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

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.