Robotic engineers face major challenges to solve the complex actuation needs of Human-Robot Collaboration with existing act robotic gearboxes. Available technologies comprise high-ratio Planetary Gearheads, Cycloid Drives and Harmonic Drives, inherited from conventional industrial robotics. Alternative approaches include Direct-Drive and Quasi Direct-Drive actuation strategies, which propose to cancel or substantially reduce gear ratio, in order to minimize reflected inertia and attain enough backdrivability for collaborative tasks. This paper presents the proof-of-concept validation of a novel high-ratio, Wolfrom-based, gearbox technology that follows a different approach to attain the same objective. Testing five different gearbox prototypes, we confirm the ability of the R2poweR technology to improve efficiency and backdrivability while retaining the weight and control advantages derived from the use of high reduction ratios. The result is a highly efficient, backdrivable, high-ratio gearbox with exciting Huma-Robot Collaboration potential.

Research towards (compliant) actuators, especially redundant ones like the Series Parallel Elastic Actuator (SPEA), has led to the development of drive trains, which have demonstrated to increase efficiency, torque-to-mass-ratio, power-to-mass ratio, etc. In the field of robotics such drive trains can be implemented, enabling technological improvements like safe, adaptable and energy-efficient robots. The choice of the used motor and transmission system, as well as the compliant elements composing the drive train, are highly dependent of the application and more specifically on the allowable weight and size. In order to optimally design an actuator adapted to the desired characteristics and the available space, scaling laws governing the specific actuator can simplify and enhance the reliability of the design process. Although scaling laws of electric motors and links are known, none have been investigated for a complete redundant drive train. The present study proposes to fill this gap by providing scaling laws for electric motors in combination with their transmission system. These laws are extended towards parallelization, i.e. replacing one big motor with gearbox by several smaller ones in parallel. The results of this study show that the torque/mass ratio for a motor-gearbox can not be increased by parallelization, but that it can increase the torque/volume ratio. This is however only the case if a good topology is chosen.

OBJECTIVES: To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. MATERIALS AND METHODS: Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. RESULTS: Graph-traversal heuristics provided high coverage (71-96% of terms in the test sets of discharge summaries) at the expense of subset size (17-51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24-55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. DISCUSSION: Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. CONCLUSION: Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available.