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Comparative Benchmark of Sampling-Based and DRL Motion Planning Methods for Industrial Robotic Arms.

Ignacio Fidalgo Astorquia¹, Guillermo Villate-Castillo², Alberto Tellaeche¹

¹Department of Computing, Electronics and Communication Technologies, University of Deusto, Avenida de las Universidades 24, 48007 Bilbao, Spain.

Sensors (Basel, Switzerland)

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Summary

A new deep reinforcement learning (DRL) motion planner for industrial robots significantly outperforms traditional sampling-based methods in speed and success rate. This advancement offers potential for real-time robotic control.

Keywords:

Open Motion Planning Library (OMPL)curriculum learning deep reinforcement learning (DRL)hybrid motion planning industrial robotics motion planning sampling-based planners

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Area of Science:

Robotics
Artificial Intelligence
Motion Planning

Background:

Industrial robotic arms require efficient and reliable motion planning.
Classical sampling-based planners (e.g., OMPL) face challenges in complex environments.
Learning-based approaches, particularly deep reinforcement learning (DRL), show promise for improving motion planning.

Purpose of the Study:

To compare the performance of OMPL planners against a DRL-based planner for industrial robotic arm motion planning.
To evaluate planning time, success rate, and trajectory smoothness.
To investigate the potential of DRL for real-time, high-throughput applications.

Main Methods:

A UR3e robot with an RG2 gripper was used.
Over 100,000 collision-free trajectories were generated using OMPL and MoveIt.
A DRL agent was trained using curriculum learning and expert demonstrations (Soft Actor-Critic).
Time-Optimal Parameterization using TOPPRA ensured dynamic feasibility.

Main Results:

The DRL planner achieved higher success rates and significantly lower planning times compared to OMPL.
DRL-generated trajectories were more compact and deterministic.
Classical planners showed better zero-shot adaptability and environmental generality.

Conclusions:

DRL-based motion planning offers significant advantages for industrial robotic arms, especially for real-time applications.
Hybrid architectures combining DRL and classical planners could leverage the strengths of both.
This study provides practical insights into planning paradigm trade-offs.