636 research outputs found
深層強化学習を用いた動的環境下における事前知識不要なロボットナビゲーションに関する研究
Tohoku University博士(工学)thesi
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
We present SocialGym 2, a multi-agent navigation simulator for social robot
research. Our simulator models multiple autonomous agents, replicating
real-world dynamics in complex environments, including doorways, hallways,
intersections, and roundabouts. Unlike traditional simulators that concentrate
on single robots with basic kinematic constraints in open spaces, SocialGym 2
employs multi-agent reinforcement learning (MARL) to develop optimal navigation
policies for multiple robots with diverse, dynamic constraints in complex
environments. Built on the PettingZoo MARL library and Stable Baselines3 API,
SocialGym 2 offers an accessible python interface that integrates with a
navigation stack through ROS messaging. SocialGym 2 can be easily installed and
is packaged in a docker container, and it provides the capability to swap and
evaluate different MARL algorithms, as well as customize observation and reward
functions. We also provide scripts to allow users to create their own
environments and have conducted benchmarks using various social navigation
algorithms, reporting a broad range of social navigation metrics. Projected
hosted at: https://amrl.cs.utexas.edu/social_gym/index.htmlComment: Submitted to RSS 202
Towards the simulation of cooperative perception applications by leveraging distributed sensing infrastructures
With the rapid development of Automated Vehicles (AV), the boundaries of their function alities are being pushed and new challenges are being imposed. In increasingly complex
and dynamic environments, it is fundamental to rely on more powerful onboard sensors and
usually AI. However, there are limitations to this approach. As AVs are increasingly being
integrated in several industries, expectations regarding their cooperation ability is growing,
and vehicle-centric approaches to sensing and reasoning, become hard to integrate. The
proposed approach is to extend perception to the environment, i.e. outside of the vehicle,
by making it smarter, via the deployment of wireless sensors and actuators. This will vastly
improve the perception capabilities in dynamic and unpredictable scenarios and often in a
cheaper way, relying mostly in the use of lower cost sensors and embedded devices, which rely
on their scale deployment instead of centralized sensing abilities. Consequently, to support
the development and deployment of such cooperation actions in a seamless way, we require
the usage of co-simulation frameworks, that can encompass multiple perspectives of control
and communications for the AVs, the wireless sensors and actuators and other actors in the
environment. In this work, we rely on ROS2 and micro-ROS as the underlying technologies
for integrating several simulation tools, to construct a framework, capable of supporting the
development, test and validation of such smart, cooperative environments. This endeavor
was undertaken by building upon an existing simulation framework known as AuNa. We
extended its capabilities to facilitate the simulation of cooperative scenarios by incorporat ing external sensors placed within the environment rather than just relying on vehicle-based
sensors. Moreover, we devised a cooperative perception approach within this framework,
showcasing its substantial potential and effectiveness. This will enable the demonstration of
multiple cooperation scenarios and also ease the deployment phase by relying on the same
software architecture.Com o rápido desenvolvimento dos Veículos Autónomos (AV), os limites das suas funcional idades estão a ser alcançados e novos desafios estão a surgir. Em ambientes complexos
e dinâmicos, é fundamental a utilização de sensores de alta capacidade e, na maioria dos
casos, inteligência artificial. Mas existem limitações nesta abordagem. Como os AVs estão
a ser integrados em várias indústrias, as expectativas quanto à sua capacidade de cooperação estão a aumentar, e as abordagens de perceção e raciocínio centradas no veículo,
tornam-se difíceis de integrar. A abordagem proposta consiste em extender a perceção para
o ambiente, isto é, fora do veículo, tornando-a inteligente, através do uso de sensores e
atuadores wireless. Isto irá melhorar as capacidades de perceção em cenários dinâmicos e
imprevisíveis, reduzindo o custo, pois a abordagem será baseada no uso de sensores low-cost
e sistemas embebidos, que dependem da sua implementação em grande escala em vez da
capacidade de perceção centralizada. Consequentemente, para apoiar o desenvolvimento
e implementação destas ações em cooperação, é necessária a utilização de frameworks de
co-simulação, que abranjam múltiplas perspetivas de controlo e comunicação para os AVs,
sensores e atuadores wireless, e outros atores no ambiente. Neste trabalho será utilizado
ROS2 e micro-ROS como as tecnologias subjacentes para a integração das ferramentas de
simulação, de modo a construir uma framework capaz de apoiar o desenvolvimento, teste e
validação de ambientes inteligentes e cooperativos. Esta tarefa foi realizada com base numa
framework de simulação denominada AuNa. Foram expandidas as suas capacidades para
facilitar a simulação de cenários cooperativos através da incorporação de sensores externos
colocados no ambiente, em vez de depender apenas de sensores montados nos veículos.
Além disso, concebemos uma abordagem de perceção cooperativa usando a framework,
demonstrando o seu potencial e eficácia. Isto irá permitir a demonstração de múltiplos
cenários de cooperação e também facilitar a fase de implementação, utilizando a mesma
arquitetura de software
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated
environments, commonly referred to as social robot navigation. While the field
of social navigation has advanced tremendously in recent years, the fair
evaluation of algorithms that tackle social navigation remains hard because it
involves not just robotic agents moving in static environments but also dynamic
human agents and their perceptions of the appropriateness of robot behavior. In
contrast, clear, repeatable, and accessible benchmarks have accelerated
progress in fields like computer vision, natural language processing and
traditional robot navigation by enabling researchers to fairly compare
algorithms, revealing limitations of existing solutions and illuminating
promising new directions. We believe the same approach can benefit social
navigation. In this paper, we pave the road towards common, widely accessible,
and repeatable benchmarking criteria to evaluate social robot navigation. Our
contributions include (a) a definition of a socially navigating robot as one
that respects the principles of safety, comfort, legibility, politeness, social
competency, agent understanding, proactivity, and responsiveness to context,
(b) guidelines for the use of metrics, development of scenarios, benchmarks,
datasets, and simulators to evaluate social navigation, and (c) a design of a
social navigation metrics framework to make it easier to compare results from
different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table
SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
This work proposes a new framework for a socially-aware dynamic local planner
in crowded environments by building on the recently proposed Trajectory-ranked
Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the
social navigation problem, our multi-modal learning planner explicitly
considers social interaction factors, as well as social-awareness factors into
T-MEDIRL pipeline to learn a reward function from human demonstrations.
Moreover, we propose a novel trajectory ranking score using the sudden velocity
change of pedestrians around the robot to address the sub-optimality in human
demonstrations. Our evaluation shows that this method can successfully make a
robot navigate in a crowded social environment and outperforms the state-of-art
social navigation methods in terms of the success rate, navigation time, and
invasion rate
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