{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:46:09Z","timestamp":1777715169752,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[1991,8,1]],"date-time":"1991-08-01T00:00:00Z","timestamp":681004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[1991,8]]},"abstract":"<jats:p>In this article we consider the problem of task-directed information gathering. We first develop a decision-theo retic model of task-directed sensing in which sensors are modeled as noise-contaminated, uncertain measurement systems, and sensing tasks are inodeled by a transforma tion describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system.<\/jats:p>\n                  <jats:p>This description allows us to develop a standard condi tional Bayes decision-making model where the value of information, or payoff, of an estimate is defined as the average utility (the expected value of some function of decision or estimation error) relative to the current proba bility distribution and the best estimate is that which max imizes payoff. The optimal sensor viewing strategy is that which maximizes the net payoff (decision value minus observation costs) of the final estimate. The advantage of this solution is generality\u2014it does not assume a particular sensing modality or sensing task. However, solutions to this updating problem do not exist in closed form. This motivates the development of an approximation to the optimal solution based on a grid-based implementation of Bayes' theorem.<\/jats:p>\n                  <jats:p>We describe this algorithm, analyze its error properties. and indicate how it can be made robust to errors in the description of sensors and discrepancies between geomet ric models and sensed objects. We also present the results of this fusion technique applied to several different infor mation gathering tasks in simulated situations and in a distributed sensing system we have constructed.<\/jats:p>","DOI":"10.1177\/027836499101000401","type":"journal-article","created":{"date-parts":[[2007,3,11]],"date-time":"2007-03-11T03:05:13Z","timestamp":1173582313000},"page":"285-313","source":"Crossref","is-referenced-by-count":36,"title":["Computational Methods for Task-directed Sensor Data Fusion and Sensor Planning"],"prefix":"10.1177","volume":"10","author":[{"given":"Greg","family":"Hager","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science University of Pennsylvania Philadelphia, Pennsylvania 19104-6389"}]},{"given":"Max","family":"Mintz","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science University of Pennsylvania Philadelphia, Pennsylvania 19104-6389"}]}],"member":"179","published-online":{"date-parts":[[1991,8,1]]},"reference":[{"key":"atypb1","volume-title":"Robotic Object Recognition Using Vision and Touch","author":"Allen, P.","year":"1988"},{"key":"atypb2","volume-title":"Proceedings of the First International Canference on Computer Vision","author":"Aloimonos, J."},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1177\/027836498800700605"},{"key":"atypb4","volume-title":"The Third IEEE Workshop on Computer Vision","author":"Bajcsy, R."},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1109\/5.5968"},{"key":"atypb6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-4286-2"},{"key":"atypb7","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box, G.","year":"1976"},{"key":"atypb8","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(81)90028-X"},{"key":"atypb9","volume-title":"Proceedings of the 1985 IEEE International Conference on Robotics and Automation","author":"Brooks, R."},{"key":"atypb10","volume-title":"Proceedings of the 1989 IEEE International Conference on Robotics and Automation","author":"Cameron, A."},{"key":"atypb11","volume-title":"Proceedings of IJCAI-75","author":"Coles, L."},{"key":"atypb12","volume-title":"Proceedings of the 1988 IEEE International Conference on Robotics and Automation","author":"Cowan, C.K."},{"key":"atypb13","volume-title":"Integration, Coordination, and Control of Multi-Sensor Systems","author":"Durrant-Whyte, H.","year":"1988"},{"key":"atypb14","volume-title":"Theory of Optimal Experiments","author":"Fedorov, V.","year":"1972"},{"key":"atypb15","volume-title":"Robotics Research, The First International Symposium","author":"Giralt, G."},{"key":"atypb16","doi-asserted-by":"publisher","DOI":"10.1109\/JRA.1986.1087057"},{"key":"atypb17","volume-title":"Active reduction of uncertainty in multi-sensor systems. 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