Paper Reading AI Learner

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

2022-09-07 06:33:57
Z. Jiang, G. Song

Abstract

With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.

Abstract (translated)

URL

https://arxiv.org/abs/2209.02954

PDF

https://arxiv.org/pdf/2209.02954.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot