Planning sensing strategies in a robot work cell with multi-sensor capabilities

Hutchinson, Seth; Cromwell, R. L.; Kak, Avinash C.

doi:10.1109/70.88098

Cited by 149 publications

(43 citation statements)

References 19 publications

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“…In case of intensity cameras used in inspection tasks, there is a nearly perfect estimate of the geometry and viewpoint [11][12][13]. In assembly tasks [14] and model-based recognition [15][16][17], the geometry is also known. On the other hand, there is no geometry information in reverse engineering.…”

Section: Previous Workmentioning

confidence: 99%

Shape recovery and viewpoint planning for reverse engineering

Zetu

Akgündüz

2005

Int J Adv Manuf Technol

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In this paper, a new methodology for surface reconstruction from sets of points for reverse engineering is described. The sets of points are obtained using stereovision techniques. Techniques for minimizing the number of images required for a complete object reconstruction are explored based on characterizing the shapes to be recovered in terms of visibility and number and nature of cavities. The ultimate goal of our viewpoint planning technique is to minimize the number of images when no prior information about the objects is available.

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Section: Previous Workmentioning

confidence: 99%

Shape recovery and viewpoint planning for reverse engineering

Zetu

Akgündüz

2005

Int J Adv Manuf Technol

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“…Such sensing methods can be converted into anytime algorithms by varying the number of sensor readings. For example, Hutchinson and Kak (1989) present a technique for gradually reducing the ambiguity in the world description over time using the Dempster-Shafer theory of evidence. Hager and Mintz (1987) developed a decision-theoretic technique for selecting optimal sensing strategies.…”

Section: Anytime Sensing In the Real-worldmentioning

confidence: 99%

Resource-bounded sensing and planning in autonomous systems

Zilberstein

1996

Auton Robot

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This paper is concerned with the implications of limited computational resources and uncertainty on the design of autonomous systems. To address this problem, we redefine the principal role of sensor interpretation and planning processes. Following Agre and Chapman's plan-as-communication approach, sensing and planning are treated as computational processes that provide information to an execution architecture and thus improve the overall performance of the system. We argue that autonomous systems must be able to trade off the quality of this information with the computational resources required to produce it. Anytime algorithms, whose quality of results improves gradually as computation time increases, provide useful performance components for timecritical sensing and planning in robotic systems. In our earlier work, we introduced a compilation scheme for optimal composition of anytime algorithms, This paper demonstrates the applicability of the compilation technique to the construction of autonomous systems. The result is a flexible approach to construct systems that can operate robustly in real-time by exploiting the tradeoff between time and quality in planning, sensing and plan execution.

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“…Researchers have used model-based approaches, requiring previously known environments (Burschka, 1997;Cowan, 1988;Hutchinson, 1989;Kececi, 1998;Laugier, 1995). Target motions (if any) are assumed to be known (Laugier, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…Potential tasks include explosive ordinance removal, de-mining and hazardous waste handling, exploration and development of space, environment restoration, and construction Huntsberger, 2000;Hutchinson, 1989;Khatib, 1995;Osborn, 1987;Shaffer, 1992).…”

Section: Introductionmentioning

confidence: 99%

Visually Guided Cooperative Robot Actions Based on Information Quality

Sujan

Dubowsky

2005

Auton Robot

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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.

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Planning sensing strategies in a robot work cell with multi-sensor capabilities

Cited by 149 publications

References 19 publications

Shape recovery and viewpoint planning for reverse engineering

Shape recovery and viewpoint planning for reverse engineering

Resource-bounded sensing and planning in autonomous systems

Visually Guided Cooperative Robot Actions Based on Information Quality

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