A survey of automated visual inspection
Computer Vision and Image Understanding
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Task-Oriented Generation of Visual Sensing Strategies in Assembly Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Vision for Complete Scene Reconstruction and Exploration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor planning for 3D object search
Computer Vision and Image Understanding
A Solution to the Next Best View Problem for Automated Surface Acquisition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Automated photogrammetric network design using the parisian approach
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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Autonomous sensor planning is a problem of interest to scientists in the fields of computer vision, robotics, and photogrammetry. In automated visual tasks, a sensing planner must make complex and critical decisions involving sensor placement and the sensing task specification. This paper addresses the problem of specifying sensing tasks for a multiple manipulator workcell given an optimal sensor placement configuration. The problem is conceptually divided in two different phases: activity assignment and tour planning. To solve such problems, an optimization methodology based on evolutionary computation is developed. Operational limitations originated from the workcell configuration are considered using specialized heuristics as well as a floating-point representation based on the random keys approach. Experiments and performance results are presented.