This paper presents the design and experimental results of a proprioceptive, high-bandwidth quasi-direct drive (QDD) actuator for highly dynamic robotic applications. A comprehensive review of the mechanical design of the PULSE115-60 actuator is presented, with particular focus on the design parameters affecting the dynamic performance of the actuator and a full specification is provided. Fundamental parameters to describe the dynamic behaviour of an actuator are discussed, and an experimental method to determine speed and torque bandwidth of the actuator is presented. A rigorous method to determine backdrive torque is also explained. Finally, experimental results quantifying the dynamic performance of the PULSE115-60 actuator are discussed. The PULSE115-60 actuator has a highly dynamic response, surpassing the torque bandwidth at low torque amplitudes showcased in state-of-the-art literature. The differences between current and torque bandwidth, two concepts often conflated in literature, are elucidated. Experimental procedures detailed in previous work are discussed and a novel standardised procedure is proposed for robust characterisation and fair comparison of different actuation systems. Finally, performance results for PULSE115-60 are presented, demonstrating a torque bandwidth of 66.3 Hz at an amplitude of 6 N•m, ±0.11° of backlash and 0.37 N•m of backdrive torque.

Physical Human-Robot Interaction(pHRI) re-quires taking safety into account from the design board to the collaborative operation of any robot. For collaborative robotic environments, where human and machine are sharing space and interacting physically, the analysis and quantification of impacts becomes very relevant and necessary. Furthermore, analyses of this kind are a valuable source of information for the design of safer, more efficient pHRI. In the definition of the first parameter for dynamic impact analysis, the dynamic impact mitigation capacity was considered for certain configurations of the robot, but the design characteristics of the robot, such as the inertia of actuators, were not included. This paradigm changed when MIT presented the “impact mitigation factor” (IMF) with which, in addition to considering the ability of a certain robot to mitigate impacts for every configuration, it was possible to quantify backdriveability by taking the inertia of actuators into account for the calculation of the factor. However, IMF was proposed as a method to analyse floating robots like. This paper presents the Generalised Impact Absorption Factor (GIAF), suitable for both floating and fixed-base robots. GIAF is a valuable design parameter, as it provides information about the backdriveability of each joint, while allowing the comparison of impact response between floating and fixed-base robotic platforms. In this work, the mathematical definition of GIAF is developed and examples of possible uses of GIAF are presented.

This study reports the initial testing of a gait exoskeleton for Spinal Muscular Atrophy (SMA) patients having variable muscle strength with no balance or ambulation capabilities. To improve the quality of life of such patients, a pediatric gait exoskeleton was developed. The ATLAS exoskeleton has 8 active degrees of freedom (DOF): 2 at the hip (adduction/abduction and flexion/extension), 1 at the knee and ankle joint for flexion and extension. A feasibility test was performed to gauge the initial response of the patients. This study demonstrates that the exoskeleton was able to provide gait assistance and sit-to-stand movements effectively to the subjects. This kind of wearable exoskeleton will play a key role in the rehabilitation of SMA patients and delay further metabolic degeneration in the future.

Many robotics applications require contact with the environment, from traditional pick and place task to legged locomotion. Nevertheless, to increase adaptability to different terrains it is necessary to know its contact properties. These properties can be known beforehand or extracted from contact forces. In this paper a practical evaluation using adaptive filtering techniques to extract the properties from different environmental conditions, with the intention to increase the adaptability of the robot to different environments is presented. We use the well-known linear spring-dashpot model and fit its parameters to four different materials in order to establish which of the reviewed methods (the recursive and windowed least squares algorithms) perform better to describe the material properties. The results show that the recursive least squares provide better tracking performance while the windowed least squares gives a smoother response.