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

Nicolas Gautier, Yves Guillermit, Mathieu Porez, David Lemoine, Damien Chablat

TL;DR

The paper tackles robust base placement for a Fanuc CRX10iA/L under positioning uncertainties by integrating IK-aware trajectory simulation with a PSO-driven search over base configurations. It introduces a robustness criterion based on the largest inscribed circle within feasibility regions, obtained via alpha-shapes and Voronoi-based distance metrics, to pinpoint optimal placements. The workflow jointly handles inverse kinematics with multiple solutions, trajectory validation against singularities and joint limits, and self-collision checks, while the PSO with dynamic clustering efficiently explores large spaces and avoids exhaustive enumeration. Experimental results in a plasma-cutting scenario on a mobile welding base demonstrate reduced calibration effort and improved reproducibility, highlighting practical benefits for online programming and flexible production environments.

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.

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

TL;DR

The paper tackles robust base placement for a Fanuc CRX10iA/L under positioning uncertainties by integrating IK-aware trajectory simulation with a PSO-driven search over base configurations. It introduces a robustness criterion based on the largest inscribed circle within feasibility regions, obtained via alpha-shapes and Voronoi-based distance metrics, to pinpoint optimal placements. The workflow jointly handles inverse kinematics with multiple solutions, trajectory validation against singularities and joint limits, and self-collision checks, while the PSO with dynamic clustering efficiently explores large spaces and avoids exhaustive enumeration. Experimental results in a plasma-cutting scenario on a mobile welding base demonstrate reduced calibration effort and improved reproducibility, highlighting practical benefits for online programming and flexible production environments.

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 -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.

Paper Structure

This paper contains 10 sections, 28 equations, 7 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Simulation of robotic trajectories
  3. Computation of inverse kinematic model
  4. Validation of a robot trajectory
  5. Detection of robot self-collisions
  6. Optimization of the Robot's Position
  7. Exploring configuration spaces with a PSO algorithm
  8. Clustering and determination of the largest inscribed circle
  9. Experimentations
  10. Conclusions

Figures (7)

  • Figure 1: Four residual curves corresponding to the four couples $[\epsilon_1, \epsilon_2]$ for the pose $[-0.2406, -0.1188, 0.5603, 2.6204, 1.1236, 0.4276]$ as a function of $q_4$
  • Figure 2: Visualization of the 16 inverse kinematic solutions for the pose $[-0.2406, -0.1188, 0.5603, 2.6204, 1.1236, 0.4276]$.
  • Figure 3: Workpiece and its associated saddle-shaped trajectory.
  • Figure 4: Percentage of feasible trajectory for different saddle-shaped trajectories.
  • Figure 5: Exploration with the PSO algorithm on the previous examples: gradient from green to yellow indicates the score in ascending order when blue is the minimal score.
  • ...and 2 more figures