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In industrial environments, robot navigation requires collision-free path planning, especially in the presence of dynamic obstacles such as moving machines, humans, and other autonomous systems. Traditional algorithms like A* and Analytic Hierarchy Process (AHP) are widely used, to solve these problems but they have their limitations. A* primarily focuses on static, distance-optimized path planning but is unable to adapt to dynamic changes. On the contrary, AHP alone is unable handle complex path planning scenarios. In this work, we propose a hybrid algorithm that integrates AHP with Proximal Policy Optimization (PPO), to overcome these limitations. PPO is used for global path planning. By using optimal navigation policies through interaction with the environment, PPO refines the way-points generated by AHP based on distance, angle of movement, and collision safety. The outcome of the hybrid algorithm is an efficient and adaptive set of global way-points which forms the final path. The performance of the proposed AHP-PPO algorithm is compared with the existing methods, such as A* with AHP (AAHP) and Artificial Potential Field (APF), using matrices like path length, angular variation, and obstacle avoidance. AHP-PPO is observed to outperform the existing approaches while demonstrating enhanced adaptability, smoother trajectories, and greater safety.

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Robotic Path Planning Using PPO Based Reinforcement Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Arpan Garai,
  • Anindita Kundu,
  • Moudipa Jana

摘要

In industrial environments, robot navigation requires collision-free path planning, especially in the presence of dynamic obstacles such as moving machines, humans, and other autonomous systems. Traditional algorithms like A* and Analytic Hierarchy Process (AHP) are widely used, to solve these problems but they have their limitations. A* primarily focuses on static, distance-optimized path planning but is unable to adapt to dynamic changes. On the contrary, AHP alone is unable handle complex path planning scenarios. In this work, we propose a hybrid algorithm that integrates AHP with Proximal Policy Optimization (PPO), to overcome these limitations. PPO is used for global path planning. By using optimal navigation policies through interaction with the environment, PPO refines the way-points generated by AHP based on distance, angle of movement, and collision safety. The outcome of the hybrid algorithm is an efficient and adaptive set of global way-points which forms the final path. The performance of the proposed AHP-PPO algorithm is compared with the existing methods, such as A* with AHP (AAHP) and Artificial Potential Field (APF), using matrices like path length, angular variation, and obstacle avoidance. AHP-PPO is observed to outperform the existing approaches while demonstrating enhanced adaptability, smoother trajectories, and greater safety.