Animals can incorporate large numbers of actuators because of the characteristics of muscles; whereas, robots cannot, as typical motors tend to be large, heavy, and inefficient. However, shape-memory alloys (SMA), materials that contract during heating because of change in their crystal structure, provide another option. SMA, though, is unidirectional and therefore requires an additional force to reset (extend) the actuator, which is typically provided by springs or antagonistic actuation. These strategies, however, tend to limit the actuator's work output and functionality as their force-displacement relationships typically produce increasing resistive force with limited variability. In contrast, magnetic springs-composed of permanent magnets, where the interaction force between magnets mimics a spring force-have much more variable force-displacement relationships and scale well with SMA. However, as of yet, no method for designing magnetic springs for SMA-actuators has been demonstrated. Therefore, in this paper, we present a new methodology to tailor magnetic springs to the characteristics of these actuators, with experimental results both for the device and robot-integrated SMA-actuators. We found magnetic building blocks, based on sets of permanent magnets, which are well-suited to SMAs and have the potential to incorporate features such as holding force, state transitioning, friction minimization, auto-alignment, and self-mounting. We show magnetic springs that vary by more than 3 N in 750 $\mu$m and two SMA-actuated devices that allow the MultiMo-Bat to reach heights of up to 4.5 m without, and 3.6 m with, integrated gliding airfoils. Our results demonstrate the potential of this methodology to add previously impossible functionality to smart material actuators. We anticipate this methodology will inspire broader consideration of the use of magnetic springs in miniature robots and further study of the potential of tailored magnetic springs throughout mechanical systems.

Many robots make use of gravitational potential energy, generated by another mode, to enhance mobility through gliding locomotion. However, unstructured environments can create situations in which the initial conditions for steady-state gliding cannot be achieved; for example, jumping out of a hole, where the obstacle is very close to the robot. This paper suggests an optimization methodology for finding airfoil configurations and control strategies to maximize the effective non-steady-state gliding ratio for the most challenging initial condition, that of zero velocity. Parameters for the optimization are a location of a robot's center-of-mass in relation to its center-of-pressure and, through the addition of a tail, an active pitch control strategy. The optimal center-of-mass location produces the best passive gliding performance (morphological intelligence), and the optimal control strategy improves the gliding distance. Due to the aerodynamic complexities of modeling the collapsible airfoils, we find the optimal location of the center-of-mass from gliding experiments performed on the robot at different center-of-mass locations and initial pitch angles. An optimal location of the center-of-mass was found to be 40% of the wing chord for our robotic platform; measured from the wing's leading edge. The optimal location has a wide range of initial pitch angles which result in stable, yet non-steady-state, gliding behaviors. The morphological intelligence built into our robotic platform creates two observable dynamic behaviors, that of horizontal velocity gain and sink rate minimization. We then estimate the drag coefficients from the experiments, and conduct dynamic simulations to optimize the pitch control strategy. The design methodology presented here can enhance the non-steady-state gliding performance of a broad range of gliding robots, and the control strategy can further enhance performance on those which utilize an active tail.

Focusing on the physical interaction between people and machines within safety constraints in versatile situations, this paper proposes a new, efficient actuation approach, continuous-state coupled elastic actuation (CCEA), to provide oncoming human-machine systems with an intrinsic programmable stiffness capacity to shape output force corresponding to the deviation between human motions and set positions of the system. As one of all the possible CCEA systems, a prototype of a 2-DOF coupled elastic actuator is designed to provide a compromise between performance and safety. Using a pair of antagonistic four-bar linkages, the inherent stiffness of the system can be adjusted dynamically. Compared to the state-of-the-art variable stiffness actuators, the CCEA system is unique in that it can achieve near zero mechanical stiffness in an efficient way. In addition, a human-robot interaction model is built to investigate the controlled bandwidth and safety of the CCEA system. For the application of assistive exercises, this study also proposes two kinds of controls for assistive exercises. Finally, a CCEA exoskeleton is built for elbow rehabilitation. Both simulations and experiments are conducted to show some desired properties of the proposed CCEA system.