Human intention estimation algorithm design for robot in human and robot cooperated cell assembly

2010 ◽  
Author(s):  
Fei Chen ◽  
Baiqing Sun ◽  
Jian Huang ◽  
Hironobu Sasaki ◽  
Toshio Fukuda
Keyword(s):  
Algorithm Design ◽  
Estimation Algorithm ◽  
Cell Assembly ◽  
Human Intention ◽  
Intention Estimation

Robotic Hardware and Software Integration for Changing Human Intentions

2012 ◽  
pp. 380-406 ◽  
Author(s):  
Akif Durdu ◽  
Ismet Erkmen ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Video Processing ◽  
Spatial Information ◽  
Human Robot Interaction ◽  
Phase System ◽  
Robot Interaction ◽  
Two Phase ◽  
Video Frames ◽  
Human Intention ◽  
Two Phases ◽  
Intention Estimation

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.

Intention and Body-Mood Engineering via Proactive Robot Moves in HRI

2015 ◽  
pp. 255-282 ◽  
Author(s):  
O. Can Görür ◽  
Aydan M. Erkmen
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Human Body ◽  
Mood Induction ◽  
Positive Mood ◽  
Focused Attention ◽  
Elastic Networks ◽  
Human Intention ◽  
Novel Concept ◽  
Intention Estimation

This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots. Current human intention has to be deviated towards the new desired one by destabilizing the obstinance of human intention, inducing positive mood and making the “robot gain curiosity of human”. Deviations are generated as sequences of transient intentions tracing intention trajectories. The authors use elastic networks to generate, in two modes of body mood: “confident” and “suspicious”, transient intentions directed towards the desired one, choosing among intentional robot moves previously learned by HMM.

scholarly journals Robotic Hardware and Software Integration for Changing Human Intentions

Robotics ◽  
2013 ◽  
pp. 1381-1406
Author(s):  
Akif Durdu ◽  
Ismet Erkmen ◽  
Aydan M. Erkmen ◽  
Alper Yilmaz
Keyword(s):  
Video Processing ◽  
Spatial Information ◽  
Human Robot Interaction ◽  
Phase System ◽  
Robot Interaction ◽  
Two Phase ◽  
Video Frames ◽  
Human Intention ◽  
Two Phases ◽  
Intention Estimation

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.

scholarly journals The Improved Division-Less MT-Type Velocity Estimation Algorithm for Low-Cost FPGAs

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 361 ◽  
Author(s):  
Aleš Hace
Keyword(s):  
System Performance ◽  
Low Cost ◽  
Algorithm Design ◽  
Estimation Algorithm ◽  
Velocity Estimation ◽  
The Novel ◽  
Single Chip ◽  
High Bandwidth ◽  
Practical Performance ◽  
Incremental Encoder

Advanced motion control applications require smooth and highly accurate high-bandwidth velocity feedback, which is usually provided by an incremental encoder. Furthermore, high sampling rates are also demanded in order to achieve cutting-edge system performance. Such control system performance with high accuracy can be achieved easily by FPGA-based controllers. On the other hand, the well-known MT method for velocity estimation has been well proven in practice. However, its complexity, which is related to the inherent arithmetic division involved in the calculus part of the method, prevents its holistic implementation as a single-chip solution on small-size low-cost FPGAs that are suitable for practical optimized control systems. In order to overcome this obstacle, we proposed a division-less MT-type algorithm that consumes only minimal FPGA resources, which makes it proper for modern cost-optimized FPGAs. In this paper, we present new results. The recursive discrete algorithm has been further optimized, in order to improve the accuracy of the velocity estimation. The novel algorithm has also been implemented on the experimental FPGA board, and validated by practical experiments. The enhanced algorithm design resulted in improved practical performance.

Intention and Body-Mood Engineering via Proactive Robot Moves in HRI

2019 ◽  
pp. 247-275
Author(s):  
O. Can Görür ◽  
Aydan M. Erkmen
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Human Body ◽  
Mood Induction ◽  
Positive Mood ◽  
Focused Attention ◽  
Elastic Networks ◽  
Human Intention ◽  
Novel Concept ◽  
Intention Estimation

This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots. Current human intention has to be deviated towards the new desired one by destabilizing the obstinance of human intention, inducing positive mood and making the “robot gain curiosity of human”. Deviations are generated as sequences of transient intentions tracing intention trajectories. The authors use elastic networks to generate, in two modes of body mood: “confident” and “suspicious”, transient intentions directed towards the desired one, choosing among intentional robot moves previously learned by HMM.

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