Lifelong Learning for Vision based AUV Control

基于视觉的 AUV 控制的终身学习

• 批准号: 2270314
• 金额: --
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2270314
• 关键词: Lifelong; Learning; Vision; based; AUV

Principal goal for project: The main objective is to design a low-level controller for autonomous underwater vehicles (AUVs) using learning frameworks. The algorithm should be portable across multiple robots, The controller should adapt quickly to any change in the dynamics (increase of payload, current disturbances, damage to the robot, thruster efficiency decay, etc...)Research project: Control of AUVs is a difficult task mainly due to the non-linearity of the dynamics in the underwater domain. Classic control approaches, such as PIDs or sliding mode controller, usually operate at a low speed and require a lot of expert knowledge to tune the different parameters. The parameters usually work for one specific configuration of a robot and tuning is necessary every time the robot configuration changes.To overcome those limitations, this research will look at learning-based methods and will try to combine them with classic control methods. The learning-based methods allow using data collected during robot operation to improve the controller. Combining those methods with classic control approaches allow us to speed up the learning process as most of the recent learning methods require a lot of data and learning time.

项目主要目标：主要目标是使用学习框架为自主水下航行器（auv）设计一个低级控制器。该算法应在多个机器人之间可移植，控制器应快速适应任何动态变化（有效载荷增加，电流干扰，机器人损坏，推进器效率衰减等）。研究项目：水下机器人的控制是一项艰巨的任务，主要是由于水下区域动力学的非线性。传统的控制方法，如pid或滑模控制器，通常在低速下运行，需要大量的专家知识来调整不同的参数。这些参数通常适用于机器人的一个特定配置，每次机器人配置发生变化时都需要进行调优。为了克服这些限制，本研究将着眼于基于学习的方法，并将尝试将它们与经典控制方法相结合。基于学习的方法允许使用机器人操作过程中收集的数据来改进控制器。将这些方法与经典控制方法相结合，可以加快学习过程，因为大多数最新的学习方法需要大量的数据和学习时间。