Push recovery and active balancing for inexpensive humanoid robots
| dc.contributor.author | Hosseinmemar, Amirhossein | |
| dc.contributor.examiningcommittee | McLeod, Bob (Electrical & Computer Engineering) Wang, Yang (Computer Science) Wong, Ching Chang (Electrical Engineering Tamkang University) | en_US |
| dc.contributor.supervisor | Anderson, John (Computer Science) Baltes, Jacky (Computer Science) | en_US |
| dc.date.accessioned | 2019-09-06T16:19:43Z | |
| dc.date.available | 2019-09-06T16:19:43Z | |
| dc.date.issued | 2019-08-28 | en_US |
| dc.date.submitted | 2019-08-29T04:21:03Z | en |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Active balancing of a humanoid robot is a challenging task due to the complexity of combining a walking gait, dynamic balancing, vision and high-level behaviors. My Ph.D research focuses on the active balancing and push recovery problems that allow inexpensive humanoid robots to balance while standing and walking, and to compensate for external forces. In this research, I have proposed a push recovery mechanism that employs two machine learning techniques, Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) to learn recovery step trajectories during push recovery using a closed-loop feedback control. I have implemented a 3D model using the Robot Operating System (ROS) and Gazebo. To reduce wear and tear on the real robot, I used this model for learning the recovery steps for different impact strengths and directions. I evaluated my approach in both in the real world and in simulation. All the real world experiments are performed by Polaris, a teen- sized humanoid robot in the Autonomous Agent Laboratory (AALab), University of Manitoba. The design, implementation, and evaluation of hardware, software, and kinematic models are discussed in this document. | en_US |
| dc.description.note | October 2019 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34176 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Humanoid robot, Push recovery, Active balancing, Deep reinforcement learning | en_US |
| dc.title | Push recovery and active balancing for inexpensive humanoid robots | en_US |
| dc.type | doctoral thesis | en_US |