Reducing latency when using Virtual Reality for teaching in sport

Latency is a frequently cited shortcoming of Virtual Reality (VR) applications. To compensate for excessive latency, prediction mechanisms may use sophisticated mathematical algorithms, which may not be appropriate for complex virtual teaching applications. This paper suggests that heuristic prediction algorithms could be used to develop more effective and general systems for VR educational applications. A fast synchronization squash simulation illustrates where heuristic prediction can be used to deal with latency problems

Design considerations for delivering e-learning to surgical trainees

Copyright © 2011, IGI Global. Distributed with permission.Challenges remain in leveraging e-health technologies for continuous medical education/professional development. This study examines the interface design and learning process features related to the use of multimedia in providing effective support for the knowledge and practice of surgical skills. Twenty-one surgical trainees evaluated surgical content on a CD-ROM format based on 14 interface design and 11 learning process features using a questionnaire adapted from an established tool created to assess educational multimedia. Significant Spearman’s correlations were found for seven of the 14 interface design features – ‘Navigation’, ‘Learning demands’, ‘Videos’, ‘Media integration’, ‘Level of material’, ‘Information presentation’ and ‘Overall functionality’, explaining ratings of the learning process. The interplay of interface design and learning process features of educational multimedia highlight key design considerations in e-learning. An understanding of these features is relevant to the delivery of surgical training, reflecting the current state of the art in transferring static CD-ROM content to the dynamic web or creating CD/web hybrid models of education

Online hyper-evolution of controllers in multirobot systems

In this paper, we introduce online hyper-evolution (OHE) to accelerate and increase the performance of online evolution of robotic controllers. Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms and controllers online during task execution. We present two approaches: OHE-fitness, which uses the fitness score of controllers as the criterion to select promising algorithms over time, and OHE-diversity, which relies on the behavioural diversity of controllers for algorithm selection. Both OHE-fitness and OHE-diversity are distributed across groups of robots that evolve in parallel. We assess the performance of OHE-fitness and of OHE-diversity in two foraging tasks with differing complexity, and in five configurations of a dynamic phototaxis task with varying evolutionary pressures. Results show that our OHE approaches: (i) outperform multiple state-of-the-art algorithms as they facilitate controllers with superior performance and faster evolution of solutions, and (ii) can increase effectiveness at different stages of evolution by combining the benefits of multiple algorithms over time. Overall, our study shows that OHE is an effective new paradigm to the synthesis of controllers for robots.info:eu-repo/semantics/acceptedVersio

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study

Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users

Development of a tabletop guidance system for educational robots

The guidance of a vehicle in an outdoor setting is typically implemented using a Real Time Kinematic Global Positioning System (RTK-GPS) potentially enhanced by auxiliary sensors such as electronic compasses, rotation encoders, gyroscopes, and vision systems. Since GPS does not function in an indoor setting where educational competitions are often held, an alternative guidance system was developed. This article describes a guidance method that contains a laser-based localization system, which uses a robot-borne single laser transmitter spinning in a horizontal plane at an angular velocity up to 81 radians per second. Sensor arrays positioned in the corners of a flat rectangular table with dimensions of 1.22 m × 1.83 m detected the laser beam passages. The relative time differences among the detections of the laser passages gave an indication of the angles of the sensors with respect to the laser beam transmitter on the robot. These angles were translated into Cartesian coordinates. The guidance of the robot was implemented using a uni-directional wireless serial connection and position feedback from the localization system. Three experiments were conducted to test the system: 1) the accuracy of the static localization system was determined while the robot stood still. In this test the average error among valid measurements was smaller than 0.3 %. However, a maximum of 3.7 % of the measurements were invalid due to several causes. 2) The accuracy of the guidance system was assessed while the robot followed a straight line. The average deviation from this straight line was 3.6 mm while the robot followed a path with a length of approximately 0.9 m. 3) The overall performance of the guidance system was studied while the robot followed a complex path consisting of 33 sub-paths. The conclusion was that the system worked reasonably accurate, unless the robot came in close proximity

Tutorials at PPSN 2016

PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field