Javascript must be enabled for the correct page display

Reinforcement Learning For Offshore Crane Set-down Operations

Ding, Mingcheng (2018) Reinforcement Learning For Offshore Crane Set-down Operations. Master's Thesis / Essay, Artificial Intelligence.


Download (7MB) | Preview
[img] Text
Restricted to Registered users only

Download (119kB)


Offshore activities are usually carried out in one of the worst working environments where vessels and objects are affected by weather all the time. One of the most common offshore operations is to set down a heavy object onto the deck of a floating vessel. A good set-down always requires a small impact force as well as a short distance to the target position. It can be quite challenging to achieve due to various reasons, such as ship motions, crane mechanics, and so forth. It takes years to train crane operators to make as many correct decisions as possible. Any small mistake might cause severe consequences. In this project, we investigated the feasibility of solving this practical offshore set-down problem using Reinforcement Learning (RL) techniques. As a feasibility study, we started from the simplest possible environment where only the heave motion and impact velocity are considered. Then, we gradually upgraded the simulation environment by adding more environmental and physical features with respect to a practical situation. The results in different environments bring us an overview of the possibilities and limitations of standard RL algorithms. We demonstrated that the methods suffer from the general challenges of RL, such as sparse rewards and sample efficiency in solving the long-term objective set-down problem. We tried various methods to work around this issue, such as transfer learning, hierarchical RL, and using simulation-based methods.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor name: Wiering, M.A.
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 11 Dec 2018
Last Modified: 18 Dec 2018 15:21

Actions (login required)

View Item View Item