10 accepted papers
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized sign
Probabilistic state estimation is essential for robots navigating uncertain environments. Accurately and efficiently managing uncertainty in estimated states is key to robust robotic operation. However, nonlinearities in robotic platforms pose significant challenges that require advanced estimation
This paper addresses a spatial coverage optimization problem where multiple heterogeneous sensors are deployed in a convex environment with a known priority function. Each sensor's coverage is defined by an anisotropic spatial distribution. We introduce the Stein Coverage algorithm, a distribution-m
We propose a distributed deployment solution for a group of networked agents that should provide a service for a large set of targets, which densely populate a finite area. The agents are heterogeneous in the sense that their quality of service (QoS), modeled as spatial Gaussian distribution, is dif
Loosely coupled multi-agent estimation algorithms such as covariance intersection (CI) for track-to-track fusion, and discorrelated minimum variance (DMV) and practical estimated cross-covariance minimum variance (PECMV) for cooperative localization (CL), which account for inter-agent correlations i
This letter proposes a data-driven method for learning convergent control policies from offline data using Contraction theory. Contraction theory enables constructing a policy that makes the closed-loop system trajectories inherently convergent towards a unique trajectory. At the technical level, id
In cooperative localization (CL), communicating mobile agents use inter-agent relative measurements to improve their dead-reckoning-based global localization. Measurement scheduling enables an agent to decide which subset of available inter-agent relative measurements it should process when its comp
This letter proposes a novel adaptive sample space-based Viterbi algorithm for target localization in an online manner. The method relies on discretizing the target's motion space into cells representing a finite number of hidden states. Then, the most probable trajectory of the tracked target is co
This letter studies the measurement scheduling problem for a group of N mobile robots moving on a flat surface that are preforming cooperative localization (CL). We consider a scenario in which due to the limited on-board resources such as battery life and communication bandwidth only a given number
For a team of mobile robots with limited onboard resources, we propose a partially decentralized implementation of an extended Kalman filter for cooperative localization. In the proposed algorithm, unlike a fully centralized scheme that requires, at each timestep, information from the entire team to…