Open AccessDecision-Theoretical Navigation of Service Robots Using POMDPs with Human-Robot Co-Occurrence Prediction
Author(s) -
Kun Qian,
Xudong Ma,
Xianzhong Duan,
Fang Fang,
Bo Zhou
Publication year - 2013
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/55926
Subject(s) - computer science , partially observable markov decision process , robot , reliability (semiconductor) , motion planning , probabilistic logic , artificial intelligence , term (time) , model predictive control , motion (physics) , service (business) , control (management) , machine learning , markov chain , markov model , power (physics) , physics , economy , quantum mechanics , economics
To improve the natural human-avoidance skills of service robots, a human motion predictive navigation method is proposed, namely PN-POMDP. A human-robot motion co-occurrence estimation algorithm is proposed which incorporates long-term and short-term human motion prediction. To improve the reliability of probabilistic and predictive navigation, the POMDP model is utilized to generate navigation control policies through theoretically optimal decisions. A layered motion control structure is proposed that combines global path planning and reactive avoidance. Multiple comity policies are integrated with a decision-making module that generates efficient and human-compliant navigational behaviours for robots. Experimental results illustrate the effectiveness and reliability of the predictive navigation method