In electrically actuated robots most energy losses are due to the heating of the actuators. This energy loss can be greatly reduced with parallel elastic actuators, by optimizing the elastic element such that it delivers most of the required torques. Previously used optimization methods relied on parameterizing the spring characteristic, thereby limiting the set of spring characteristics optimized over and with that the loss reduction that can be obtained. This letter shows that such parametrization is not necessary; a method is presented to compute the optimal characteristic as an analytic function of the trajectory. The efficacy of this method is demonstrated using two examples. The first example considers the optimal spring characteristic for a parallel elastic actuator supporting the human ankle during walking. The second example applies the method in combination with trajectory optimization on a single degree of freedom robot performing a specific pick-and-place task. The task at hand has a height difference between the pick and the place location. With the analytical optimal spring, it is shown that the robot can recover enough of the energy released by the package to function without external electric energy supply.

The main task of robotic grippers, holding an object, does not require work theoretically. Yet grippers consume significant amounts of energy in practice. This paper presents an approach for designing an energy-saving drive for robotic grippers employing a Statically Balanced Force Amplifier (SBFA) and a Non-backdrivable mechanism (NBDM). A novel metric (Grip Performance Metric) to systematically evaluate drives regarding their energy consumption, is used in the design phase; afterwards, the realization and testing of a prototype (REED, Robotic Energy-Efficient Drive) are presented. Results show that the actuation force can be reduced by 92%, resulting in energy-savings of 86% for an example task. This shows the potential of drives based on SBFAs and NBDMs to achieve energy-neutral grippers.

From which states and with what controls can a biped avoid falling or reach a given target state? What is the most robust way to do these? So as to help with the design of walking robot controllers, and perhaps give insights into human walking, we address these questions using two simple 2-D models: the inverted pendulum (IP) and linear inverted pendulum (LIP). Each has one state variable at mid-stance, i.e., hip velocity, and two state-dependent controls at each step, i.e., push-off magnitude and step length (IP) and step time and length (LIP). Using practical targets and constraints, we compute all combinations of initial states and control actions for the next step, such that the robot can, with the best possible future controls, avoid falling for <formula><tex>$n$</tex></formula> steps or reach a target within <formula><tex>$n$</tex></formula> steps. All such combinations constitute regions in the combined space of states and controls. Farther from the boundaries of these regions, the robot tolerates larger errors and disturbances. Furthermore, for these models, and thus possibly real bipeds, usually if it is possible to avoid falling, it is possible to reach the target, and if it is possible to reach the target, it is possible to do so in two steps.

Robots would perform better when their mechanical structure is specifically designed for their designated task, for instance by adding spring mechanisms. However, designing such mechanisms, which match the dynamics of the robot with the task, is hard and time consuming. To assist designers, a platform that automatically designs dynamical mechanisms is needed. This letter introduces a novel string-based representation for mechanisms, including evolutionary operators, that allows an evolutionary algorithm to automatically design dynamical mechanisms for a designated task. The mechanism representation allows simultaneous optimization of topology and parameters. Simulation experiments investigate various algorithms to obtain best optimization performance. We show the efficacy of the representation, operators, and evolutionary algorithm by designing mechanisms that track straight lines and ellipses by virtue of both their kinematic and dynamic properties.