Depth and presence sensors are used to prevent collisions in environments where human/robot coexistence is relevant. To address the problem of occluded areas, we extend in this paper a recently introduced efficient approach for preventing collisions using a single depth sensor to multiple depth and/or presence sensors. Their integration is systematically handled by resorting to the concept of image planes, where computations can be suitable carried out on 2D data without reconstructing obstacles in 3D. To maximize the on-line collision detection performance by multiple sensor integration, an off-line optimal sensor placement problem is formulated in a probabilistic framework, using a cell decomposition and characterizing the probability of cells being in the shadow of obstacles or unobserved. This approach allows to fit the optimal numerical solution to the most probable operating conditions of a human and a robot sharing the same working area. Three examples of optimal sensor placement are presented. ©2010 IEEE.
Multiple depth/presence sensors: Integration and optimal placement for human/robot coexistence / Flacco, Fabrizio; DE LUCA, Alessandro. - (2010), pp. 3916-3923. (Intervento presentato al convegno 2010 IEEE International Conference on Robotics and Automation, ICRA 2010 tenutosi a Anchorage, AK nel 3 May 2010 through 7 May 2010) [10.1109/robot.2010.5509125].
Multiple depth/presence sensors: Integration and optimal placement for human/robot coexistence
FLACCO, FABRIZIO;DE LUCA, Alessandro
2010
Abstract
Depth and presence sensors are used to prevent collisions in environments where human/robot coexistence is relevant. To address the problem of occluded areas, we extend in this paper a recently introduced efficient approach for preventing collisions using a single depth sensor to multiple depth and/or presence sensors. Their integration is systematically handled by resorting to the concept of image planes, where computations can be suitable carried out on 2D data without reconstructing obstacles in 3D. To maximize the on-line collision detection performance by multiple sensor integration, an off-line optimal sensor placement problem is formulated in a probabilistic framework, using a cell decomposition and characterizing the probability of cells being in the shadow of obstacles or unobserved. This approach allows to fit the optimal numerical solution to the most probable operating conditions of a human and a robot sharing the same working area. Three examples of optimal sensor placement are presented. ©2010 IEEE.File | Dimensione | Formato | |
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