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Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors.
Designing robust, modular and fast mobile robots, operating in high dynamic environments is a challenging task.
Such a revision must happen particularly with respect to mobile robots operating in dynamic real life environments.
Mobile robots operating in real and populated environments usually execute tasks that require accurate knowledge on their position.
An implemenThison of the proposed strategy is discussed, and simulation results presented.
This paper presents a path planning algorithm for car-like mobile robots operating on a known static rough terrain environment.
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This paper concerns the specific production systems in which mobile robots operate in the same shop floor and at the same time with conventional and multi-functional machines and humans.
The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects.
Four real field experiments were conducted using a mobile robot operating in an agricultural field.
An autonomous mobile robot operating in an unstructured environment must be able to deal with dynamic changes of the environment.
Experiments show that the Bayesian sensor planning strategy is capable of controlling an autonomous mobile robot operating in partly known environments.
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