Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition
Universidade Federal de Santa Maria - UFSM（圣玛丽亚联邦大学）
Ricardo Bedin Grando, Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Nicolas Pieper Bortoluzzi, Pedro Miranda Pinheiro, Armando Alves Net… 展开作者
Ricardo Bedin Grando, Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Nicolas Pieper Bortoluzzi, Pedro Miranda Pinheiro, Armando Alves Neto, Paulo Lilles Jorge Drews-Jr
Since the application of Deep Q-Learning to the continuous action domain in Atari-like games, Deep Reinforcement Learning (Deep-RL) techniques for motion control have been qualitatively enhanced. Nowadays, modern Deep-RL can be successfully applied to solve a wide range of complex decision-making tasks for many types of vehicles. Based on this context, in this paper, we propose the use of Deep-RL to perform autonomous mapless navigation for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs), robots that can operate in both, air or water media. We developed two approaches, one deterministic and the other stochastic. Our system uses the relative localization of the vehicle and simple sparse range data to train the network. We compared our approaches with a traditional geometric tracking controller for mapless navigation. Based on experimental results, we can conclude that Deep-RL-based approaches can be successfully used to perform mapless navigation and obstacle avoidance for HUAUVs. Our vehicle accomplished the navigation in two scenarios, being capable to achieve the desired target through both environments, and even outperforming the geometric-based tracking controller on the obstacle-avoidance capability.
Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization
Universidad Tecnológica del Uruguay (UTEC)（乌拉圭技术大学）；Centro de Ciências Computacionais (C3) of Universidade Federal do Rio Grande (FURG)（里奥格兰德联邦大学计算科学中心）；Universidade Federal de Santa Maria (UFSM)（圣玛丽亚联邦大学）
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles
Universidade Federal do Rio Grande - FURG（里奥格兰德联邦大学（FURG））；Universidade Federal de Santa Maria - UFSM（圣玛丽亚联邦大学（UFSM））；Technological University of Uruguay（乌拉圭技术大学）
SurfAAV: Design and Implementation of a Novel Multimodal Surfing Aquatic-Aerial Vehicle
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China（智能科学与技术学院，国防科技大学，长沙，中国）；School of Electronic Information, Central South University, Changsha, China（电子信息学院，中南大学，长沙，中国）
Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles
Federal University of Rio Grande（里奥格兰德联邦大学）；University of Tsukuba（筑波大学）；VersusAI；Technological University of Uruguay（乌拉圭理工大学）；Federal University of Santa Maria（圣玛丽亚联邦大学）
ZS-Puffin: Design, Modeling and Implementation of an Unmanned Aerial-Aquatic Vehicle with Amphibious Wings
School of Ocean Engineering and Technology, Sun Yat‐sen University（海洋工程与技术学院，中山大学）；Southern Marine Science and Engineering Guangdong Laboratory（广东海洋工程科学实验室）；Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat‐sen University)（极地环境综合观测重点实验室（中山大学））；Guangdong Provincial Key Laboratory of Information Technology for Deep Water Acoustics（广东省深水声学信息技术重点实验室）
Underwater Motions Analysis and Control of a Coupling-Tiltable Unmanned Aerial-Aquatic Vehicle
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳人工智能与机器人研究院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
SHRUMS: Sensor Hallucination for Real-time Underwater Motion Planning with a Compact 3D Sonar
Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）；Aquaculture Robotics and Automation, SINTEF Ocean（水产养殖机器人与自动化，SINTEF海洋）
Underwater Dense Mapping with the First Compact 3D Sonar
Mechanical Engineering Department, University of Delaware（德克萨斯大学机械工程系）；Dartmouth College（达特茅斯学院）；Aquaculture Robotics and Automation Group, SINTEF Ocean（SINTEF海洋研究所水产机器人与自动化小组）
Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots
Technological University of Uruguay（乌拉圭技术大学）；Federal University of Rio Grande - FURG（里奥格兰德联邦大学（FURG））；Universidade Federal de Santa Maria - UFSM（圣玛丽亚联邦大学（UFSM））
TJ-FlyingFish: Design and Implementation of an Aerial-Aquatic Quadrotor with Tiltable Propulsion Units
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；The Chinese University of Hong Kong（香港中文大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）；Zhejiang University（浙江大学）
