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This book addresses the relation between Problem-Based Learning (PBL) and interdisciplinarity and challenges the often implicit assumption that PBL leads to interdisciplinarity by default. The book examines theoretical and philosophical aspects of PBL and interdisciplinary learning. The first part of the book conceptualises the notions of problem-based learning and interdisciplinary learning, and highlights some key overlaps and ways of conceiving of their interrelatedness. It discusses the role of problem-based medical education in relation to interdisciplinary professionalism in medical education. Taking the reader into the realm of techno-anthropology, the book discusses the role of problems and projects in transgressing disciplines, and presents an analysis of three challenges facing new students when entering interdisciplinary and problem-based higher education. The second part of the book focuses on practicing interdisciplinarity in problem-based higher education. It explores how the construction of problems in interdisciplinary PBL projects can be seen from the perspectives of multicultural groups, and examines group processes in interdisciplinary PBL projects. It concludes by taking a closer look at student practices in interdisciplinary PBL, and at how students are positioned and position themselves in the complex transdisciplinary PBL project.

Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the artificial neural network inspired by the remarkable abilities of the animals in different tasks. This paper describes the theory and implementation of neural networks for hand-eye calibration and inverse kinematics of a six degrees of freedom robot arm equipped with a stereo vision system. The feedforward neural network and the network training with error propagation algorithm are applied. The proposed approaches are validated in experiments. The results indicate that the hand-eye calibration with simple neural network outperforms the conventional method. Meanwhile, the neural network exhibits a promising performance in solving inverse kinematics. [ABSTRACT FROM AUTHOR]

This paper describes a navigation method based on road detection using both a laser scanner and a vision sensor. The method is to classify the surface in front of the robot into traversable segments (road) and obstacles using the laser scanner, this classifies the area just in front of the robot (2.5 m). The front looking camera is used to classify the road from this distance and forward, taking a seed area from the laser scanner data and from this estimate the outline of the visible part of the road. The method has been tested successfully on gravelled and asphalt roads in a national park environment. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive comparative study of several nonlinear controllers for stabilisation of the under actuated unstable nonlinear object known as the Acrobot in the literature. The object is a two DOF robot arm only actuated at the elbow. The study compares several control algorithms from the literature and a new algorithm developed during the study. The comparison is based on both simulation and real experiments for all controllers. [ABSTRACT FROM AUTHOR]

The paper describes the possibilities of enhancing the performance of autonomous vehicles by interacting with the surrounding environment. Benefits and drawbacks of the use of different sensors and strategies for integrating the acquired knowledge are examined in an example case of a ‘through partly closed door' navigation task for an AGV. This illustrates the increased ability of sensors to acquire knowledge by actively doing explorative actions. A combined mechanical actuation and probing device accentuates the sense-act paradigm. The theory and algorithms used are described in detail, and extensive experiments with an implementation of the ‘through door' navigation algorithm on our non-holonomic test-bed Autonomous Guided Vehicle are documented. [ABSTRACT FROM AUTHOR]