The need for a training modality for tooth extraction procedures is increasing, as dental students do not feel properly trained. In this study, a prototype of a training setup is designed, in which extraction procedures can be performed on jaw models and cadaveric jaws. The prototype was designed in a way that it can give real-time feedback on the applied forces in all three dimensions (buccal/lingual, mesial/distal, and apical/coronal), torques, and angular velocity. To evaluate the prototype, a series of experimental extractions on epoxy models, conserved jaws, and fresh frozen jaws were performed. Extraction duration (s), angular velocity (degrees/s), average force (N), average torque (Nm), linear impulse (Ns), and angular impulse (N ms) were shown in real-time to the user and used to evaluate the prototype. In total, 342 (92.9 % ) successful extractions were performed using the prototype (n= 113 epoxy factory-made, n=187 epoxy re-used, n=17 conserved, n=25 fresh frozen). No significant differences were found between the conserved and the fresh frozen jaws. The fresh frozen extraction duration, linear impulse, and angular impulse differed significantly from the corresponding values obtained for the epoxy models. Extractions were successfully performed, and the applied forces, torques, and angular velocity were recorded and shown as real-time feedback using the prototype of the dental extraction trainer. The feedback of the prototype is considered reliable.@en

a measurement setup is proposed that, for the first time, is capable of capturing the combination of high forces and subtle movements exerted during tooth removal procedures in high detail and in a reproducible manner by using robot technology. The outcomes of a design process from a collaboration between clinicians, mechanical and software engineers together with first results are presented in this proof of concept.Clinical relevance - by measuring all aspects of tooth removal in a single setup a strong database can be build that will deliver the data needed to gain scientific understanding of what makes (un)successful tooth removal. It gives a unique opportunity to model the procedure, evaluate techniques, understand and predict adverse events as well as to create new evidence-based teaching methods.

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Details for the errata of the paper "Design and Evaluation of a Balance Assistance Control Moment Gyroscope" published in the ASME Journal of Mechanisms and Robotics are presented.@en

We recently proposed the theoretical idea of a wearable balancing aid, consisting of a set of control moment gyroscopes (CMGs) contained into a backpacklike orthopedic corset. Even though similar solutions have been reported in the literature, important considerations in the synthesis and design of the actuators remained to be addressed. These include design requirements such as aerodynamic behavior of the spinning flywheel, induced dynamics by the wearer's motion, and stresses in the inner components due to the generated gyroscopic moment. In this paper, we describe the design and evaluation of a single CMG, addressing in detail the aforementioned requirements. In addition, given the application of the device in human balance, the design follows the European directives for medical electrical equipment. The developed system was tested in a dedicated balance test bench showing good agreement with the expected flywheel speed, and calculated power requirements in the actuators and output gyroscopic moment. The device was capable of producing a peak gyroscopic moment of approximately 70 N·m with a total CMG mass of about 10 kg.

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This paper describes Team Delft’s robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft’s robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot’s software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.

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