Titlebook: Reasoning with Uncertainty in Robotics; International Worksh Leo Dorst,Michiel Lambalgen,Frans Voorbraak Conference proceedings 1996 Spring

銀版照相

A sensor-based motion planner for mobile robot navigation with uncertainty, robot is equipped with sensors which, if properly used, may provide information to overcome the uncertainty accumulated during the motions. The planner produces robust motion strategies composed of sensor-based motion commands which guarantee that the robot can reach the goal with an error lower th

代理人

Encapsulate

濃縮

Structuring uncertain knowledge with hierarchical bayesian networks,f random variables with a lot of dependencies between them. A Hierarchical Bayesian network (HBN) architecture is developed where elementary random variables are successively combined to new ones, thus yielding compact summaries of the components information. This joint knowledge representation on d

清楚

Uncertainty treatment in a surface filling mobile robot, In order to carry out this mission several modules execute in parallel as the robot evolves. The main modules developed are: the navigation strategy, the localisation system and the map building procedure. This paper shows the models and mechanisms used to overcome uncertainty of the sensors and of

蒼白

ARM

Robot navigation with markov models: A framework for path planning and learning with limited computve performance with experience and to adapt to new circumstances is equally important for long-term operation. Real-time constraints, limited computation and memory, as well as the cost of collecting training data also need to be accounted for. In this paper, we discuss our evolving architecture for

sigmoid-colon

有機體

Belief formation by constructing models,nsider a mode of ‘intelligent’ uncertain reasoning, based on forming beliefs by constructing models, which would seem to be a natural, and simple, step in the evolution of certain of today‘s robots and may provide the robot with a means of reasoning about the uncertainties of the world it encounters.

FANG