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Sebastian Thrun - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic approach to collaborative multi-robot localization
    Autonomous Robots, 2000
    Co-Authors: Dieter Fox, Hannes Kruppa, Wolfram Burgard, Sebastian Thrun
    Abstract:

    This paper presents a statistical algorithm for collaborative mobile robot localization. Ourapproach uses a sample-based version of Markov localization, capable of localizing mobile Robots in an any-time fashion. When teams of Robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot’s belief whenever one robot detects another. As a result, the Robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile Robots equipped with cameras and laser range-finders for detecting other Robots. The results, obtained with the real Robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous Robots collaborate during localization.

  • Collaborative multi-robot localization
    Annual German Conference on Artificial Intelligence, 1999
    Co-Authors: Dieter Fox, Hannes Kruppa, Wolfram Burgard, Sebastian Thrun
    Abstract:

    This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile Robots in an any-time fashion. When teams of Robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the Robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile Robots. The Robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

Dieter Fox - One of the best experts on this subject based on the ideXlab platform.

  • A practical, decision-theoretic approach to multi-robot mapping and exploration
    2004
    Co-Authors: J. Ko, B. Stewart, Kurt Konolige, D. Fox, Dieter Fox, Benson Limketkai
    Abstract:

    An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that Robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual Robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robot's relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robot's partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two Robots successfully merging their maps using the decision-theoretic rendezvous strategy.

  • Probabilistic approach to collaborative multi-robot localization
    Autonomous Robots, 2000
    Co-Authors: Dieter Fox, Hannes Kruppa, Wolfram Burgard, Sebastian Thrun
    Abstract:

    This paper presents a statistical algorithm for collaborative mobile robot localization. Ourapproach uses a sample-based version of Markov localization, capable of localizing mobile Robots in an any-time fashion. When teams of Robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot’s belief whenever one robot detects another. As a result, the Robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile Robots equipped with cameras and laser range-finders for detecting other Robots. The results, obtained with the real Robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous Robots collaborate during localization.

  • Collaborative multi-robot localization
    Annual German Conference on Artificial Intelligence, 1999
    Co-Authors: Dieter Fox, Hannes Kruppa, Wolfram Burgard, Sebastian Thrun
    Abstract:

    This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile Robots in an any-time fashion. When teams of Robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the Robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile Robots. The Robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

Bilge Mutlu - One of the best experts on this subject based on the ideXlab platform.

  • Look Like Me: Matching Robot Personality via Gaze to Increase Motivation
    ACM Conference on Human Factors in Computing Systems (CHI), 2015
    Co-Authors: Sean Andrist, Bilge Mutlu
    Abstract:

    Socially assistive Robots are envisioned to provide social and cognitive assistance where they will seek to motivate and engage people in therapeutic activities. Due to their physicality, Robots serve as a powerful technology for motivating people. Prior work has shown that effective motivation requires adaption to user needs and characteristics, but how Robots might successfully achieve such adaptation is still unknown. In this paper, we present work on matching a robot's personality-expressed via its gaze behavior-to that of its users. We confirmed in an online study with 22 participants that the robot's gaze behavior can successfully express either an extroverted or introverted personality. In a laboratory study with 40 participants, we demonstrate the positive effect of personality matching on a user's motivation to engage in a repetitive task. These results have important implications for the design of adaptive robot behaviors in assistive human-robot interaction.

  • Robots in organizations: {The} role of workflow, social, and environmental factors in human-robot interaction
    2008 3rd ACM IEEE International Conference on Human - Robot Interaction ( HRI ), 2008
    Co-Authors: Bilge Mutlu, Jodi Forlizzi
    Abstract:

    Robots are becoming increasingly integrated into the workplace, impacting organizational structures and processes, and affecting products and services created by these organizations. While Robots promise significant benefits to organizations, their introduction poses a variety of design challenges. In this paper, we use ethnographic data collected at a hospital using an autonomous delivery robot to examine how organizational factors affect the way its members respond to Robots and the changes engendered by their use. Our analysis uncovered dramatic differences between the medical and post-partum units in how people integrated the robot into their workflow and their perceptions of and interactions with it. Different patient profiles in these units led to differences in workflow, goals, social dynamics, and the use of the physical environment. In medical units, low tolerance for interruptions, a discrepancy between the perceived cost and benefits of using the robot, and breakdowns due to high traffic and clutter in the robot's path caused the robot to have a negative impact on the workflow and staff resistance. On the contrary, post-partum units integrated the robot into their workflow and social context. Based on our findings, we provide design guidelines for the development of Robots for organizations.

