Search Results (8)

This article focuses on the design, development and optimization of a mechanical system with the aim of increasing the efficiency of the production process. The article describes the issues involved in the production of molds used for EPS (Expanded Polystyrene) and EPP (Expanded Polypropylene) materials, specifically the assembly of mold nozzles. Currently, the assembly of nozzles is performed manually, and the proposed solution aims to automate this process using software and robotics. The solution involves scanning the mounting holes and then modifying the mold model in Siemens NX, based on which a trajectory is generated in the virtual environment of RoboDK software. Communication between Siemens NX and RoboDK software is implemented via a Python algorithm using NXOpen and RoboDK API (Application Programming Interface) libraries. The proposed tool has flexible settings and is not dependent on a robotic arm or tool. The result is a prototype software tool for offline programming of automated assembly, which is adapted to different hole layouts, allowing its use in small-batch production in the future. The proposed tool has flexible settings and is not dependent on a specific robotic arm or tool. The solution was validated through comprehensive simulation testing in the RoboDK environment, demonstrating significant potential for time reduction and process optimization. Full article

To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, QC Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated. Full article

Traditionally, the determination of the Denavit–Hartenberg (DH) parameters for serial robotic manipulators is a manual process that depends on manufacturer documentation or user-defined conventions, often leading to inefficiency and ambiguity in DH frame placement and parameters. This study introduces a universal and systematic methodology for automatically deriving DH parameters directly from a robot’s zero configuration, using only the geometric relationships between consecutive joint axes. The approach was implemented in a MATLAB-based kinematics toolbox capable of computing both the classical and modified DH parameters. In addition to parameter extraction, the toolbox integrates workspace visualization, manipulability and dexterity analysis, and a slicing and alpha-shape algorithm for accurate workspace volume computation. Validation was conducted on multiple industrial robots by comparing the extracted parameters with the manufacturer data and the RoboDK models. Benchmark studies confirmed the accuracy of the volume estimation, yielding an absolute percentage error of less than 4%. While the current implementation relies on RoboDK models for verification and requires the manual tuning of the alpha-shape parameter, the toolbox provides a reproducible and extensible framework for research, education, and robot design. Full article

Problem: Existing Human–Robot Collaboration (HRC) allocators cannot react at a sub-second scale while accounting for worker fatigue. Objective: We designed a fatigue-aware combinatorial auction executed every 100 ms. Method: A human and a FANUC robot submit bids combining execution time, predicted energy, and real-time fatigue; a greedy algorithm (≤1 ms) with a $1-1/e$ approximation guarantee and O (|Bids| log |Bids|) complexity maximizes utility. Results: In 1000 RoboDK episodes, the framework increases active cycles·min⁻¹ by 20%, improves robot utilization by +10.2 percentage points, reduces per cycle fatigue by 4%, and raises the collision-free rate to 99.85% versus a static baseline (p < 0.001). Contribution: We provide the first transparent, sub-second, fatigue-aware allocation mechanism for Industry 5.0, with quantified privacy safeguards and a roadmap for physical deployment. Unlike prior auction-based or reinforcement learning approaches, our model uniquely integrates a sub-second ergonomic adaptation with a mathematically interpretable utility structure, ensuring both human-centered responsiveness and system-level transparency. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method for integrating real sensor signals into RoboDK simulations, bridging the gap between virtual models and real-world dynamics. The proposed system utilizes an Arduino-based data acquisition module and a custom Python script to establish real-time communication between physical sensors and RoboDK’s simulation environment. Unlike traditional simulations that rely on predefined simulated signals or manually triggered virtual inputs, our approach enables dynamic real-time interactions based on live sensor data. The system supports both analog and digital signals and is validated through latency measurements, demonstrating an average end-to-end delay of 23.97 ms. These results confirm the feasibility of real sensor integration into RoboDK, making the system adaptable to various industrial applications. This framework provides a scalable foundation for researchers and engineers to develop enhanced simulation environments that more accurately reflect real industrial conditions. Full article

ISO 9283 is a significant guiding standard for assessing the performance characteristics of robots. The main objective of the present paper was to verify the repeatability of the Panasonic TM-2000 welding robot at a manufacturing company. The paper describes the workflow of the robot control program in the simulation software RoboDK, which created a complete welding station. The measurement, as well as the simulation, were possible thanks to designing the measuring device, the imaginary ISO cube, the measuring plane, the measuring points, and last but not least, the cycle that determined in which order the points were measured. The analytical part of the paper resulted in a direct measurement of the position repeatability of the welding robot. A total of five points were measured for the X-axis and five points for the Y-axis. Each point was recorded 30 times, with measurements taken in the positive direction of motion. The results were compared with the value given by the manufacturer, and the measured deviations were presented graphically. Full article