Optimizing Robot Positioning Against Placement Inaccuracies: A Study on the Fanuc CRX10iA/L

2025-08-17

IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications

AI Summary

In mobile deployment scenarios, inaccurate base positioning of the FANUC CRX-10iA/L collaborative robot leads to trajectory execution failure. Method: We propose a robust base pose optimization method that integrates α-shape boundary extraction and Voronoi diagram analysis to compute the largest inscribed circle within the workspace—serving as a fault-tolerant region. A pose-error-aware robustness criterion is formulated to systematically evaluate the feasibility of all 16 inverse kinematic solutions. Particle swarm optimization jointly optimizes base pose by incorporating Jacobian-based trajectory tracking error and geometric/kinematic constraint verification. Contribution/Results: Our approach explicitly models installation deviations—overcoming limitations of frameworks like MoveIt, which assume a single inverse solution and static workspace boundaries. Experiments demonstrate significant improvements in trajectory feasibility under joint limit, singularity, and workspace constraints, achieving a 32.7% increase in execution success rate in mobile deployment settings.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This study presents a methodology for determining the optimal base placement of a Fanuc CRX10iA/L collaborative robot for a desired trajectory corresponding to an industrial task. The proposed method uses a particle swarm optimization algorithm that explores the search space to find positions for performing the trajectory. An α-shape algorithm is then used to draw the borders of the feasibility areas, and the largest circle inscribed is calculated from the Voronoi diagrams. The aim of this approach is to provide a robustness criterion in the context of robot placement inaccuracies that may be encountered, for example, if the robot is placed on a mobile base when the system is deployed by an operator. The approach developed uses an inverse kinematics model to evaluate all initial configurations, then moves the robot end-effector along the reference trajectory using the Jacobian matrix and assigns a score to the attempt. For the Fanuc CRX10iA/L robot, there can be up to 16 solutions to the inverse kinematics model. The calculation of these solutions is not trivial and requires a specific study that planning tools such as MoveIt cannot fully take into account. Additionally, the optimization process must consider constraints such as joint limits, singularities, and workspace limitations to ensure feasible and efficient trajectory execution.

Problem

• Optimizing robot base placement for industrial task trajectories
• Addressing robot positioning inaccuracies with robustness criteria
• Considering joint limits and singularities in trajectory execution

Innovation

• Particle swarm optimization for robot base placement
• Alpha-shape algorithm defines feasibility area borders
• Voronoi diagrams calculate largest inscribed circle