Comparative benchmark of sampling-based and DRL motion planning methods for industrial robotic arms
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2025-08-25
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Multidisciplinary Digital Publishing Institute (MDPI)
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This study presents a comprehensive comparison between classical sampling-based motion planners from the Open Motion Planning Library (OMPL) and a learning-based planner based on Soft Actor–Critic (SAC) for motion planning in industrial robotic arms. Using a UR3e robot equipped with an RG2 gripper, we constructed a large-scale dataset of over 100,000 collision-free trajectories generated with MoveIt-integrated OMPL planners. These trajectories were used to train a DRL agent via curriculum learning and expert demonstrations. Both approaches were evaluated on key metrics such as planning time, success rate, and trajectory smoothness. Results show that the DRL-based planner achieves higher success rates and significantly lower planning times, producing more compact and deterministic trajectories. Time-optimal parameterization using TOPPRA ensured the dynamic feasibility of all trajectories. While classical planners retain advantages in zero-shot adaptability and environmental generality, our findings highlight the potential of DRL for real-time and high-throughput motion planning in industrial contexts. This work provides practical insights into the trade-offs between traditional and learning-based planning paradigms, paving the way for hybrid architectures that combine their strengths.
Palabras clave
Curriculum learning
Deep reinforcement learning (DRL)
Hybrid motion planning
Industrial robotics
Motion planning
Open Motion Planning Library (OMPL)
Sampling-based planners
Deep reinforcement learning (DRL)
Hybrid motion planning
Industrial robotics
Motion planning
Open Motion Planning Library (OMPL)
Sampling-based planners
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Astorquia, I. F., Villate-Castillo, G., Tellaeche, A., & Vazquez, J.-I. (2025). Comparative benchmark of sampling-based and DRL motion planning methods for industrial robotic arms. Sensors, 25(17). https://doi.org/10.3390/S25175282