COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an underactuated Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters

Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric

Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.Comment: Presented at IV 202

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

On Course Towards Model-Free Guidance: A Self-Learning Approach To Dynamic Collision Avoidance for Autonomous Surface Vehicles

I denne masteroppgåva vart det demonstrert at djup forsterkande læring (engelsk: Deep Reinforcement Learning / DRL) kan nyttast for å trena eit reaktivt, autonomt fartøy utstyrt med påmonterte avstandssensorar til å navigera ukjent farvatn, kva omfattar ikkje berre ei utfordring om å unngå stranding medan ein går framover i samsvar med den ønskte ruta, men også dynamisk kollisjonsunngåelse, altså styringsstrategiar som minimerer risikoen for sammentreff i situasjonar der fartøyet er på kollisjonskurs med andre imøtekommande eller kryssande skip. For dette formålet vart læringsalgoritmen PPO (engelsk Proximal Policy Optimication / PPO) nytta, som er sett på som ein leiande DRL-metode for anvendelser innan reguleringsteknikk av kontinuerleg natur. Den lærande agenten, som gjennom treningsprosessen vart rettleidd av ein belønningsfunksjon konstuert for å, på numerisk vis, gjenspegla preferansane våre for styringsåtferda til fartøyet, vart så evaluert basert på prestasjonen sin i eit virtuelt simuleringsmiljø som vart rekonstruert frå terreng- og maritime trafikkdata frå Trondheimsfjorden

Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way. The AI agent, which is equipped with multiple rangefinder sensors for obstacle detection, is trained and evaluated in a challenging, stochastically generated simulation environment based on the OpenAI gym Python toolkit. Notably, the agent is provided with real-time insight into its own reward function, allowing it to dynamically adapt its guidance strategy. Depending on its strategy, which ranges from radical path-adherence to radical obstacle avoidance, the trained agent achieves an episodic success rate close to 100%

Wasatch Academy.

Two white residential-looking buildings, one of which has a car parked next to it. There are trees around both of the buildings. Snow capped mountains can be seen in the distance.Sepia. Numbers and titles for the Russell H. Goodman slide collection were taken from an accompanying list. This photograph is listed as number 26. The list also includes the following paragraph: HISTORIC PROTESTANT CHURCHES OF THE WEST Photographs taken by Mr. and Mrs. Russell H. Goodman, in whose [names] these duplicate slides are presented to the Archives of Westminster College by Willard R. Harwood and Emil Nyman. <br

Palmyrena : city,hinterland and caravan trade between orient and occident: proceedings of the conference held in Athens, December 1-3, 2012 /

Includes bibliographical references

Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning

Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector. Since the electrification of transport is happening simultaneously, autonomous marine vessels can reduce environmental impact, lower costs, and increase efficiency. Although close monitoring is still required to ensure safety, the ultimate goal is full autonomy. One major milestone is to develop a control system that is versatile enough to handle any weather and encounter that is also robust and reliable. Additionally, the control system must adhere to the International Regulations for Preventing Collisions at Sea (COLREGs) for successful interaction with human sailors. Since the COLREGs were written for the human mind to interpret, they are written in ambiguous prose and therefore not machine-readable or verifiable. Due to these challenges and the wide variety of situations to be tackled, classical model-based approaches prove complicated to implement and computationally heavy. Within machine learning (ML), deep reinforcement learning (DRL) has shown great potential for a wide range of applications. The model-free and self-learning properties of DRL make it a promising candidate for autonomous vessels. In this work, a subset of the COLREGs is incorporated into a DRL-based path following and obstacle avoidance system using collision risk theory. The resulting autonomous agent dynamically interpolates between path following and COLREG-compliant collision avoidance in the training scenario, isolated encounter situations, and AIS-based simulations of real-world scenarios. (C) 2022 The Author(s). Published by Elsevier Ltd