Adaptive and intuitive interactions with socially-competent pedagogical assistant robots

2015 ◽  
Author(s):  
Maya Dimitrova ◽  
Hiroaki Wagatsuma ◽  
Gyanendra Nath Tripathi ◽  
Guangyi Ai
Keyword(s):  
Assistant Robots

Safety verification method for preventing friction blisters during utilization of physical assistant robots

2017 ◽  
Vol 31 (13) ◽  
pp. 680-694 ◽  
Author(s):  
Xuewei Mao ◽  
Yoji Yamada ◽  
Yasuhiro Akiyama ◽  
Shogo Okamoto ◽  
Kengo Yoshida
Keyword(s):  
Safety Verification ◽  
Verification Method ◽  
Assistant Robots

scholarly journals Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019 ◽  
Vol 9 (15) ◽  
pp. 3196 ◽  
Author(s):  
Lidia María Belmonte ◽  
Rafael Morales ◽  
Antonio Fernández-Caballero
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Systematic Mapping Study ◽  
General View ◽  
Mapping Study ◽  
Vision Systems ◽  
Systematic Mapping ◽  
Aerial Vehicles ◽  
Assistant Robots

Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

A Human–Robot Cooperative Learning System for Easy Installation of Assistant Robots in New Working Environments

2004 ◽  
Vol 40 (3) ◽  
pp. 233-265 ◽  
Author(s):  
María Elena López ◽  
Rafael Barea ◽  
Luis Miguel Bergasa ◽  
María Soledad Escudero
Keyword(s):  
Cooperative Learning ◽  
Learning System ◽  
Working Environments ◽  
Assistant Robots

A Navigation System for Assistant Robots Using Visually Augmented POMDPs

2005 ◽  
Vol 19 (1) ◽  
pp. 67-87 ◽  
Author(s):  
María Elena López ◽  
Luis Miguel Bergasa ◽  
Rafael Barea ◽  
María Soledad Escudero
Keyword(s):  
Navigation System ◽  
Assistant Robots

scholarly journals A Multi-Agent Control Architecture for a Robotic Wheelchair

2006 ◽  
Vol 3 (3) ◽  
pp. 179-189 ◽  
Author(s):  
C. Galindo ◽  
A. Cruz-Martin ◽  
J. L. Blanco ◽  
J. A. Fernńndez-Madrigal ◽  
J. Gonzalez
Keyword(s):  
Reinforcement Learning ◽  
Control Architecture ◽  
Daily Lives ◽  
Multi Agent ◽  
Human Skills ◽  
Overall Performance ◽  
Agent Control ◽  
Robotic Wheelchairs ◽  
Assistant Robots ◽  
Robotic Wheelchair

Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair) to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

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