arXiv subjects

Neelkamal Somisetty

Publications and source records attributed to Neelkamal Somisetty.

4 recordsLinked to original sources

arXiv2025

Lateral String Stability in Autonomous & Connected Vehicle Platoons

This paper addresses the lateral control of Autonomous and Connected Vehicles (ACVs) in a platoon executing an Emergency Lane Change (ELC) maneuver. These maneuvers are typically triggered by emergency signals from the front or rear of the platoon in response to the need to avoid obstacles or allow other vehicles to pass. The study assumes that ACVs maintain reliable connectivity, enabling each following vehicle to access GPS position traces of both the lead and immediately preceding vehicles in the platoon. We demonstrate that lateral string stability in the ACV platoon can be achieved using communicated information solely from the lead and preceding vehicles. Additionally, we present a lateral control framework for ACVs, which helps track a discretized preview of the trajectory constructed from the communicated data. This framework involves constructing two distinct trajectories based on the preview data from the lead and preceding vehicles, calculating the associated errors and lateral control actions for each, and then integrating these to generate a steering command. Numerical results validate the effectiveness of the proposed lateral control scheme.

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arXiv2024

Robust Lateral Control of a Convoy of Autonomous & Connected Vehicles with Limited Preview

This paper addresses the lateral control of Autonomous & Connected Vehicles (ACVs) convoys during Emergency Lane Change (ELC) maneuvers. These maneuvers are initiated in response to emergency cues from either the front or rear of the convoy, responding to the need to avoid obstacles or facilitate the passage of other vehicles. The primary objective of this study is to develop a lateral control scheme for ACVs based on the available information. The foundational assumption in this study is the existence of reliable connectivity among ACVs, wherein each subsequent ACV possesses information concerning the GPS position traces of both the lead and immediately preceding vehicles within the convoy. This connectivity facilitates the construction of a composite ELC trajectory that synthesizes this information, serving as a "discretized" preview of the trajectory to be tracked. The procedural steps include constructing this composite trajectory, determining cross-track error, heading, and yaw rate errors relative to it, and subsequently formulating a lateral control strategy. Furthermore, the paper presents findings on the lateral string stability of ACV convoys across various scenarios, encompassing changes in longitudinal velocity and scenarios where lead vehicle information is unavailable. Numerical and experimental results validate the efficacy of the proposed lateral control scheme for ACV convoys.

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arXiv2024

Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization

This paper addresses the optimization of edge-weighted networks by maximizing algebraic connectivity to enhance network robustness. Motivated by the need for precise robot position estimation in cooperative localization and pose-graph sparsification in Simultaneous Localization and Mapping (SLAM), the algebraic connectivity maximization problem is formulated as a Mixed Integer Semi-Definite Program (MISDP), which is NP-hard. Leveraging spectral graph theoretic methods, specifically Cheeger's inequality, this work introduces novel "Cheeger cuts" to strengthen and efficiently solve medium-scale MISDPs. Further, a new Mixed Integer Linear Program (MILP) is developed for efficiently computing Cheeger cuts, implemented within an outer-approximation algorithm for solving the MISDP. A greedy k-opt heuristic is also presented, producing high-quality solutions that serve as valid lower bounds for Cheeger cuts. Comprehensive numerical analyses demonstrate the efficacy of strengthened cuts via substantial improvements in run times on synthetic and realistic robot localization datasets.

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arXiv2024

Optimal Robust Network Design: Formulations and Algorithms for Maximizing Algebraic Connectivity

This paper focuses on designing edge-weighted networks, whose robustness is characterized by maximizing algebraic connectivity, or the second smallest eigenvalue of the Laplacian matrix. This problem is motivated by cooperative vehicle localization, where accurately estimating relative position measurements and establishing communication links are essential. We also examine an associated problem where every robot is limited by payload, budget, and communication to pick no more than a specified number of relative position measurements. The basic underlying formulation for these problems is nonlinear and is known to be NP-hard. Our approach formulates this problem as a Mixed Integer Semi-Definite Program (MISDP), later reformulated into a Mixed Integer Linear Program (MILP) for obtaining optimal solutions using cutting plane algorithms. We introduce a novel upper-bounding algorithm based on principal minor characterization of positive semi-definite matrices and discuss a degree-constrained lower bounding formulation inspired by robust network structures. In addition, we propose a maximum cost heuristic with low computational complexity to identify high-quality feasible solutions for instances involving up to one hundred nodes. We show extensive computational results corroborating our proposed methods.

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