1000 resultados para Mr. Robot


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Redundant sensors are needed on a mobile robot so that the accuracy with which it perceives its surroundings can be increased. Sonar and infrared sensors are used here in tandem, each compensating for deficiencies in the other. The robot combines the data from both sensors to build a representation which is more accurate than if either sensor were used alone. Another representation, the curvature primal sketch, is extracted from this perceived workspace and is used as the input to two path planning programs: one based on configuration space and one based on a generalized cone formulation of free space.

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Planner is a formalism for proving theorems and manipulating models in a robot. The formalism is built out of a number of problem-solving primitives together with a hierarchical multiprocess backtrack control structure. Statements can be asserted and perhaps later withdrawn as the state of the world changes. Under BACKTRACK control structure, the hierarchy of activations of functions previously executed is maintained so that it is possible to revert to any previous state. Thus programs can easily manipulate elaborate hypothetical tentative states. In addition PLANNER uses multiprocessing so that there can be multiple loci of changes in state. Goals can be established and dismissed when they are satisfied. The deductive system of PLANNER is subordinate to the hierarchical control structure in order to maintain the desired degree of control. The use of a general-purpose matching language as the basis of the deductive system increases the flexibility of the system. Instead of explicitly naming procedures in calls, procedures can be invoked implicitly by patterns of what the procedure is supposed to accomplish. The language is being applied to solve problems faced by a robot, to write special purpose routines from goal oriented language, to express and prove properties of procedures, to abstract procedures from protocols of their actions, and as a semantic base for English.

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This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and use of the extra blocks as temporary supports and counterweights in the course of construction. BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces. Various alternative approaches are discussed, and suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, CONNIVER, for this type of problem solving.

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M J Neal, A hardware proof of concept of a sailing robot for ocean observation, IEEE Journal of Oceanic Engineering, 2006 accepted for publication RAE2008

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M J Neal and J Timmis. Timidity: A useful mechanism for robot control? Informatica - special issue on perception and emotion based control, 4(27):197-204, 2003.

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Hardy, N. W., Barnes, D. P., Lee, M. (1987). Declarative sensor knowledge in a robot monitoring system. In: Languages for Sensor-Based Control in Robotics, Ulrich Rembold and Klaus H?rmann (eds), Springer-Verlag, p. 169-188.

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N. W. Hardy, M. H. Lee, and D. P. Barnes. Knowledge engineering in robot control. In Proceedings of Expert Systems '83, pages 70-77, Cambridge, 1983.

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Lee, M., Hardy, N., & Barnes, D. P. (1983). Error recovery in robot applications. 217-222. Paper presented at 6th British Robot Association Annual Conference, Birmingham, Birmingham, United Kingdom.

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M. H. Lee and Q. Meng, Growth of Motor Coordination in Early Robot Learning, IJCAI-05, 2005.

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Meng, Q., Lee, M. (2003). Adapting Home Service Robot Behaviours by Experience Reuse and Interaction with Humans. 673-678. Paper presented at IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Port Island, Kobe, Japan

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T. G. Williams, J.J. Rowland, and Lee M.H., Teaching from Examples in Assembly and Manipulation of Snack Food Ingredients by Robot, Proc. IEEE/RSJ Int. Conf. on Robots and Systems (IROS 2001), Nov., 2001, pp2300-2305.

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T. G. Williams, J.J. Rowland, Lee M.H. and M.J. Neal Teaching by Example in Food Assembly by Robot, Proc. 2000 IEEE Int. Conf. On Robotics and Automation, San Francisco, April 2000, pp3247-52.

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King R. D., Whelan, K. E., Jones, F. M., Reiser, P. G. K., Bryant, C. H., Muggleton, S., Kell, D. B. and Oliver, S. G. (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427 (6971) p247-252

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Walker,J. and Wilson,M.S., 'Evolving mobile robot behaviours in simulation -- a comparison', Towards Intelligent Mobile Robots; Proceedings of the 3rd annual British conference on autonomous mobile robotics and autonomous systems (TIMR'01), 2001

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Soldatova, L., Clare, A., Sparkes, A. and King, R. D. (2006) An ontology for a robot scientist. Bioinformatics 2006 22: 464-471