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.

@en

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.

@en

Sampling-based kinodynamic planners, such as Rapidly-exploring Random Trees (RRTs), pose two fundamental challenges: computing a reliable (pseudo-)metric for the distance between two randomly sampled nodes, and computing a steering input to connect the nodes. The core of these challenges is a Two Point Boundary Value Problem, which is known to be NP-hard. Recently, the distance metric has been approximated using supervised learning, reducing computation time drastically. The previous work on such learning RRTs use direct optimal control to generate the data for supervised learning. This paper proposes to use indirect optimal control instead, because it provides two benefits: it reduces the computational effort to generate the data, and it provides a low dimensional parametrization of the action space. The latter allows us to learn both the distance metric and the steering input to connect two nodes. This eliminates the need for a local planner in learning RRTs. Experimental results on a pendulum swing up show 10-fold speed-up in both the offline data generation and the online planning time, leading to at least a 10-fold speed-up in the overall planning time.

@en

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.

@en

While progress in many fields of robotics has been swift, robot arm movement in scenarios without contact has changed little in the last decades. This lack of change is not due a lack of potential for improvement. After all, the human arms that these robot emulate move in ways that are more robust, energy efficient and adaptable. This thesis is inspired by human movement skill in these three aspects to improve the movement of robotic arms in non-contact situations. The six main contributions of this thesis are divided over those three aspects. The aspects are studied for two motions, the reaching motion, that is, move from the initial position to a pre-specified target position and back, and the pick-and-place motion, which adds picking and placing an object at the initial and target positions respectively. The first aspect, robustness, is studied from a stability standpoint. The first aspect is the topic of the first part of this thesis, which contains four of its six main contributions. Stability, as understood in this thesis, is the property of returning to a fixed (desired) motion after an initial disturbance two motions. This form of stability is a minimal requirement for successful task completion. Most current robot arms rely on fast sensory feedback for their stability. This contrasts with humans, who rely on skill at choosing motions that are intrinsically stable. Such self-stable motions have been used by earlier robotics researchers to make robots that juggle or walk without the need for sensory feedback. However, these robots perform tasks involving impacts, which can have a large stabilizing effect. No such impacts are available in reaching motions. A self-stabilizing reaching motion instead depends on ingenious use potential energy and centrifugal or Coriolis effects.@en

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.@en

This paper identifies the class of actuators called clutched elastic actuators (CEAs). CEAs use clutches to control the energy flow into springs. CEAs in exoskeletons, prostheses, legged robots, and robotic arms have shown the ability to reduce the energy consumption and motor requirements, such as peak torque and peak power. Because of those abilities, they are increasingly used in robotics. In this paper, we categorize existing CEA designs, identify trends in those designs, and provide a method to analyze their functionality. Based on a literature survey, current CEA designs are placed in nine categories, depending on their morphology. The main trend is that CEA designs are becoming more complex, meaning that the number of clutches and springs increases. We show with the introduced mathematical analysis that the functionality can be analyzed with a constraint matrix, a stiffness matrix, and multiplication of a clutch-dependent diagonal matrix with an oriented incidence matrix. This method eases the analysis of the functionality of CEAs. Furthermore, it can lead to new CEA designs in which the number of resulting stiffnesses grows exponentially with the number of springs and clutches.@en