Seminář z mobilní robotiky, LS 2009

P. Svestka and M. H. Overmars. Coordinated motion planning for multiple car-like robots using probabilistic roadmaps. In Proc. IEEE Int. Conf. Robot. & Autom., pages 1631-1636, 1995. (md)

Anytime Dynamic A*: An Anytime, Replanning Algorithm (případně původní ARA*: Anytime A* with Provable Bounds on Sub-Optimality) (ji)

Co nového na CeBITu …

RobotChallenge / Puck Collect

Terrence Fong, Sébastien Grange, Charles Thorpe and Charles Baur

IEEE International Workshop on Robot-Human Interactive Communication, September 2001, Bordeaux and Paris, France

Multi-robot remote driving has traditionally been a difficult problem. Whenever an operator is often forced to divide his limited resources (attention, decision making, etc.) among multiple robots, control becomes complicated and performance quickly deteriorates as a result. To remedy this, we need to find ways to make command generation and coordination efficient, so that human-robot interaction is transparent and tasks are easy to perform. In this paper, we discuss the use of collaboration, human-robot dialogue and waypoint-based driving for vehicle teleoperation. We then describe how these techniques can enable a single operator to effectively control multiple mobile robots.

Test Driven Developement Miško Hevery - Software Engineer Google

Počet míst je omezen, je potřeba registrace na careermarket.cz.

Rhombic Dodecahedron Shape for Self-Assembling Robots, M. Yim, J. Lamping, E. Mao, J.G. Chase, Xerox PARC SPL TechReport P9710777, 1997

Abstract A self-assembling robotic system capable of approximating arbitrary three dimensional shapes utilizing repeated rhombic dodecahedron shaped modules is presented. This shape allows very simple rotational motions about its edges. Using this shape and the motion constraints it imposes, a distributed algorithm using localized information is created and simulated. The algorithm causes each module to move in an efficient manner without explicitly planning the entire motion of each module before hand. For most shapes the algorithm achieves full completion of the desired goal configuration. Example of a tea cup shape utilizing 441 modules with 100% completion is shown.

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