다중 작업 수행을 위한 영공간 여유도 기반 가중치 계층 제어

초록

As robots increasingly operate in complex and human-interactive environments, hierarchical control frameworks are essential for managing multiple concurrent tasks in redundant manipulators. However, conventional methods often degrade near kinematic singularities or spatial conflicts because lower-priority tasks ignore the system's remaining kinematic redundancy. To resolve this critical issue, we propose a weighted hierarchical control framework based on the null-space margin, which quantifies the remaining kinematic capacity of each joint unoccupied by higher-priority tasks. The proposed method dynamically evaluates each joint's kinematic margin in real-time, translating it into a continuous weight for the pseudoinverse control inputs. This mechanism induces proactive yielding of lower-priority tasks, enabling preemptive singularity avoidance without heavy iterative computational optimization. We validated our approach through dynamic obstacle avoidance and trajectory tracking simulations using a 7-DOF Franka Emika FR3 manipulator. The results demonstrate that the framework successfully executes stable avoidance motions while strictly maintaining safety margins and preventing joint saturation.

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