TY - JOUR

T1 - Enhancing Visual Inspection in Remanufacturing

T2 - 58th CIRP Conference on Manufacturing Systems - CMS 2025

AU - Koch, Dominik

AU - Kaiser, Jan Philipp

AU - Stamer, Florian

AU - Stark, Rainer

AU - Lanza, Gisela

N1 - Conference code: 58

PY - 2025

Y1 - 2025

N2 - This paper presents an approach for solving the View Planning Problem (VPP) in robotic inspection using Reinforcement Learning (RL). Building on a prior framework, this work takes a significant step forward by integrating a detailed robotic simulation environment with essential modules for trajectory and reachability planning. This allows for the development of an RL agent that not only selects adaptive viewpoints but also considers kinematic constraints and collision-free paths, which are crucial for practical, real-world inspections. The study specifically targets the initial inspection of returned products with high variability, demonstrating the feasibility of RL to manage complex tasks in remanufacturing. The RL-based solution is evaluated using Soft Actor-Critic (SAC) and Proximal Policy Optimization algorithms, with SAC showing superior performance. The learned strategies were validated on a real inspection station, showing the capability of using RL based inspection strategies. This research offers a robust, adaptable solution for inspection challenges, bridging the gap between theoretical models and application-ready inspection systems.

AB - This paper presents an approach for solving the View Planning Problem (VPP) in robotic inspection using Reinforcement Learning (RL). Building on a prior framework, this work takes a significant step forward by integrating a detailed robotic simulation environment with essential modules for trajectory and reachability planning. This allows for the development of an RL agent that not only selects adaptive viewpoints but also considers kinematic constraints and collision-free paths, which are crucial for practical, real-world inspections. The study specifically targets the initial inspection of returned products with high variability, demonstrating the feasibility of RL to manage complex tasks in remanufacturing. The RL-based solution is evaluated using Soft Actor-Critic (SAC) and Proximal Policy Optimization algorithms, with SAC showing superior performance. The learned strategies were validated on a real inspection station, showing the capability of using RL based inspection strategies. This research offers a robust, adaptable solution for inspection challenges, bridging the gap between theoretical models and application-ready inspection systems.

KW - Automation

KW - Reinforcement Learning

KW - Robotics

KW - Visual inspection

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=105009412682&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2025.02.228

DO - 10.1016/j.procir.2025.02.228

M3 - Conference article in journal

AN - SCOPUS:105009412682

VL - 134

SP - 939

EP - 944

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

Y2 - 13 April 2025 through 16 April 2025

ER -