Rule-Based Interpretable AI for Concurrent Collision Detection in Industrial Robot Manipulators

Abstract

Safe human-robot collaboration in industrial environments demands collision detection systems that are both computationally efficient and interpretable. Existing approaches — based on geometric modeling, bounding volume hierarchies, or physics engines — impose significant computational overhead that limits real-time performance, particularly for high-degree-of-freedom manipulators operating in complex workspaces. This reliance on expensive computation also perpetuates manual path teaching and validation practices that reduce deployment efficiency and increase operator workload.This paper proposes a rule-based artificial intelligence framework that replaces iterative geometric calculations with a learned, symbolic representation of the collision function. Joint configurations are sampled across the robot's operational space within a simulation environment and labeled according to their collision state. An ensemble learning method is trained on this dataset to approximate the collision boundary directly from joint space, bypassing the need for explicit kinematic or geometric modeling at query time.The central contribution of this work is the systematic extraction of decision rules from the trained ensemble model. These rules are compiled into a structured knowledge base, which an inference engine queries to evaluate collision states in constant time — independent of scene complexity or robot configuration. This architecture offers two critical advantages over classical methods: a substantial reduction in computational cost during operation, and a transparent, inspectable representation of system behavior that supports validation and human oversight.The proposed method is evaluated on a six-degree-of-freedom industrial manipulator in a controlled simulation environment. Results demonstrate a significant speed-up in collision checking relative to physics-based engine calculations, achieving real-time performance suitable for integration into motion planning pipelines. Prediction accuracy remains within acceptable bounds for practical deployment, and the rule-based structure allows collision logic to be audited without specialized simulation tools.From a human factors perspective, the approach reduces dependence on manual robot teaching and path validation — tasks that remain labor-intensive and error-prone in current industrial practice. By lowering the computational and operational barriers to collision-safe motion planning, the proposed system supports safer, more efficient human-robot collaboration in manufacturing environments.

Keywords: Human-robot Collaboration, Collision Detection, Industrial Robot Manipulator, Explainable AI, Rule-based Systems, Real-time Motion Planning

DOI: 10.54941/ahfe1007681