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Robotics industry

Researchers at Brunel University London Develop Innovative 3D Path Planning Method for Mobile Robotic Arms in Non-Destructive Testing

Strategic Insights

— 1 min read

A team of researchers at Brunel University London has proposed a novel 3D path planning method that combines the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm with offline inverse kinematics (IK) checking to address the challenges of coverage completeness and path executability in mobile robotic arm planning for industrial ultrasonic nondestructive testing (NDT). The method, which uses a graph structure and offline IK validation, achieves 100% surface coverage in all tested cases with no collisions occurring during execution.

Key Takeaways:

  • The research proposes a 3D path planning method that combines NEAT and offline IK checking to address challenges in mobile robotic arm planning for industrial NDT.
  • The method uses a graph structure and offline IK validation to optimize coverage, path smoothness, and path length.
  • Simulation results on the ROS + Rviz platform across different surface geometries show that the proposed method achieves 100% surface coverage in all tested cases, with no collisions occurring during execution.
  • The method provides a practical solution for inspection scenarios where arm reachability and surface complexity present significant challenges.
  • The research team used the NEAT-based 3D path planning method to demonstrate its effectiveness in achieving complete surface coverage in various NDT scenarios.
  • The method's ability to optimize coverage, path smoothness, and path length makes it a valuable addition to the field of mobile robotic arm planning.
  • The research has the potential to improve the efficiency and accuracy of industrial NDT processes.
  • Zhang Mengyuan, Yang Qingping, Sutcliffe Mark, and Nicholson Ian are the authors of the study, which was published in the MATEC Web of Conferences.
  • The research was conducted at the Department of Mechanical and Aerospace Engineering, Brunel University London.

Statistics:

  • Surface coverage: 100%
  • Collision-free execution: 100%
  • Tested surface geometries: 5
  • ROS + Rviz platform used for simulation: yes
  • NEAT algorithm used for path optimization: yes
  • Offline IK checking used for validation: yes

Sources:

  • MATEC Web of Conferences, 2025, 413():05002. (MATEC Web of Conferences - http://www.matec-conferences.org)
  • EDP Sciences (Publisher)
  • Zhang Mengyuan et al. (2025). NEAT-based 3D path planning for mobile robotic arms in NDT with offline inverse kinematics validation. MATEC Web of Conferences, 413, 05002.

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