Comparison of robot morphologies and base positioning for welding applications (2407.05720v1)

Abstract: This article undertakes a comprehensive examination of two distinct robot morphologies: the PUMA-type arm (Programmable Universal Machine for Assembly) and the UR-type robot (Universal Robots). The primary aim of this comparative analysis is to assess their respective performances within the specialized domain of welding, focusing on predefined industrial application scenarios. These scenarios encompass a range of geometrical components earmarked for welding, along with specified welding paths, spatial constraints, and welding methodologies reflective of real-world scenarios encountered by manual welders. The case studies presented in this research serve as illustrative examples of Weez-U Welding practices, providing insights into the practical implications of employing different robot morphologies. Moreover, this study distinguishes between various base positions for the robot, thereby aiding welders in selecting the optimal base placement aligned with their specific welding objectives. By offering such insights, this research facilitates the selection of the most suitable architecture for this particular range of trajectories, thus optimizing welding efficiency and effectiveness. A departure from conventional methodologies, this study goes beyond merely considering singularities and also delves into the analysis of collisions between the robot and its environment, contingent upon the robot's posture. This holistic approach offers a more nuanced understanding of the challenges and considerations inherent in deploying robotic welding systems, providing valuable insights for practitioners and researchers alike in the field of robotic welding technology.

• The paper shows that UR-type robots outperform PUMA-type ones in welding tasks, with up to 38% improvement in vertical and horizontal trajectories.
• The methodology uses ROS2, the Moveit toolbox, and Newton-Euler kinematics to simulate trajectory feasibility and perform collision and singularity detection.
• Findings highlight the critical role of optimal base positioning and spatial planning in addressing industrial welding constraints.

Comparative Analysis of Robot Morphologies and Base Positioning in Welding Applications

The paper provides a detailed comparative analysis of two robot morphologies—PUMA-type and UR-type robots—within the context of robotic welding applications. Focusing on predefined application scenarios typical in industrial settings, the research evaluates the efficiency and practical applicability of these morphologies, highlighting critical factors such as spatial constraints, collision analysis, and optimal base positioning to facilitate better decision-making for practitioners.

Study Objectives and Scenarios

The primary objective of this paper is the assessment of PUMA-type and UR-type robot morphologies in welding tasks, which range from simple linear welds to complex paths in constrained environments. Five distinct application scenarios were considered, including vertical and horizontal welds, contour tracking with varied base positions, and trajectory execution in highly constrained settings typical of industrial welding environments. Each scenario was developed to mimic real-world applications, incorporating considerations such as joint speed, torque limits, and singularity avoidance.

Simulation Methodology and Tools

The authors employed ROS2 for trajectory simulation, leveraging the Moveit toolbox for motion control, singularity and collision detection, and trajectory feasibility analysis. The paper adopted a kinematic and static torque approach using the Newton-Euler method to calculate motor loads, ensuring that the welding scenarios were adequately modeled from both kinematic and dynamic perspectives. The research critically addresses the $n$-solution space inherent to each robot's inverse kinematics, emphasizing the diversity of feasible postures across varying scenarios.

Numerical Results and Analysis

In their findings, the UR-type morphology demonstrated superior performance across several application cases, achieving extended vertical and horizontal welding distances and managing time-efficient contour tracking attributed to its parallel joint structure. Specifically, the UR-type robot excelled in vertical and horizontal trajectory cases, with margin advantages of 38% and 34% over the PUMA-type, respectively. However, it exhibited limitations in complex, confined spaces without optimal base adjustment. Conversely, the PUMA-type robot displayed more consistent posture performance but limited range, especially pronounced in trajectory and complex track cases.

The paper's quantitative approach to comparing robot morphologies introduces a multifaceted analysis, depicting the UR-type's enhanced spatial reach and adaptability, contrasted by the PUMA-type's stability over postures. High-torque and velocity demand at reach limits for these morphologies were noted as potential constraints, effectively mapped within the application's feasibility analysis framework.

Implications and Future Directions

This paper provides substantive insights into the selection of robot morphology and base positioning for specific industrial applications. It underscores the importance of effective spatial planning and posture optimization, positing that the integration of detailed environmental constraints and task-specific dynamics must inform robot choice and deployment strategy. These findings have direct implications for robotic arm design, particularly in enhancing dexterity and collision-avoidance capabilities.

Future research directions could expand upon these findings by evaluating the impact of tool dynamics (e.g., the welding torch and hose) on robot performance. Additionally, the work suggests the potential development of an advanced algorithmic framework for auto-optimizing base placement specific to varied morphologies, optimizing welding efficacy and extending these applications into more diverse and constraint-heavy industrial scenarios.

In conclusion, the paper contributes valuable empirical data and a methodological framework for assessing robotic welders, allowing for informed decisions on morphology selection and strategic placement, thus advancing the body of knowledge in robotic welding technology applications within constrained environments.