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Robotics industry

Novel Hybrid Algorithm for Robotic Arm Obstacle Avoidance Planning

Strategic Insights

— 1 min read

Research findings on robotics have revealed a new algorithm designed to optimize motion planning for multi-degree-of-freedom robotic arms operating in narrow spaces. The novel algorithm, named Whale Hybrid Improved Dung Beetle Optimizer (WHIDBO), combines the Whale Optimization Algorithm (WOA) and the improved Dung Beetle Optimization algorithm (DBO) to achieve efficient positioning of the end-effector and safety of all links. Financial support for this research came from various provincial and central government-backed initiatives in China. According to the researchers, WHIDBO demonstrates superior convergence speed and stability compared to seven benchmark algorithms, and practical validation using a kinematic-constrained fitness function with cubic B-spline smoothing shows WHIDBO generates collision-free paths 21.15% shorter than DBO, while improving computational efficiency by 23.33%.

Key Takeaways:

  • WHIDBO is a novel hybrid algorithm that combines Whale Optimization Algorithm (WOA) and improved Dung Beetle Optimization algorithm (DBO) to optimize motion planning for robotic arms.
  • WHIDBO demonstrates superior convergence speed and stability compared to seven benchmark algorithms on CEC2022 functions.
  • Practical validation shows WHIDBO generates collision-free paths 21.15% shorter than DBO, while improving computational efficiency by 23.33%.
  • The crisscross mechanism in WHIDBO enhances the balance between exploitation and exploration phases during optimization.
  • WHIDBO was tested on a kinematic-constrained fitness function with cubic B-spline smoothing.
  • The research highlights the potential of WHIDBO for industrial automation systems requiring reliable motion planning in geometrically complex environments.
  • The project received financial support from the Major Cultivation Project of Gansu Province University Research and Innovation Platform, Excellent Doctoral Foundation of Gansu Province, and Central Government-guided Special Project for Local Science and Technology Development of Sichuan Province.
  • Researchers from Lanzhou University of Technology, including Wuyin Jin, Dong Chen, Zhiyuan Rui, Lan Luo, Jiazhen Li, and Wentao Wang, contributed to the development of WHIDBO.

Statistics:

  • 21.15% shorter collision-free paths compared to DBO.
  • 23.33% improvement in computational efficiency.
  • WHIDBO demonstrates superior convergence speed and stability compared to seven benchmark algorithms.
  • WHIDBO was tested on 45 CEC2022 functions.

Sources:

  • A Novel Hybrid Improved Dung Beetle Optimization Algorithm for Robotic Arm Obstacle Avoidance Planning. Cluster Computing, 2025;28(13).
  • Journal of Engineering. October 20, 2025; p 1829.
  • NewsRx. New Findings from Lanzhou University of Technology Update Understanding of Robotics.

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