Work

Panda Manipulator Control with Obstacle Avoidance Through Reinforcement Learning in a Simulated Environment

Year: 2025

Type: article

Abstract: Robotic systems with learning capabilities have many powerful applications in unstructured environments. Through reinforcement learning, robots can quickly adapt to new situations and learn from direct environmental interaction. This work proposes a simulation environment based on Robotics Toolbox for Python to solve a classic problem of the inverse kinematics of manipulators, ensuring that the robot reaches the desired position without colliding with the obstacles present in the scene. The potential of this reinforcement learning method is illustrated through simulation using the Franka-Emika Panda manipulator trained by the Deep Deterministic Policy Gradient algorithm.

Source: Journal of Bioengineering, Technologies and Health

DOI: 10.34178/jbth.v8i1.464

Authors: Marcelo Albergaria Paulino Fernandes Ferreira, Taniel Silva Franklin, Oberdan Rocha Pinheiro

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: SENAI CIMATEC, Agência Nacional do Petróleo, Gás Natural e Biocombustíveis, Shell Brasil, Empresa Brasileira de Pesquisa e Inovação Industrial

OpenAlex ID: W4409201032

FWCI:

Cites: 2

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Related to: 10