Risk-Aware Adaptive Safety Margins for Model Predictive Control with Orientation–Motion Coupled Barrier Functions in Dynamic Environments

Safe navigation in dynamic environments remains challenging because classical distance-based constraints ignore the coupling between a robot’s translational motion and attitude dynamics, and fixed safety margins are either over-conservative or risky under varying uncertainty and approach speed. This paper presents a Risk-Aware Model Predictive Control (RA-MPC) framework that addresses both limitations through two integrated components. First, we introduce Orientation–Motion Coupled Control Barrier Functions (O-MCBFs) that enforce unified safety constraints linking collision avoidance with attitude stability limits, preventing dangerous pose configurations during dynamic obstacle avoidance. Second, we develop Risk-Aware Adaptive Margins (RAAMs) that compute time-varying safety buffers based on relative velocity, robot braking capability, and prediction uncertainty, enabling context-dependent safety–efficiency trade-offs without manual parameter tuning. The proposed method integrates these components into a quadratic programming formulation within MPC, ensuring real-time computational tractability. Experimental results demonstrate higher success rates, smoother trajectories, and improved progress toward the goal, with no observed safety violations under the tested conditions. These findings indicate that coupling pose-space safety with risk-adaptive margins provides a principled and practical path to safe and efficient navigation in dynamic scenes.