TY - GEN
T1 - Object trajectory information for robotic service in intelligent space
AU - Skulkittiyut, Weerachai
AU - Yamaguchi, Toru
AU - Mizukawa, Makoto
PY - 2014/2/20
Y1 - 2014/2/20
N2 - Implementation of robotic service is needed extremely enormous of information including environment, place, user and object knowledge. It is extremely difficult for a robot to do tasks ordered by a human without having some basic knowledge or information. This paper proposes a method to create object knowledge focusing on an object and object's place relationship for the robotic service namely tidy-up service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. In details, as the first phase, we conduct a questionnaire to collect the trajectories in term of places of individual object from the participants. Based on the collected object trajectory information, we are able to build Markov chain model of the object which the states are possible places and transition probabilities are the probability that the object moved from one place to other places. In final, we are able to use the transition probability including the Markov chain model to predict and provide the next appropriate place. The result showed that the proposed approach is efficient in creating object trajectory as knowledge, hence, helping the robots to and to provide intuitive service.
AB - Implementation of robotic service is needed extremely enormous of information including environment, place, user and object knowledge. It is extremely difficult for a robot to do tasks ordered by a human without having some basic knowledge or information. This paper proposes a method to create object knowledge focusing on an object and object's place relationship for the robotic service namely tidy-up service, in which a robot is asked to take objects such as books, cups, dishes on a table to appropriate places automatically. In details, as the first phase, we conduct a questionnaire to collect the trajectories in term of places of individual object from the participants. Based on the collected object trajectory information, we are able to build Markov chain model of the object which the states are possible places and transition probabilities are the probability that the object moved from one place to other places. In final, we are able to use the transition probability including the Markov chain model to predict and provide the next appropriate place. The result showed that the proposed approach is efficient in creating object trajectory as knowledge, hence, helping the robots to and to provide intuitive service.
KW - Intelligent space
KW - Markov chain model
KW - Object trajectory information
KW - Robotic service
UR - http://www.scopus.com/inward/record.url?scp=84893917736&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893917736&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.490-491.1172
DO - 10.4028/www.scientific.net/AMM.490-491.1172
M3 - Conference contribution
AN - SCOPUS:84893917736
SN - 9783038350019
T3 - Applied Mechanics and Materials
SP - 1172
EP - 1176
BT - Mechanical Design and Power Engineering
T2 - 2013 2nd International Conference on Mechanical Design and Power Engineering, ICMDPE 2013
Y2 - 29 November 2013 through 30 November 2013
ER -