Analysis of Methods for Playing Human Robot Hide-and-Seek in a Simple Real World Urban Environment

Goldhoorn, Alex; Sanfeliu, Alberto; Alquézar, René

doi:10.1007/978-3-319-03653-3_37

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…En [10] y [9] se enfocó al juego del escondite, porque es más limitado y da facilidad en la investigación del problema de la búsqueda de per- sonas. Primero se empezó a trabajar en espacios discretos [9] usando MOMDPs (Mixed Observable Markovian Decision Processes, [15]) para buscar personas. En [8] se extendió el POMCP [17] para poder usarlo en tiempo real y en espacio continuo (CR-POMCP, [8]).…”

Section: Trabajos Anterioresunclassified

Un nuevo método cooperativo para encontrar personas en un entorno urbano con robots móviles

Goldhoorn¹,

Zulueta²,

Mancho³

et al. 2022

Actas De Las XXXVII Jornadas De Automática 7, 8 Y 9 De Septiembre De 2016, Madrid

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Se presenta un nuevo método para localizar a personas en entornos urbanos usando robots móviles sociales que trabajan de manera cooperativa, el cual supera las limitaciones de enfoques ya existentes, que se adaptan a entornos específicos, o se basan en comportamientos humanos poco re- alistas. Con este método cooperativo los robots pueden encontrar a personas fuera del campo de rango de sensores u ocultados por obstáculos dinámicos o estáticos. Nuestro enfoque incluye la búsqueda de personas, seguimiento, cooperación multi-robot y comunicación. En particular se define un “Cooperative Highest-Belief Continuous Real-time POMCP” que puede ejecutarse en tiempo real y en entornos continuos y grandes. En este método se usan algoritmos de búsqueda on-line Partially Observable Monte-Carlo Planning (POMCP), los cuales, al contrario de trabajos anteriores son capaces de planificar con incertidumbre y con grandes espacios de estados. La estrategia de búsqueda hace un balanceo entre la probabilidad de que la persona esté en una posición concreta, la distancia de las posiciones, y si la posición está cerca de una meta ya asignada a otro robot. Se ha validado el método con extensivo número de simulaciones y experimentos reales con una persona y dos robots.

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Section: Trabajos Anterioresunclassified

Un nuevo método cooperativo para encontrar personas en un entorno urbano con robots móviles

Goldhoorn¹,

Zulueta²,

Mancho³

et al. 2022

Actas De Las XXXVII Jornadas De Automática 7, 8 Y 9 De Septiembre De 2016, Madrid

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“…The created ACT-R module learned how to play hide-and-seek generating new rules. In [10], a human and robot were following an other person cooperatively. Both methods focused on cognitive methods, which require a high amount of symbolic knowledge of the world.…”

Section: Related Workmentioning

confidence: 99%

Searching and tracking people in urban environments with static and dynamic obstacles

Goldhoorn

Garrell

Alquézar

et al. 2017

Robotics and Autonomous Systems

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Searching and tracking people in crowded urban areas where they can be occluded by static or dynamic obstacles is an important behavior for social robots which assist humans in urban outdoor environments. In this work, we propose a method that can handle in real-time searching and tracking people using a Highest Belief Particle Filter Searcher and Tracker. It makes use of a modified Particle Filter (PF), which, in contrast to other methods, can do both searching and tracking of a person under uncertainty, with false negative detections, lack of a person detection, in continuous space and real-time. Moreover, this method uses dynamic obstacles to improve the predicted possible location of the person. Comparisons have been made with our previous method, the Adaptive Highest Belief Continuous Real-time POMCP Follower, in different conditions and with dynamic obstacles. Real-life experiments have been done during two weeks with a mobile service robot in two urban environments of Barcelona with other people walking around.

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“…First, we started to work in discrete time and space [14], and we used MOMDPs (Mixed Observable Markovian Decision Processes, [15]) to search for a person.…”

Section: Related Workmentioning

confidence: 99%

“…Other works [15], [22], [17], [18] showed simulations of hide-and-seek like problems using POMDPs, but in small discrete environments, and only few real-life experiments [18], [14] were done.…”

Section: B Continuous Real-time Pomcpmentioning

confidence: 99%

Continuous real time POMCP to find-and-follow people by a humanoid service robot

Goldhoorn

Garrell

Alquézar

et al. 2014

2014 IEEE-RAS International Conference on Humanoid Robots

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Abstract-This study describes and evaluates two new methods for finding and following people in urban settings using a humanoid service robot: the Continuous Real-time POMCP method, and its improved extension called Adaptive Highest Belief Continuous Real-time POMCP follower. They are able to run in real-time, in large continuous environments. These methods make use of the online search algorithm Partially Observable Monte-Carlo Planning (POMCP), which in contrast to other previous approaches, can plan under uncertainty on large state spaces. We compare our new methods with a heuristic person follower and demonstrate that they obtain better results by testing them extensively in both simulated and real-life experiments. More than two hours, over 3 km, of autonomous navigation during real-life experiments have been done with a mobile humanoid robot in urban environments.

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Analysis of Methods for Playing Human Robot Hide-and-Seek in a Simple Real World Urban Environment

Cited by 4 publications

References 12 publications

Un nuevo método cooperativo para encontrar personas en un entorno urbano con robots móviles

Un nuevo método cooperativo para encontrar personas en un entorno urbano con robots móviles

Searching and tracking people in urban environments with static and dynamic obstacles

Continuous real time POMCP to find-and-follow people by a humanoid service robot

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