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This paper presents an exploration of the integration of four prominent pathfinding algorithms: Ant Colony Optimization (ACO), Potential Fields, Genetic Algorithm (GA), and Rapidly-Exploring Random Trees Connect (RRT-Connect) within the FreeCAD environment. Each algorithm is implemented independently to evaluate its effectiveness in path planning for robotic arms and assembly applications. The study aims to provide insights into the performance of these algorithms in terms of trajectory optimization, computational efficiency, and obstacle avoidance. The paper also presents a comparative analysis of the four algorithms to evaluate their performance in terms of computational time, path length, and effectiveness in a 3D static environment. The framework, implemented in FreeCAD, serves as a tool for simulating and comparing different path planning strategies in industrial scenarios.

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Path Planning in 3D Static Environments Using ACO, GA, Potential Fields and RRT-Connect Integrated in FreeCAD

  • 会议论文

  • 发表日期：

    2025年

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  • Ahmed Nhouchi,
  • Salma Ben Said,
  • Mohamed Amine Ben Abdallah,
  • Nizar Aifaoui

摘要

This paper presents an exploration of the integration of four prominent pathfinding algorithms: Ant Colony Optimization (ACO), Potential Fields, Genetic Algorithm (GA), and Rapidly-Exploring Random Trees Connect (RRT-Connect) within the FreeCAD environment. Each algorithm is implemented independently to evaluate its effectiveness in path planning for robotic arms and assembly applications. The study aims to provide insights into the performance of these algorithms in terms of trajectory optimization, computational efficiency, and obstacle avoidance. The paper also presents a comparative analysis of the four algorithms to evaluate their performance in terms of computational time, path length, and effectiveness in a 3D static environment. The framework, implemented in FreeCAD, serves as a tool for simulating and comparing different path planning strategies in industrial scenarios.