doi:10.3390/robotics15040072engMartinez-Baselga, DiegoLanaspa, DiegoRiazuelo, LuisMontano, LuisAdaptive Optimal Collision Avoidance of Dynamic Agents for Differential-Drive RobotsART-2026-149135Efficient 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.2026http://zaguan.unizar.es/record/17105210.3390/robotics15040072http://zaguan.unizar.es/record/171052oai:zaguan.unizar.es:171052info:eu-repo/grantAgreement/ES/AEI/PID2022-139615OB-I00info:eu-repo/grantAgreement/ES/AEI/PRE2020-094415info:eu-repo/grantAgreement/ES/DGA/T45-23RRobotics 15, 4 (2026), 72byhttps://creativecommons.org/licenses/by/4.0/deed.esinfo:eu-repo/semantics/openAccess