ProceedingsIEEE Computer Society Press, 2001 - Artificial intelligence |
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Page 7
Sensor Planning for Mobile Robot Localization Based on Probabilistic Inference
Using Bayesian Network Department of ... Since we can model causal relations
between situations of the robot ' s behavior and sensing events as nodes of a ...
Sensor Planning for Mobile Robot Localization Based on Probabilistic Inference
Using Bayesian Network Department of ... Since we can model causal relations
between situations of the robot ' s behavior and sensing events as nodes of a ...
Page 8
The low level action control ( LLAC ) identifies local sensor patterns of a limited
sensor information space and directly maps these patterns to the motor command
space . However , since the sensor capability is limited in the real - world and the
...
The low level action control ( LLAC ) identifies local sensor patterns of a limited
sensor information space and directly maps these patterns to the motor command
space . However , since the sensor capability is limited in the real - world and the
...
Page 9
S1 S2 St Sn S1 S2 ( a ) ( b ) Figure 3 : Construction and reconstruction of the
Bayesian network for sensor planning 6 Implementation of LLAC The mobile
robot is basically driven by a potential method . Figure 1 ( left ) shows a trajectory
of the ...
S1 S2 St Sn S1 S2 ( a ) ( b ) Figure 3 : Construction and reconstruction of the
Bayesian network for sensor planning 6 Implementation of LLAC The mobile
robot is basically driven by a potential method . Figure 1 ( left ) shows a trajectory
of the ...
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Contents
Session MIB Assembly Planning Room | 31 |
Session MIC Production System1 Room C | 61 |
Session M2A Planning Control of Manipulators Room | 92 |
Copyright | |
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algorithm analysis applied approach assembly automatic Automation calculated called cell combination complex components computed condition connected considered constraints coordinate corresponding cost defined described determined developed direction disassembly dynamic edge effect Engineering environment equipment error evaluation example execution experiments Figure finger flexible force function geometric given graph grasp holons IEEE initial integrated International introduced Japan joint machine manipulation manufacturing material means measured mechanical method mobile robot module motion movement moving node object obstacles obtained operation optimal orientation parameters path performed planning position possible presented problem Proceedings proposed reference region relation represented Research respectively robot rotation rules scheduling selection sensor sequence shown shows simulation solutions space specific station step structure subassembly surface Table task tion tolerance tool trajectory transition