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robot planning
Problems in commonsense and robot planning are approached by methods adapted from program synthesis research; planning is regarded as an application of automated deduction.
      
The proposed scheme can also be used for mobile robot planning.
      
Reasoning about change is a central issue in research on human and robot planning.
      
A second set of techniques that largely do not apply is those for coordinated robot planning.
      
An integrated robot planning and execution framework requires accurate state detection.
      
Considered here is their application to on-line, centralized, multiple mobile robot planning problems.
      
Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled.
      
Implementing these techniques leads to decreased planning time and allows for on-the-fly robot planning.
      
It also relates our work to previous research on multi-robot planning.
      
In the new multi-robot planning system presented, each robot plans trajectories as described in sections 2.1 through 2.3.
      
In essence, the teams of humans are performing like a centralized multi-robot planning algorithm.
      
More emphasis will be placed on roadmaps as this technique has shown promising results for complicated robot planning problems.
      
Multi-robot planning has attracted considerable research, and the above bibliographical references are not exhaustive.
      
Section 3 then describes the application of SBL to multi-robot planning.
      
This research adds new techniques that improve upon existing sampling strategies when applied to coupled multi-robot planning.
      
This section presents a new endgame region for multiple mobile robot planning that exploits some geometric properties of a multi-rover system.
      
That is, it has an exponentially fast convergence for general motion planning problems, including multi-robot planning problems.
      
The work developed on multi-robot planning is mainly focused on the problem of distributed task planning for a team of heterogeneous robots.
      
The market-based robot planning system, on the other hand, is able to benefit from its distributed nature.
      
 

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