violation risks under complex physical constraints. Simulation experiments validate the framework’s effectiveness in truck-trailer systems. Consequently, this work provides a solution for safe motion planning of articulated vehicles in confined spaces that balances theoretical rigor with engineering practicality, offering a robust methodology for navigating constrained operational domains while maintaining strict safety guarantees throughout the vehicle's articulation dynamics.

0 citationsRead paper

Decoupled Thrust-Axis Attitude Control Using Quaternions for Chandrayaan-3 Lunar Landing Mission

May 28, 2026

This study addresses the adverse guidance–control interactions in lunar landing caused by three-axis coupling inherent in conventional quaternion-based attitude control. To resolve this issue, the work proposes a novel decoupled control method that, for the first time within a quaternion framework, enables independent control of the thrust-axis orientation. This approach retains the singularity-free advantage of quaternions while effectively circumventing the guidance–control coupling induced by the shortest-path property of standard quaternion interpolation. Integrated with a polynomial guidance algorithm and state estimation in the selenocentric coordinate frame, the proposed scheme successfully enabled the Chandrayaan-3 mission to achieve a high-precision soft landing near the lunar south pole, thereby demonstrating its effectiveness and robustness.

0 citationsRead paper

Real-Time Retargeting Using Controllability Boundary for Chandrayaan-3 Lunar Landing

May 28, 2026

This work proposes a data-driven, real-time retargeting guidance strategy for lunar landing missions, which was successfully deployed for the first time in an actual mission—Chandrayaan-3—to address scenarios where the nominal landing site becomes infeasible. The approach integrates a near-fuel-optimal descent trajectory with a high-level decision-making mechanism to rapidly redirect the lander toward an alternative safe zone. Feasibility of candidate targets is efficiently assessed using a convex representation of controllability boundaries, enabling swift evaluation without compromising computational tractability. The framework combines convex optimization with real-time trajectory replanning to ensure responsive and robust guidance updates. Both pre-flight simulations and in-flight telemetry from Chandrayaan-3 confirm the effectiveness and reliability of the proposed architecture under operational constraints.

0 citationsRead paper

Elastic Scheduling of Intermittent Query Processing in a Cluster Environment

May 08, 2026

This work addresses the challenge of meeting deadlines while minimizing costs for concurrent streaming queries in cluster environments under dynamic workloads and unpredictable query arrivals. The paper proposes an intermittent query scheduling framework tailored for elastic parallel execution, which, to the best of our knowledge, is the first to integrate elastic resource provisioning into intermittent query processing. By dynamically scaling cluster nodes up or down, the approach simultaneously satisfies windowed query deadlines and reduces resource expenditure. Implemented on Apache Spark and deployed on AWS EMR, the system combines elastic computing with batch-oriented scheduling algorithms. Experimental evaluations on TPC-H and Yahoo Streaming Benchmark datasets demonstrate significant improvements over both static configurations and Spark Streaming, achieving superior timeliness and cost efficiency.

0 citationsRead paper

Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data

Apr 28, 2026

This study addresses the challenge of radiometric inconsistency in multi-mission lunar orbital imagery caused by variations in illumination, sensors, and imaging conditions, which hinders seamless mosaicking. The work proposes the first cross-mission radiometric normalization framework based on conditional generative adversarial networks (cGANs), mapping Chandrayaan-2 Terrain Mapping Camera (TMC) data—augmented with SELENE imagery—as inputs to the Lunar Reconnaissance Orbiter Camera Wide Angle Camera (LROC WAC) reference standard. The approach employs a U-Net generator and a PatchGAN discriminator, combined with a patch-based training scheme and an overlap-aware inference strategy to preserve large-scale structural continuity while effectively eliminating visible seams. Experimental results demonstrate that the method significantly outperforms conventional histogram matching in terms of SSIM, PSNR, and RMSE metrics, substantially improving tonal uniformity and structural consistency across multi-source lunar images.

0 citationsRead paper