On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Automatic Sensor Placement from Vision Task Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Information invariants for distributed manipulation
International Journal of Robotics Research
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Coordination and Learning in Multirobot Systems
IEEE Intelligent Systems
A Framework and Architecture for Multirobot Coordination
ISER '00 Experimental Robotics VII
Principled Communication for Dynamic Multi-robot Task Allocation
ISER '00 Experimental Robotics VII
Merging Gaussian Distributions for Object Localization in Multi-robot Systems
ISER '00 Experimental Robotics VII
Toward selecting and recognizing natural landmarks
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
The effect of action recognition and robot awareness in cooperative robotic teams
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Vision-Based Pose Computation: Robust and Accurate Augmented Reality Tracking
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Compensating for model uncertainty in the control of cooperative field robots
Compensating for model uncertainty in the control of cooperative field robots
Efficient Information-based Visual Robotic Mapping in Unstructured Environments
International Journal of Robotics Research
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
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In field environments it is not usually possible to provide robots in advance with valid geometric models of its environment and task element locations. The robot or robot teams need to create and use these models to locate critical task elements by performing appropriate sensor based actions. This paper presents a multi-agent algorithm for a manipulator guidance task based on cooperative visual feedback in an unknown environment. First, an information-based iterative algorithm to intelligently plan the robot's visual exploration strategy is used to enable it to efficiently build 3D models of its environment and task elements. The algorithm uses the measured scene information to find the next camera position based on expected new information content of that pose. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. Second, after an appropriate environment model has been built, the quality of the information content in the model is used to determine the constraint-based optimum view for task execution. The algorithm is applicable for both an individual agent as well as multiple cooperating agents. Simulation and experimental demonstrations on a cooperative robot platform performing a two component insertion/mating task in the field show the effectiveness of this algorithm.