SAMS LaboratoryTexas State University
Research at SAMS
Our work brings dynamics, decision-making, learning, optimization, and control together across ground vehicles, aerial systems, and coordinated robot teams.
Game-theoretic and interaction-aware autonomous-vehicle decisions for mixed traffic, including cooperative, competitive, and uncertain human behavior.
Methods for estimating driver intent, preferences, and future motion so autonomous vehicles can anticipate the effects of their actions on surrounding road users.
Control and learning architectures that pursue performance while enforcing explicit safety requirements under uncertainty and changing environments.
Adaptive and robust control methods for individual vehicles and connected vehicle groups, designed to improve following behavior, traffic flow, and stability.
Models and algorithms for planning and coordinating electric aircraft and supporting infrastructure across advanced air mobility networks.
Scalable coordination strategies for heterogeneous ground and aerial robots operating with shared task, energy, terrain, and communication constraints.
Emerging work on using foundation models to interpret goals and support high-level decisions while keeping motion authorization and safety enforcement in a verified control layer.