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Robotic Hardware and Software Integration for Changing Human Intentions

Robotic Hardware and Software Integration for Changing Human Intentions

Akif Durdu, Ismet Erkmen, Aydan M. Erkmen, Alper Yilmaz
Copyright: © 2012 |Pages: 27
ISBN13: 9781466601765|ISBN10: 1466601760|EISBN13: 9781466601772
DOI: 10.4018/978-1-4666-0176-5.ch013
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MLA

Durdu, Akif, et al. "Robotic Hardware and Software Integration for Changing Human Intentions." Prototyping of Robotic Systems: Applications of Design and Implementation, edited by Tarek Sobh and Xingguo Xiong, IGI Global, 2012, pp. 380-406. https://doi.org/10.4018/978-1-4666-0176-5.ch013

APA

Durdu, A., Erkmen, I., Erkmen, A. M., & Yilmaz, A. (2012). Robotic Hardware and Software Integration for Changing Human Intentions. In T. Sobh & X. Xiong (Eds.), Prototyping of Robotic Systems: Applications of Design and Implementation (pp. 380-406). IGI Global. https://doi.org/10.4018/978-1-4666-0176-5.ch013

Chicago

Durdu, Akif, et al. "Robotic Hardware and Software Integration for Changing Human Intentions." In Prototyping of Robotic Systems: Applications of Design and Implementation, edited by Tarek Sobh and Xingguo Xiong, 380-406. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0176-5.ch013

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Abstract

Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit spatial information by learning a Hidden Markov Model (HMM) of motion, which is tailored for intelligent robotic interaction. The algorithmic design consists of two phases. At first, the approach detects and tracks human to estimate the current intention. Later, this information is used by autonomous robots that interact with detected human to change the estimated intention. In the tracking and intention estimation phase, postures and locations of the human are monitored by applying low-level video processing methods. In the latter phase, learned HMM models are used to reshape the estimated human intention. This two-phase system is tested on video frames taken from a real human-robot environment. The results obtained using the proposed approach shows promising performance in reshaping of detected intentions.

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