1,582 research outputs found
Structured output prediction for semantic perception in autonomous vehicles
A key challenge in the realization of autonomous vehicles is the machine's ability to perceive its surrounding environment. This task is tackled through a model that partitions vehicle camera input into distinct semantic classes, by taking into account visual contextual cues. The use of structured machine learning models is investigated, which not only allow for complex input, but also arbitrarily structured output. Towards this goal, an outdoor road scene dataset is constructed with accompanying fine-grained image labelings. For coherent segmentation, a structured predictor is modeled to encode label distributions conditioned on the input images. After optimizing this model through max-margin learning, based on an ontological loss function, efficient classification is realized via graph cuts inference using alpha-expansion. Both quantitative and qualitative analyses demonstrate that by taking into account contextual relations between pixel segmentation regions within a second-degree neighborhood, spurious label assignments are filtered out, leading to highly accurate semantic segmentations for outdoor scenes
Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles
Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE
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
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to coordinate multiple agents as a team to defeat their enemies. To
maintain a scalable yet effective communication protocol, we introduce a
Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
vectorised extension of actor-critic formulation. We show that BiCNet can
handle different types of combats with arbitrary numbers of AI agents for both
sides. Our analysis demonstrates that without any supervisions such as human
demonstrations or labelled data, BiCNet could learn various types of advanced
coordination strategies that have been commonly used by experienced game
players. In our experiments, we evaluate our approach against multiple
baselines under different scenarios; it shows state-of-the-art performance, and
possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent
Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat
Games", Mar 201
A cooperative active perception approach for swarm robotics
More than half a century after modern robotics first emerged, we still face
a landscape in which most of the work done by robots is predetermined, rather
than autonomous. A strong understanding of the environment is one of the key
factors for autonomy, enabling the robots to make correct decisions based on the
environment surrounding them.
Classic methods for obtaining robotic controllers are based on manual specification, but become less trivial as the complexity scales. Artificial intelligence
methods like evolutionary algorithms were introduced to synthesize robotic controllers by optimizing an artificial neural network to a given fitness function that
measures the robots’ performance to solve a predetermined task.
In this work, a novel approach to swarm robotics environment perception is
studied, with a behavior model based on the cooperative identification of objects
that fly around an environment, followed by an action based on the result of the
identification process. Controllers are obtained via evolutionary methods. Results
show a controller with a high identification and correct decision rates.
The work is followed by a study on scaling up that approach to multiple environments. Experiments are done on terrain, marine and aerial environments,
as well as on ideal, noisy and hybrid scenarios. In the hybrid scenario, different
evolution samples are done in different environments. Results show the way these
controllers are able to adapt to each scenario and conclude a hybrid evolution is
the best fit to generate a more robust and environment independent controller to
solve our task.Mais de um século após a robótica moderna ter surgido, ainda nos deparamos
com um cenário onde a maioria do trabalho executado por robôs é pré-determinado,
ao invés de autónomo. Uma forte compreensão do ambiente é um dos pontos chave
para a autonomia, permitindo aos robôs tomarem decisões corretas baseadas no
ambiente que os rodeia.
Abordagens mais clássicas para obter controladores de robótica são baseadas na
especificação manual, mas tornam-se menos apropriadas à medida que a complexidade aumenta. Métodos de inteligência artificial como algoritmos evolucionários
foram introduzidos para obter controladores de robótica através da otimização de
uma rede neuronal artificial para uma função de fitness que mede a aptidão dos
robôs para resolver uma determinada tarefa.
Neste trabalho, é apresentada uma nova abordagem para perceção do ambiente
por um enxame de robôs, com um modelo de comportamento baseado na identificação cooperativa de objetos que circulam no ambiente, seguida de uma atuação
baseada no resultado da identificação. Os controladores são obtidos através de
métodos evolucionários. Os resultados apesentam um controlador com uma alta
taxa de identificação e de decisão.
Segue-se um estudo sobre o escalonamento da abordagem a múltiplos ambientes. São feitas experiencias num ambiente terrestre, marinho e aéreo, bem
como num contexto ideal, ruidoso e híbrido. No contexto híbrido, diferentes samples da evolução ocorrem em diferentes ambientes. Os resultados demonstram a
forma como cada controlador se adapta aos restantes ambientes e concluem que a
evolução híbrida foi a mais capaz de gerar um controlador robusto e transversal
aos diferentes ambientes.
Palavras-chave: Robótica evolucionária, Sistemas multi-robô, Cooperação,
Perceção, Identificação de objetos, Inteligência artificial, Aprendizagem automática,
Redes neuronais, Múltiplos ambientes
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