Multi-Layer Environmental Affordance Map for Robust Indoor Localization, Event Detection and Social Friendly Navigation

Wu, Ping-Tsang; Yu, Chee-An; Chan, Shao-Hung; Chiang, Meng‐Tsan; Fu, Li-Chen

doi:10.1109/iros40897.2019.8968455

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…The occupancy map [13] and the topological map [18], [19] describe the connections between sources and destinations using a graph as possible movement actions in the environment based on the robot's traversability. In the multi-layer environmental affordance map, the connections between movement actions considering human activities are reflected in the occupancy map based on the traversability and the concept of the space inferred from the object set [20].…”

Section: B Environmental Representation Methodsmentioning

confidence: 99%

ASTRON: Action-Based Spatio-Temporal Robot Navigation

2021

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To achieve the tasks provided by a user, it is necessary for robots to have a plan that fully exploits their functionalities in an environment. The objective of this study is to realize robot task planning in real space for effectively use of the robot's functions The plan is formed by deriving a feasible action sequence by interpreting the instructions within the scope of the action possibilities of the robots and the changes in them. In this paper, we first propose an action graph as a novel environmental representation approach to facilitate the understanding of the robot's action possibility in real space. In the action graph, the action possibility is represented by nodes, which describe the spatial position to perform each feasible action, and edges, which describe the feasible actions, based on the subsystem-level affordance and the arrangement of objects in the environment. We also propose an action-based spatio-temporal robot navigation (ASTRON), which focuses on robot navigation tasks. ASTRON enables the robots to determine a feasible action sequence that utilizes their functions by interpreting the instructions based on the action graph. The effectiveness of the proposed method was evaluated through simulations and actual machine experiments in a coffee shop environment. In the actual machine experiments, the proposed method was applied to robots with different subsystem configurations. The experimental results demonstrated that the proposed method could plan the feasible action sequence to complete the tasks by considering the environmental state and the subsystem configurations of the robot.

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Section: B Environmental Representation Methodsmentioning

confidence: 99%

ASTRON: Action-Based Spatio-Temporal Robot Navigation

2021

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“…La extracción de modelos 3D para mapear permite, por ejemplo, crear mapas de alto nivel que incluyen información semántica (Wu et al, 2019). Algunos trabajos como (Zhang et al, 2021) determinan la ubicación de los objetos utilizando el centro de la bounding box para obtener la profundidad.…”

Section: Estado Del Arteunclassified

Extracción de modelos 3D basado en CNN y nubes de puntos para mapeado

Mendez,

Mora,

Barber

2023

XLIV Jornadas De Automática: Libro De Actas: Universidad De Zaragoza, Escuela De Ingeniería Y Arquitectura, 6, 7 Y 8 De Septiem

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La obtención de modelos 3D precisos es importante en robótica. Una amplia gama de aplicaciones como mapear objetos requieren de un conocimiento preciso de su forma y disposición. Sin embargo, puede ser difícil lograr una alta precisión, especialmente cuando se trata de datos reales que incluyen factores como oclusiones, desorden y ruido, influyendo en el resultado final. En el estado del arte, se combina deep learning en imágenes 2D con segmentación de nubes de puntos. Sin embargo, los estudios comparativos al respecto son muy limitados. En este trabajo, implementamos y comparamos varios métodos existentes, aplicando mejoras para que estos funcionen correctamente en entornos complejos. Analizamos cuatro formas de obtener modelos 3D, tres de ellos basados en detección por bounding box y un cuarto basado en la obtención de una máscara mediante instance segmentation. Se realiza una comparación tanto cualitativa como cuantitativa, presentando las fortalezas y los límites de cada método, además de un ejemplo práctico de uso.

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“…In APF, this situation is generated when the force gradient that guides the robot results in local minima that do not correspond to the desired destination [18], [19]. Search and sampling-based approaches have proved to be effective in SRN, however they have only been tested in small, static, and relatively empty indoor environments [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Online Social Robot Navigation in Indoor, Large and Crowded Environments

Silva,

Verdezoto,

Paillacho

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against stateof-the-art approaches in challenging simulated scenarios, and we also demonstrate its feasibility with the Pepper robot in real-world trials.

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Multi-Layer Environmental Affordance Map for Robust Indoor Localization, Event Detection and Social Friendly Navigation

Cited by 7 publications

References 15 publications

ASTRON: Action-Based Spatio-Temporal Robot Navigation

ASTRON: Action-Based Spatio-Temporal Robot Navigation

Extracción de modelos 3D basado en CNN y nubes de puntos para mapeado

Online Social Robot Navigation in Indoor, Large and Crowded Environments

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