13,652 research outputs found
Toward Shape Optimization of Soft Robots
International audienceIn this paper we present our work on shape optimization for soft robotics where the shape is optimized for a given soft robot usage. To obtain a parametric optimization with a reduced number of parameters, we rely on an approach where the designer progressively refines the parameter space and the fitness function until a satisfactory design is obtained. In our approach, we automatically generate FEM simulations of the soft robot and its environment to evaluate a fitness function while checking the consistency of the solution. Finally, we have coupled our framework to an evolutionary optimization algorithm, and demonstrated its use for optimizing the design of a deformable leg of a locomotive robot
Safety experiments for small robots investigating the potential of soft materials in mitigating the harm to the head due to impacts
There is a growing interest in social robots to be considered in the therapy
of children with autism due to their effectiveness in improving the outcomes.
However, children on the spectrum exhibit challenging behaviors that need to be
considered when designing robots for them. A child could involuntarily throw a
small social robot during meltdown and that could hit another person's head and
cause harm (e.g. concussion). In this paper, the application of soft materials
is investigated for its potential in attenuating head's linear acceleration
upon impact. The thickness and storage modulus of three different soft
materials were considered as the control factors while the noise factor was the
impact velocity. The design of experiments was based on Taguchi method. A total
of 27 experiments were conducted on a developed dummy head setup that reports
the linear acceleration of the head. ANOVA tests were performed to analyze the
data. The findings showed that the control factors are not statistically
significant in attenuating the response. The optimal values of the control
factors were identified using the signal-to-noise (S/N) ratio optimization
technique. Confirmation runs at the optimal parameters (i.e. thickness of 3 mm
and 5 mm) showed a better response as compared to other conditions. Designers
of social robots should consider the application of soft materials to their
designs as it help in reducing the potential harm to the head
Morphological properties of mass-spring networks for optimal locomotion learning
Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size
Gait learning for soft microrobots controlled by light fields
Soft microrobots based on photoresponsive materials and controlled by light
fields can generate a variety of different gaits. This inherent flexibility can
be exploited to maximize their locomotion performance in a given environment
and used to adapt them to changing conditions. Albeit, because of the lack of
accurate locomotion models, and given the intrinsic variability among
microrobots, analytical control design is not possible. Common data-driven
approaches, on the other hand, require running prohibitive numbers of
experiments and lead to very sample-specific results. Here we propose a
probabilistic learning approach for light-controlled soft microrobots based on
Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach
results in a learning scheme that is data-efficient, enabling gait optimization
with a limited experimental budget, and robust against differences among
microrobot samples. These features are obtained by designing the learning
scheme through the comparison of different GP priors and BO settings on a
semi-synthetic data set. The developed learning scheme is validated in
microrobot experiments, resulting in a 115% improvement in a microrobot's
locomotion performance with an experimental budget of only 20 tests. These
encouraging results lead the way toward self-adaptive microrobotic systems
based on light-controlled soft microrobots and probabilistic learning control.Comment: 8 pages, 7 figures, to appear in the proceedings of the IEEE/RSJ
International Conference on Intelligent Robots and Systems 201
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