Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces

A risk-aware architecture for resilient spacecraft operations

In this paper we discuss a resilient, risk-aware software architecture for onboard, real-time autonomous operations that is intended to robustly handle uncertainty in space-craft behavior within hazardous and unconstrained environments, without unnecessarily increasing complexity. This architecture, the Resilient Spacecraft Executive (RSE), serves three main functions: (1) adapting to component failures to allow graceful degradation, (2) accommodating environments, science observations, and spacecraft capabilities that are not fully known in advance, and (3) making risk-aware decisions without waiting for slow ground-based reactions. This RSE is made up of four main parts: deliberative, habitual, and reflexive layers, and a state estimator that interfaces with all three. We use a risk-aware goal-directed executive within the deliberative layer to perform risk-informed planning, to satisfy the mission goals (specified by mission control) within the specified priorities and constraints. Other state-of-the-art algorithms to be integrated into the RSE include correct-by-construction control synthesis and model-based estimation and diagnosis. We demonstrate the feasibility of the architecture in a simple implementation of the RSE for a simulated Mars rover scenario

Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras

In this paper we propose a fuzzy method to detect free paths in real-time using digital stereo images. It is based on looking for linear variations of depth in disparity maps, which are obtained by processing a pair of rectified images from two stereo cameras. By applying least-squares fitting over groups of disparity maps columns to a linear model, free paths are detected by giving a certainty using a fuzzy rule. Experimental results on real outdoor images are also presented.Nuria Ortigosa acknowledges the support of Universidad Polit'ecnica de Valencia under grant FPI-UPV 2008. Samuel Morillas acknowledges the support of Spanish Ministry of Education and Science under grant MTM 2009-12872-C02-01.Ortigosa Araque, N.; Morillas Gómez, S.; Peris Fajarnes, G.; Dunai Dunai, L. (2012). Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras. 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Computer Vision and Image Understanding, 80(1), 21-41. doi:10.1006/cviu.2000.0872Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., & López, M. T. (2010). Video sequence motion tracking by fuzzification techniques. Applied Soft Computing, 10(1), 318-331. doi:10.1016/j.asoc.2009.08.002Morillas, S., Gregori, V., & Hervas, A. (2009). Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images. IEEE Transactions on Image Processing, 18(7), 1452-1466. doi:10.1109/tip.2009.2019305Poloni, M., Ulivi, G., & Vendittelli, M. (1995). Fuzzy logic and autonomous vehicles: Experiments in ultrasonic vision. Fuzzy Sets and Systems, 69(1), 15-27. doi:10.1016/0165-0114(94)00237-2Alonso, J. M., Magdalena, L., Guillaume, S., Sotelo, M. A., Bergasa, L. M., Ocaña, M., & Flores, R. (2007). Knowledge-based Intelligent Diagnosis of Ground Robot Collision with Non Detectable Obstacles. Journal of Intelligent and Robotic Systems, 48(4), 539-566. doi:10.1007/s10846-006-9125-6McFetridge, L., & Ibrahim, M. Y. (2009). A new methodology of mobile robot navigation: The agoraphilic algorithm. Robotics and Computer-Integrated Manufacturing, 25(3), 545-551. doi:10.1016/j.rcim.2008.01.008Sun, H., & Yang, J. (2001). Obstacle detection for mobile vehicle using neural network and fuzzy logic. Neural Network and Distributed Processing. doi:10.1117/12.441696Ortigosa, N., Morillas, S., & Peris-Fajarnés, G. (2010). Obstacle-Free Pathway Detection by Means of Depth Maps. Journal of Intelligent & Robotic Systems, 63(1), 115-129. doi:10.1007/s10846-010-9498-4Picton, P. D., & Capp, M. D. (2008). Relaying scene information to the blind via sound using cartoon depth maps. Image and Vision Computing, 26(4), 570-577. doi:10.1016/j.imavis.2007.07.005Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334. doi:10.1109/34.888718Scharstein, D., & Szeliski, R. (2002). International Journal of Computer Vision, 47(1/3), 7-42. doi:10.1023/a:1014573219977Felzenszwalb, P. F., & Huttenlocher, D. P. (2006). Efficient Belief Propagation for Early Vision. International Journal of Computer Vision, 70(1), 41-54. doi:10.1007/s11263-006-7899-4Qingxiong Yang, Liang Wang, Ruigang Yang, Stewenius, H., & Nister, D. (2009). Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 492-504. doi:10.1109/tpami.2008.99Zitnick, C. L., & Kang, S. B. (2007). Stereo for Image-Based Rendering using Image Over-Segmentation. International Journal of Computer Vision, 75(1), 49-65. doi:10.1007/s11263-006-0018-8Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. doi:10.1017/cbo9780511811685Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404-418. doi:10.1109/21.52551C. Fodor, J. (1993). A new look at fuzzy connectives. Fuzzy Sets and Systems, 57(2), 141-148. doi:10.1016/0165-0114(93)90153-9Nalpantidis, L., & Gasteratos, A. (2010). Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image and Vision Computing, 28(6), 940-951. doi:10.1016/j.imavis.2009.11.011BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.1

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area