collaborators

14 papers

cs.RO2026

Localization in Spatiotemporal Fields via Environmental PDEs

Jose Fuentes, Abdullah Al Redwan Newaz, Ana Cavalcanti +1

This paper proposes a localization framework that uses spatiotemporal fields governed by partial differential equations (PDEs) as localization signatures. Two PDE classes are consi…

cs.RO2026

A New Implementation of NeoSLAM and a Comparative Evaluation with RatSLAM

Joao Victor T. Borges, Fabio Coelho, Paulo Padrao +4

This paper presents a new implementation of the NeoSLAM algorithm. The proposed version is a complete rewrite of NeoSLAM into a modular architecture using modern frameworks that, t…

cs.RO2026

BOWConnect: Parallel Bayesian Optimization over Windows with Learned Local Cost Maps for Sample-Efficient Kinodynamic Motion Planning

Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes +1

This paper presents BOWConnect, a bidirectional parallel kinodynamic motion planner that addresses three fundamental limitations of existing sampling-based methods: sample ineffici…

cs.RO2026

Muninn: Your Trajectory Diffusion Model But Faster

Gokul Puthumanaillam, Hao Jiang, Ruben Hernandez +4

Diffusion-based trajectory planners can synthesize rich, multimodal robot motions, but their iterative denoising makes online planning and control prohibitively slow. Existing acce…

cs.RO2026

Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests

Sourav Raxit, Jose Fuentes, Paulo Padrao +4

This letter presents an energy-efficient multi-robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing obstacles and no-fly zones (…

cs.RO2026

Multi-Robot Trajectory Planning via Constrained Bayesian Optimization and Local Cost Map Learning with STL-Based Conflict Resolution

Sourav Raxit, Abdullah Al Redwan Newaz, Jose Fuentes +3

We address multi-robot motion planning under Signal Temporal Logic (STL) specifications with kinodynamic constraints. Exact approaches face scalability bottlenecks and limited adap…