Abstract
This paper investigates the use of an autonomous service robot in an indoor complex environment, such as a hospital ward or a retirement home. This type of service robot not only needs to plan and find paths around obstacles, but must also interact with caregivers or patients. This study presents a type of service robot that combines the image from a 3D depth camera with infrared sensors, and the inputs from multiple sonar sensors in an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based approach in path planning. In personal contacts, facial features are used to perform person recognition in order to discriminate between staff, patients, or a stranger. In the case of staff, the service robot can perform a follow-me function if requested. The robot can also use an additional feature which is to classify the person’s gender. The purpose of facial and gender recognition includes helping to present choices for suitable destinations to the user. Experiments were done in cramped but open spaces, as well as confined passages scenarios, and in almost all cases, the autonomous robots were able to reach their destinations.
Highlights
Today’s workers in care-taking facilities have their hands full taking care of patients, require help for everyday routines
There are aa total of possible scenarios designed along the path that we think that the robot would need to make critical decisionsabout aboutwhether whether to to incorporate incorporate sonar sensors ininitsitsdecisions
We presented a service robot with face recognition and gender classification abilities, with accuracy reaching 92.68%
Summary
Today’s workers in care-taking facilities have their hands full taking care of patients, require help for everyday routines. We choose to investigation helpful tasks, such as delivering proper medicines to target patients, or helping to carry heavy loads in follow-me mode, performed by an autonomous robot with face and gender recognition abilities. For these purposes, this paper presents a tri-wheeled autonomous service robot equipped with a camera, an RGB-Depth (RGB is acronym for Red, Green, and Blue) sensor, and sonar sensors. Csaba the basis to determine the path to avoid the obstacles indoors; the second part uses an artificial neural [6] introduced an improved version based on fuzzy rules His system uses 16 rules, three inputs and network to identify different configurations of the environment.
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