Next Article in Journal
The Relationships between Somatic Cells and Isoleucine, Leucine and Tyrosine Content in Cow Milk
Next Article in Special Issue
A Master-Slave Separate Parallel Intelligent Mobile Robot Used for Autonomous Pallet Transportation
Previous Article in Journal
Addressable Refraction and Curved Soliton Waveguides Using Electric Interfaces
Previous Article in Special Issue
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
Open AccessArticle

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

1
Department of Autonomous and Intelligent Systems, Fundación Tekniker, Iñaki Goenaga, 5-20600 Eibar, Spain
2
Faculty of Computer Science, P° Manuel Lardizabal, 1-20018 Donostia-San Sebastián, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(2), 348; https://doi.org/10.3390/app9020348
Received: 30 December 2018 / Revised: 16 January 2019 / Accepted: 17 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Advanced Mobile Robotics)
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. View Full-Text
Keywords: deep reinforcement learning; mobile manipulation; robot learning deep reinforcement learning; mobile manipulation; robot learning
Show Figures

Figure 1

MDPI and ACS Style

Iriondo, A.; Lazkano, E.; Susperregi, L.; Urain, J.; Fernandez, A.; Molina, J. Pick and Place Operations in Logistics Using a Mobile Manipulator Controlled with Deep Reinforcement Learning. Appl. Sci. 2019, 9, 348.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop