A PID Based Meta-Learning Method About Space Non-Cooperative Active Object Tracking

Abstract

Listen

Translate article

In recent years, more and more scholars pay attention to the problem of active object tracking, which has a wide range of application scenarios. Active tracking of non-cooperative objects in space was a good application example, which has important implications for the exploration of the space environment. Datasets of the space objects are difficult to collect, thus traditional machine learning is difficult to complete space tasks, which needs train from scratch, sufficient datasets and lots of training time. Thus, we propose a PID based for Meta-Learning method for space non-cooperative active object tracking, which overcome the shortcoming of few space datasets, and save a lot of training time. We incorporate PID into the meta-learning algorithm, past and change information of gradients to update the meta-learning parameters, and reducing greatly the overshoot problem of meta-learning and accelerating the optimization process of meta-learning. Our algorithm can help train a better initialized active object tracking model due to the differentiation and integration of gradients, which can retain more information and improve stability. Our PID based meta-learning method was based on reinforcement learning, which can help achieve automated and optimal object tracking. The initial model of our method can track a wide variety of objects without train from scratch. Through a large number of comparative experiments, our method has good results in tracking speed and tracking accuracy with longer episodes and higher rewards.