Adaptive Optimal Collision Avoidance of Dynamic Agents for Differential-Drive Robots

Martinez-Baselga, Diego ; Lanaspa, Diego ; Riazuelo, Luis (Universidad de Zaragoza) ; Montano, Luis (Universidad de Zaragoza)

Resumen: Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models.

Idioma: Inglés

DOI: 10.3390/robotics15040072

Año: 2026

Publicado en: Robotics 15, 4 (2026), 72
ISSN: 2218-6581

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2022-139615OB-I00
Financiación: info:eu-repo/grantAgreement/ES/AEI/PRE2020-094415
Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)