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| 2. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 3. |
- Galindo, Cipriano, et al.
(författare)
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Life-long optimization of the symbolic model of indoor environments for a mobile robot
- 2007
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Ingår i: IEEE transactions on systems, man and cybernetics. Part B. Cybernetics. - 1083-4419 .- 1941-0492. ; 37:5, s. 1290-1304
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Tidskriftsartikel (refereegranskat)abstract
- The use of a symbolic model of the spatial environment becomes crucial for a mobile robot. that is intended to operate optimally and intelligently in indoor scenarios. Constructing such a model involves important problems that are not solved completely at present. One is called anchoring, which implies to maintain a correct dynamic correspondence between the real world and the symbols in the model. The other problem is adaptation: among the numerous possible models that could be constructed for representing a given environment, optimization involves the selection of one that improves as much as possible the operations of the robot. To cope with both problems, in this paper, we propose a framework that allows an indoor mobile robot to learn automatically a symbolic model of its environment and to optimize it over time with respect to changes in both the environment and the robot operational needs through an evolutionary algorithm. For coping efficiently with the large amounts of information that the real world provides, we use abstraction, which also helps in improving task planning. Our experiments demonstrate that the proposed framework is suitable for providing an indoor mobile robot with a good symbolic model and adaptation capabilities.
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| 4. |
- Galindo, Cipriano, et al.
(författare)
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Robot Task Planning Using Semantic Maps
- 2008
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Ingår i: Robotics and Autonomous Systems. - Amsterdam : Elsevier. - 0921-8890 .- 1872-793X. ; 56:11, s. 955-966
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Tidskriftsartikel (refereegranskat)abstract
- Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.
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| 6. |
- Galindo, Cipriano, et al.
(författare)
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Using semantic information for improving efficiency of robot task planning
- 2007
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Konferensbidrag (refereegranskat)abstract
- The use of semantic information in robotics is an emergent field of research. As a supplement to other types of information, like geometrical or topological, semantics can improve mobile robot reasoning or knowledge inference, and can also facilitate human-robot communication. These abilities are particularly relevant for robots intended to operate in everyday environments populated by people, which typically involve a great number of objects, places, and possible actions. In this paper, we explore a novel usage of semantic information: as an improvement for task planning in complex scenarios like the mentioned ones, where other planners easily find intractable situations. More specifically, we propose to first construct a "semantic" plan composed of categories of objects, places, etc. that solves a "generalized" version of the requested task, and then to use that plan for discarding irrelevant information in the definitive planning carried out on the symbolic instances of those elements (that correspond to physical elements of the world with which the robot can operate). Our results using this approach are promising, and have been compared to other existing approaches.
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