Proposed an asynchronous block coordinate descent algorithm for distributed trajectory estimation in robotics, reducing communications by up to 96.9% and achieving exponential convergence.

This paper proposes a Fisher information density (FID)-constrained trajectory sharing scheme for robot collision avoidance under GDPR regulations, achieving better privacy-utility tradeoff than fixed-…

This paper proves convergence of belief propagation algorithms for multipath data association to a unique fixed point.

The paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework that enables stable, scalable consensus control for large swarms of quadcopters using only local neighbo…

This paper derives the Cramer-Rao lower bound for target state estimation in a distributed radar sensing system, revealing a tradeoff between update rate and quantization fidelity.

This paper introduces Path Consistency Scoring (PCS), a framework that evaluates router geolocation as a path-level consistency problem and produces a path consistency score based on a Hidden Markov M…

This paper compares the performance of open-loop and closed-loop filters in inertial navigation systems using simulations.

This paper investigates how network perturbations can alter the asymptotic agreement trajectory in distributed coordination systems, proving fragility in standard cooperative output regulation schemes…

The paper proposes an uncertainty-aware, decentralized fusion layer for multi-UAV systems that significantly improves 3D localization robustness by incorporating neighbor constraints and handling faul…

The paper proposes BitTP, a lightweight bitlinear architecture that quantizes LLM-based trajectory predictors to 1.58-bit weights while keeping activations full-precision, enabling high-performance de…

The paper proposes a new paradigm for trajectory forecasting by training models with metric-agnostic probabilistic objectives and optimizing metrics as downstream tasks.

A lightweight sensor-driven Lévy walk controller is presented for efficient autonomous exploration of minimal-sensing, resource-constrained nano-UAVs.

The paper proposes a scalable, distributed approach for constrained Multi-Agent Reinforcement Learning by using local consensus over dual variables to ensure global constraint satisfaction without cen…