Towards an Iterative Algorithm for the Optimal Boundary Coverage of a 3D Environment

This paper presents a new optimal algorithm for locating a set of sensors in 3D able to see the boundaries of a polyhedral environment. Our approach is iterative and is based on a lower bound on the sensors' number and on a restriction of the original problem requiring each face to be observed in its entirety by at least one sensor. The lower bound allows evaluating the quality of the solution obtained at each step, and halting the algorithm if the solution is satisfactory. The algorithm asymptotically converges to the optimal solution of the unrestricted problem if the faces are subdivided into smaller part

Camera Placement Planning Avoiding Occlusion: Test Results Using a Robotic Hand/Eye System

Camera placement experiments are presented that demonstrate the effectiveness of a viewpoint planning algorithm that avoids occlusion of a visual target. A CCD camera mounted on a robot in a hand-eye configuration is placed at planned unobstructed viewpoints to observe a target on a real object. The validity of the method is tested by placing the camera inside the viewing region, that is constructed using the proposed new sensor placement planning algorithm and observing whether the target is truly visible. The accuracy of the boundary of the constructed viewing region is tested by placing the camera at the critical - locations of the viewing region boundary and confirming that the target is barely visible. The corresponding scenes from the candidate viewpoints are shown demonstrating that occlusions are properly avoided

The MVP sensor planning system for robotic vision tasks

The MVP (machine vision planner) model-based sensor planning system for robotic vision is presented. MVP automatically synthesizes desirable camera views of a scene based on geometric models of the environment, optical models of the vision sensors, and models of the task to be achieved. The generic task of feature detectability has been chosen since it is applicable to many robot-controlled vision systems. For such a task, features of interest in the environment are required to simultaneously be visible, inside the field of view, in focus, and magnified as required. In this paper, we present a technique that poses the vision sensor planning problem in an optimization setting and determines viewpoints that satisfy all previous requirements simultaneously and with a margin. In addition, we present experimental results of this technique when applied to a robotic vision system that consists of a camera mounted on a robot manipulator in a hand-eye configuration

Computing camera viewpoints in a robot work-cell

Automatically planning a camera viewpoint for tasks such as inspection in an active robot work-cell is a difficult problem. This paper discusses new methods for computing viewpoints which meet the feature detectability constraints of focus, field-of-view, visibility, and resolution. A theoretical outline of the method is presented, followed by experimental results and a discussion of future work

Model-Based Planning of Sensor Placement and Optical Settings

We present a model-based vision system that automatically plans the placement and optical settings of vision sensors in order to meet certain generic task requirements common to most industrial machine vision applications. From the planned viewpoints, features of interest on an object will satisfy particular constraints in the image. In this work, the vision sensor is a CCD camera equipped with a programmable lens (i.e. zoom lens) and the image constraints considered are: visibility, resolution and field of view. The proposed approach uses a geometric model of the object as well as a model of the sensor. in order to reason about the task and the environment The sensor planning system then computes the regions in space as well as the optical settings that satisfy each of the constraints separately. These results are finally combined to generate acceptable viewing locations and optical settings satisfying all constraints simultaneously. Camera planning experiments are described in which a robot-arm positions the camera at a computed location and the planned optical settings are set automatically. The corresponding scenes from the candidate viewpoints are shown demonstrating that the constraints are indeed satisfied. Other constraints, such as depth of focus, as well as other vision sensors can also be considered resulting in a fully integrated sensor planning system

Automated sensor planning for robotic vision tasks

A method is presented to determine viewpoints for a robotic vision system for which object features of interest will simultaneously by visible, inside the field-of-view, in-focus, and magnified as required. A technique that poses the problem in an optimization setting in order to determine viewpoints that satisfy all requirements simultaneously and with a margin is presented. The formulation and results of the optimization are shown, as well as experimental results in which a robot vision system is positioned and its lens is set according to this method. Camera views are taken from the computed viewpoints in order to verify that all feature detectability requirements are satisfied

A Nearly Optimal Algorithm for covering the interior of an Art Gallery

The problem of locating visual sensors can be often modeled as 2D Art Gallery problems. In particular, tasks such as surveillance require observing the interior of a polygonal environment (interior covering, IC), while for inspection or image based rendering observing the boundary (edge covering, EC) is sufficient. Both problems are NP-hard, and no technique is known for transforming one problem into the other. Recently, an incremental algorithm for EC has been proposed, and its near-optimality has been demonstrated experimentally. In this paper we show that, with some modification, the algorithm is nearly optimal also for IC. The algorithm has been implemented and tested over several hundreds of random polygons with and without holes. The cardinality of the solutions provided is very near to, or coincident with, a polygon-specific lower bound, and then suboptimal or optimal. In addition, our algorithm has been compared, for all the test polygons, with recent heuristic sensor location algorithms. In all cases, the cardinality of the set of guards provided by our algorithm was less than or equal to that of the set computed by the other algorithms. An enhanced version of the algorithm, also taking into account range and incidence constraints, has also been implemente

Enhancing education and training through data-driven adaptable games in flipped classrooms

The Flipped Classroom (FC) is a set of pedagogical approaches that move the information transmission out of class and exploit class time for active and/or peer learning activities. In this context, students are required to engage with pre- and/or post-class activities in order to prepare themselves for class work. The FC instruction method has already been used in conjunction with other learning strategies. This theoretical paper presents the first developmental steps of a research project, which aims at building the FC through a fully bespoke and personalized experience, by using data-driven adaptable games and problem-based learning elements to improve the learning experience. The project will develop a gaming platform that will support the whole FC in a cyclical perspective, and aims to use the resources of gamification in a more significant manner that could go beyond score tracking and badges. Moreover, the problem-based learning approach will be used to better frame the learning activities included in FCs, while learning analytics features will provide adaptable learning pathways. The potential of this approach is to build a better FC experience for all the stakeholders. Students will be given more agency to calibrate their learning experience, while educators can monitor the students’ progress more effectively and adjust their learning activities accordingly. Finally, researchers will get better insight into the FC learning process, and the mechanics, which contribute to optimize the learning experience

PBL3.0:Integrating Learning Analytics and Semantics in Problem-Based Learning

This paper presents the PBL3.0 project that aims at enhancing Problem Based Learning (PBL) with Learning Analytics (LA) and Learning Semantics (LS) in order to produce a new educational paradigm and pilot it to produce relevant policy recommendations. To this end, the project will reach the following objectives and corresponding specific goals: 1) Construct a new educational approach that combines a well-established learning strategy like PBL with novel technologies in learning like LA in PBL respecting legal and ethical considerations (PBL_LA), 2) Design a semantic model for PBL_LA, which will enable the annotation of learning resources in order to easily integrate them to the PBL approach and enable their discoverability when setting personalized learning pathways, 3) Adapt a set of open source software tools for supporting PBL_LA and the semantic model based on existing Learning Management Systems, analytics tools, and an intuitive semantic annotation tool, 4) Create relevant, semantically annotated educational material and perform trials at various sites in order to draw evidence-based conclusions, 5) Produce relevant policy recommendations for PBL_LA that could raise the quality in education and training, 6) Create an organic ecosystem of among others organizations, researchers, educators, students with an interest in PBL_LA. Finally, the project will develop a Community of Practice, where institutions and individuals from across Europe will be able to exchange knowledge and expertise on LA, learning semantics, innovative learning tools and approaches. This aims to support transnational cooperation and mutual learning on forward-looking issues between key stakeholders to provide solutions to current challenges in education and training