Omnidirectional Mobile Robot Path Finding Using Deep Deterministic Policy Gradient for Real Robot Control
Yuto Ushida, Hafiyanda Razan, Takuto Sakuma, Shōhei Kato · 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) · 2021
Recently, workers are in short supply in the distribution industry. Therefore, the objective of this study is to develop an autonomous mobile robot that can search for a path to the goal while avoiding static and dynamic obstacles to support workers in a warehouse. In this study, we apply five learning types in a hybrid static and dynamic environment in a simulation environment as a preparation for learning in an actual machine and verify the effectiveness of these learning methods.