A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

KALMAN FILTER AND NARX NEURAL NETWORK FOR ROBOT VISION BASED HUMAN TRACKING

Tracking human is an important and challenging problem in video-based intelligent robot systems. In this paper, a vision-based human tracking system is supposed to provide sensor input for vision-based control of a mobile robot that works in a team helping the human co-worker. A comparison between NARX neural network and Kalman filter in solving the prediction problem of human tracking in robot vision is presented. After collecting video data from a robot, simulation results obtained from the Kalman filter model are used to compare with the simulation results obtained from the NARX Neural network.Key words: robot vision, Kalman filter, neural networks, human trackin

Assistive multimodal robotic system (AMRSys): security and privacy issues, challenges, and possible solutions

Assistive robotic systems could be a suitable solution to support a variety of health and care services, help independent living, and even simulate affection, to reduce loneliness. However, adoption is limited by several issues, as well as user concerns about ethics, data security, and privacy. Other than the common threats related to internet connectivity, personal robotic systems have advanced interaction possibilities, such as audio, video, touch, and gestures, which could be exploited to gain access to private data that are stored in the robot. Therefore, novel, safer methods of interaction should be designed to safeguard users’ privacy. To solicit further research on secure and private multimodal interaction, this article presents a thorough study of the state-of-the-art literature on data security and user privacy in interactive social robotic systems for health and care. In our study, we focus on social robotics to assist older people, which is a global challenge that is receiving a great deal of attention from the robotics and social care communities. This application will have a significant positive impact on the economy and society, but poses various security and privacy issues. This article analyses the key vulnerable areas where data leakage could occur during a multimodal interaction with a personal assistive robotic system. Thus, blockchain with a resource-aware framework, along with a continuous multifactor authentication mechanism, are envisaged as a potential solution for making such systems secure by design; therefore, increasing trust, acceptability, and adoption. Among the key cybersecurity research challenges, it is crucial to create an intelligent mechanism that autonomously determines the right trade-off between continuous user prompts and system usability, according to data types and personal preferences

A bank of unscented kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics. © Springer Science & Business Media BV 2010

Analysis and modeling of airport surface operations

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-99).The focus of research in air traffic control has traditionally been on the airborne flight phase. Recently, increasing the efficiency of surface operations has been recognized to have significant potential benefits in terms of fuel and emissions savings. To identify opportunities for improvement and to quantify the consequent gains in efficiency, it is necessary to characterize current operational practices. This thesis describes a framework for analysis of airport surface operations and proposes metrics to quantify operational performance. These metrics are then evaluated for Boston Logan International Airport using actual surface surveillance data. A probabilistic model for real-time prediction of aircraft taxi-out times is described, which improves upon the accuracy of previous models based on queuing theory and regression. Finally, a regression model for estimation of aircraft taxi-out fuel burn is described. Together, the modules described here form the basis for a surface operations optimization tool that is currently under development.by Harshad Khadilkar.S.M

飛行ロボットによる人物捜索に関する研究

Tohoku University田所諭課