Pick and Place Operations in Logistics Using a Mobile Manipulator Controlled with Deep Reinforcement Learning

Clicks: 185
ID: 96717
2019
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Abstract
Programming robots to perform complex tasks is a very expensive job. Traditional path planning and control are able to generate point to point collision free trajectories, but when the tasks to be performed are complex, traditional planning and control become complex tasks. This study focused on robotic operations in logistics, specifically, on picking objects in unstructured areas using a mobile manipulator configuration. The mobile manipulator has to be able to place its base in a correct place so the arm is able to plan a trajectory up to an object in a table. A deep reinforcement learning (DRL) approach was selected to solve this type of complex control tasks. Using the arm planner’s feedback, a controller for the robot base is learned, which guides the platform to such a place where the arm is able to plan a trajectory up to the object. In addition the performance of two DRL algorithms ((Deep Deterministic Policy Gradient (DDPG)) and (Proximal Policy Optimisation (PPO)) is compared within the context of a concrete robotic task.
Reference Key
iriondo2019pickapplied Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Iriondo, Ander;Lazkano, Elena;Susperregi, Loreto;Urain, Julen;Fernandez, Ane;Molina, Jorge;
Journal applied sciences
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/2076-3417/9/2/348
Keywords
chemistry Biology (General) Engineering (General). Civil engineering (General) Technology Mechanical engineering and machinery physics

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