CLEAR-MP: Clearance Learning-Based Efficient Motion Planning for Dual-Arm Robots Under End-Effector Orientation Constraints

Dual-arm robotic manipulation of liquid or biochemical reagents poses critical challenges due to high-dimensional configuration spaces, stringent task-specific end-effector orientation requirements to prevent spillage, frequent inter-arm collisions, and cluttered experimental environments. This paper introduces CLEAR-MP (Clearance Learning-Based Efficient Motion Planning for Dual-Arm Robots under End-Effector Orientation Constraints), a modular framework that integrates multiple innovations: a decoupled learning-driven collision estimation module–comprising aPairwise Link Clearance Networkfor self-collision and aClearance Inference Networkfor environmental obstacles, aLearning-Driven Bidirectional Parallel Search Strategyfor accelerated tree expansion, parallel Cartesian batch sampling for efficient candidate generation, fast inverse-kinematics mapping, andLearning-Guided Batch Shortcut Optimizationto refine trajectories. Together, these components generate smooth, safety-certified paths with substantially reduced planning time and path length. Extensive simulations and real-robot experiments show that CLEAR-MP achieves an average path length of 2.391 m, average planning time of 3.529 s, outperforming state-of-the-art baselines by over 50% in computation and 40% in trajectory quality while maintaining strong generalization without retraining.