Analysis of methods for playing human robot hide-and-seek in a simple real world urban environment

The hide-and-seek game has many interesting aspects for studying cognitive functions in robots and the interactions between mobile robots and humans. Some MOMDP (Mixed Observable Markovian Decision Processes) models and a heuristic-based method are proposed and evaluated as an automated seeker. MOMDPs are used because the hider's position is not always known (partially observable), and the seeker's position is fully observable. The MOMDP model is used in an o-line method for which two reward functions are tried. Because the time complexity of this model grows exponentially with the number of (partially observable) states, an on-line hierarchical MOMDP model was proposed to handle bigger maps. To reduce the states in the on-line method a robot centered segmentation is used. In addition to extensive simulations, games with a human hider and a real mobile robot as a seeker have been done in a simple urban environment.Peer ReviewedPostprint (author’s final draft

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

Trabajo presentado al 14th IEEE-RAS International Conference on Humanoid Robots: Humanoids 2014 "Humans and Robots Face-to-Face", celebrado en Madrid (España) del 18 al 20 de noviembre de 2014.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.This work has been partially funded by the DPI2013-42458-P.Peer Reviewe

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

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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.Peer ReviewedPostprint (author's final draft

Searching and tracking people with cooperative mobile robots

The final publication is available at link.springer.comSocial robots should be able to search and track people in order to help them. In this paper we present two different techniques for coordinated multi-robot teams for searching and tracking people. A probability map (belief) of a target person location is maintained, and to initialize and update it, two methods were implemented and tested: one based on a reinforcement learning algorithm and the other based on a particle filter. The person is tracked if visible, otherwise an exploration is done by making a balance, for each candidate location, between the belief, the distance, and whether close locations are explored by other robots of the team. The validation of the approach was accomplished throughout an extensive set of simulations using up to five agents and a large amount of dynamic obstacles; furthermore, over three hours of real-life experiments with two robots searching and tracking were recorded and analysed.Peer ReviewedPostprint (author's final draft

Comparison of MOMDP and heuristic methods to play hide-and-seek

Trabajo presentado al 16th International Conference of the Catalan Association for Artificial Intelligence en Vic del 23 al 25 de octubre de 2013.The hide-and-seek game is considered an excellent domain for studying the interactions between mobile robots and humans. Prior to the implementation and test in our mobile robots TIBI and DABO, we have been devising different models and strategies to play this game and comparing them extensively in simulations. Recently, we have proposed the use of MOMDP (Mixed Observability Markov Decision Processes) models to learn a good policy to be applied by the seeker. Even though MOMDPs reduce the computational cost of POMDPs (Partially Observable MDPs), they still have a high computational complexity which is exponential with the number of states. For the hide-and-seek game, the number of states is directly related to the number of grid cells, and for two players (the hider and the seeker), it is the square of the number of cells. As an alternative to off-line MOMDP policy computation with the complete grid fine resolution, we have devised a two-level MOMDP, where the policy is computed on-line at the top level with a reduced number of states independent of the grid size. In this paper, we introduce a new fast heuristic method for the seeker and compare its performance to both off-line and on-line MOMDP approaches. We show simulation results in maps of different sizes against two types of automated hiders.Work supported by the Spanish Ministry of Science and Innovation under project Rob- TaskCoop (DPI2010-17112).Peer Reviewe

Using the Average Landmark Vector Method for Robot Homing.

The original publication ia available at http://www.booksonline.iospress.nl/Content/View.aspx?piid=7638Several methods can be used for a robot to return to a previously visited position. In our approach we use the average landmark vector method to calculate a homing vector which should point the robot to the destination. This approach was tested in a simulated environment, where panoramic projections of features were used. To evaluate the robustness of the method, several parameters of the simulation were changed such as the length of the walls and the number of features, and also several disturbance factors were added to the simulation such as noise and occlusion. The simulated robot performed really well. Randomly removing 50% of the features resulted in a mean of 85% successful runs. Even adding more than 100% fake features did not have any significant result on the performance.This work has been partially supported by the FI grant from the Generalitat de Catalunya and the European Social Fund, the MID-CBR project grant TIN2006-15140- C03-01 and FEDER funds and the Marco Polo Fund of the University of Groningen.Peer reviewe

Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas

Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. © 2011 Springer Science+Business Media B.V.This work was partially supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, the MID-CBR project grant TIN2006-15140- C03-01 and FEDER funds, the grant 2005-SGR-00093, the MIPRCV Consolider Imagennio 2010 and the Marco Polo fund from the University of Groningen.Peer Reviewe

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

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.This work has been partially funded by the EU project AEROARMS European projectH2020-ICT-2014-1-644271 and the CICYT project DPI2013-42458-P.Peer Reviewe