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3D objects (artifacts) are made to fulfill functions. Designing an object often starts with defining a list of functionalities or affordances (action possibilities) that it should provide, known as functional requirements. Today, designing 3D object models is still a slow and difficult activity, with few Computer-Aided Design (CAD) tools capable to explore the design solution space. The purpose of this study is to explore shape generation conditioned on desired affordances. We introduce an algorithm for generating voxelgrid object shapes which afford the desired functionalities. We follow the principle form follows function , and assume that object forms are related to affordances they provide (their functions). First, we use an artificial neural network to learn a function-to-form mapping from a dataset of affordance-labeled objects. Then, we combine forms providing one or more desired affordances, generating an object shape expected to provide all of them. Finally, we verify in simulation whether the generated object indeed possesses the desired affordances, by defining and executing affordance tests on it. Examples are provided using the affordances contain-ability, sit-ability, and support-ability. [ABSTRACT FROM AUTHOR]

In this work, a complete solution is provided for detecting and identifying cylindrical shapes, which are commonly found in household and industrial environments, using consumer-grade RGB-D cameras. Most standard approaches to detect and identify cylinders are not robust to outliers (e.g. points on other objects in the scene), which limits their applicability in realistic scenes. In addition, these methods fail to benefit from environmental constraints, e.g. the fact that cylinders often lie or stand on flat surfaces. To tackle the aforementioned limitations, we introduce three main novelties: (i) a point cloud soft voting scheme with curvature information that reduces the influence of outliers and noise, (ii) a selective sampling of the orientation space that favorsorientations known a priori , and (iii) a deep-learning based classifier to filter out objects with non-cylindrical appearance in the 2D images, thus further improving robustness to outliers. A set of experiments with synthetically generated data are used to assess the robustness of our fitting method to different levels of outliers and noise. The results demonstrate that incorporating the principal curvature direction within the orientation voting process allows for large improvements on cylinders parameters estimation. Furthermore, we demonstrate that combining the 2D deep-learning cylinder classifier with the 3D orientation voting scheme allows for large speed-up and accuracy improvements on cylinder identification. The qualitative and quantitative results with real data acquired from a consumer RGB-D camera, confirm the advantages of the proposed framework. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark. [ABSTRACT FROM AUTHOR]