Chang Yan - One of the best experts on this subject based on the ideXlab platform.

  • Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human–Robot Interaction
    Journal of Communication, 2006
    Co-Authors: Kwan Min Lee, Seung A. Jin, Wei Peng, Chang Yan
    Abstract:

    Personality is an essential feature for creating socially interactive Robots. Studies on this dimension will facilitate enhanced human–robot interaction (HRI). Using AIBO, a social robotic pet developed by Sony, we examined the issue of personality in HRI. In this gender-balanced 2 (AIBO personality: introvert vs. extrovert) by 2 (participant personality: introvert vs. extrovert) between-subject experiment (N = 48), we found that participants could accurately recognize a robot’s personality based on its verbal and nonverbal behaviors. In addition, various complementarity attraction effects were found in HRI. Participants enjoyed interacting with a robot more when the robot’s personality was complementary to their own personalities than when the robot’s personality was similar to their own personalities. The same complementarity attraction effect was found in participants’ evaluation of the robot’s intelligence and social attraction. Participants’ feelings of social presence during the interaction were a significant mediator for the complementarity attraction effects observed. Practical and theoretical implications of the current study for the design of social Robots and the study of HRI were discussed.

  • Can Robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction
    Journal of Communication, 2006
    Co-Authors: Kwan Min Lee, Seung A. Jin, Wei Peng, Chang Yan
    Abstract:

    Personality is an essential feature for creating socially interactive Robots. Studies on this dimension will facilitate enhanced human–robot interaction (HRI). Using AIBO, a social robotic pet developed by Sony, we examined the issue of personality in HRI. In this gender-balanced 2 (AIBO personality: introvert vs. extrovert) by 2 (participant per- sonality: introvert vs. extrovert) between-subject experiment (N = 48), we found that participants could accurately recognize a robot’s personality based on its verbal and nonverbal behaviors. In addition, various complementarity attraction effects were found in HRI. Participants enjoyed interacting with a robot more when the robot’s personality was complementary to their own personalities than when the robot’s personality was similar to their own personalities. The same complementarity attraction effect was found in participants’ evaluation of the robot’s intelligence and social attraction. Participants’ feelings of social presence during the interaction were a significant media- tor for the complementarity attraction effects observed. Practical and theoretical implications of the current study for the design of social Robots and the study of HRI were discussed.

Choulsoo Jang - One of the best experts on this subject based on the ideXlab platform.

  • Cooperative localization for multi-robot incorporating proprioceptive/ exteroceptive position sensors
    Springer Tracts in Advanced Robotics, 2008
    Co-Authors: Jihong Lee, Kyounghwan Jo, Choulsoo Jang
    Abstract:

    This paper presents a new method of cooperative localization for mul- tiple Robots utilizing correlation between GPS errors of common mode in shared workspace. Assuming that GPS data of individual robot are correlated strongly as the distance between Robots are close, we utilize the differential position data be- tween the Robots to refine robot’s position data. Under artificial environment for simulation with imposed model error to robot motion and GPS sensor data error, it is confirmed that the proposed method provides improved localization accuracy [9]. In addition, we present a practical solution to accumulated position error in traveling long distance.

  • Cooperative Localization for Multi-robot Incorporating Proprioceptive/Exteroceptive Position Sensors
    Springer Tracts in Advanced Robotics, 2008
    Co-Authors: Jihong Lee, Kyounghwan Jo, Choulsoo Jang
    Abstract:

    This paper presents a new method of cooperative localization for mul- tiple Robots utilizing correlation between GPS errors of common mode in shared workspace. Assuming that GPS data of individual robot are correlated strongly as the distance between Robots are close, we utilize the differential position data be- tween the Robots to refine robot’s position data. Under artificial environment for simulation with imposed model error to robot motion and GPS sensor data error, it is confirmed that the proposed method provides improved localization accuracy [9]. In addition, we present a practical solution to accumulated position error in traveling long distance.