Swimming microrobots have the potential to be used in medical applications such as targeted drug delivery. The challenges for navigating microrobots in the human body lie not only in the viscosity of body fluids but also in the existence of different types of fibers and cells such as blood cells or protein strands. This paper investigates artificial bacterial flagella (ABFs), which are helical microrobots actuated by an external magnetic field, in methyl cellulose solutions of different concentrations. It can be shown that the microrobots can be propelled in these gel-like heterogeneous solutions and successful swimming was demonstrated in solutions with a viscosity of more than 20 times that of water. Furthermore, results indicate that the existence of fibers can help ABFs swim more effectively, which agrees with previous experimental results reported for natural bacteria.
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We describe a process for enabling quadrocopters to perform and improve upon aerobatic maneuvers. We describe such maneuvers as a set of desired keyframes and a parametrized input trajectory. The full state trajectory of the vehicle is left unspecified – only predefined partial-state keyframes are used to measure errors and to refine the primitive. A first-principles model is used to find nominal trajectory parameter values and a first-order correction matrix. We apply this method to extending previous work on vertical-plane 2D adaptive flips to a fully 3D adaptive maneuver. We also show how this method can be applied to finding trajectories for flips with matching non-zero initial and final velocities. Preliminary results are presented from simulation and from quadrocopters in the ETH Flying Machine Arena.
We describe a simple and intuitive policy gradient method for improving parametrized quadrocopter multi-flips by combining iterative experiments with information from a first-principles model. We start by formulating an N-flip maneuver as a five-step primitive with five adjustable parameters. Optimization using a low-order first-principles 2D vertical plane model of the quadrocopter yields an initial set of parameters and a corrective matrix. The maneuver is then repeatedly performed with the vehicle. At each iteration the state error at the end of the primitive is used to update the maneuver parameters via a gradient adjustment. The method is demonstrated at the ETH Zurich Flying Machine Arena testbed on quadrotor helicopters performing and improving on flips, double flips and triple flips.
In this paper we propose a method to modulate the level of assistance provided by a shared controller, not only given the environmental context, but also according to the context of the user’s current behaviour. We show that the enhanced situational context can be adequately captured by using online performance metrics (such as those more usually found in the evaluation of shared control systems). The resultant controller not only allows the user to perform better in the primary task (like many shared control systems), but has also has increased the level of user acceptance, due to the personalised dynamics of the control policy.
In this literature review we explain anthropomorphism and its role in the design of socially interactive robots and human-robot interaction. We illus-trate the social phenomenon of anthropomorphism which describes people’s tendency to attribute lifelike qualities to objects and other non lifelike artifacts. We present theoretical backgrounds from social sciences, and integrate related work from robotics research, including results from experiments with social ro-bots. We present different approaches for anthropomorphic and humanlike form in a robot’s design related to its physical shape, its behavior, and its interaction with humans. This review provides a comprehensive understanding of anthro-pomorphism in robotics, collects and reports relevant references, and gives an outlook on anthropomorphic human-robot interaction.
- Detailed record: https://infoscience.epfl.ch/record/180117?ln=en
Providing adaptive shared control for Brain- Computer Interfaces (BCIs) can result in better performance while reducing the user’s mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This study aims at real-time differentiation between fast and slow trials in a motor imagery BCI. In our experiments, we refer to trials shorter than the median of trial lengths as “fast” trials and to those longer than the median as “slow” trials. We propose a classifier for real-time distinction between fast and slow trials based on estimates of the entropy rates for the first 2-3 s of the electroencephalogram (EEG). Results suggest that it can be predicted whether a trial is slow or fast well before a cutoff time. This is important for adaptive shared control especially because 55% to 75% of trials (for the five subjects in this study) are longer than that cutoff time
We present the design and evaluation of an iPad application that will be used to operate the modular robots “Roombots”. Roombots are the building blocks for adaptive pieces of furniture. The application allows a user to arrange adaptive furniture within a room. We conducted a user study with 24 participants to evaluate our approach and to freely explore people’s interaction. Data suggests that the ability to move with the device leads to a better precision of the furniture arrangement. No significant difference has been observed between using the application through a virtual representation of the room in contrast to an augmented reality environment, even if participants mentioned in a post-study questionnaire their preference for the augmented condition. Users described the interface as intuitive and easy to use.
Recent results in spinal research are challenging the historical view that the spinal reflexes are mostly hardwired and fixed behaviours. In previous work we have shown that three of the simplest spinal reflexes could be self-organised in an agonist-antagonist pair of muscles. The simplicity of these reflexes is given from the fact that they entail at most one interneuron mediating the connectivity between afferent inputs and efferent outputs. These reflexes are: the Myotatic, the Reciprocal Inibition and the Reverse Myotatic reflexes. In this paper we apply our framework to a simulated 2D leg model actuated by six muscles (mono- and bi-articular). Our results show that the framework is successful in learning most of the spinal reflex circuitry as well as the corresponding behaviour in the more complicated muscle arrangement.
There has been an increasing interest in the use of unconventional materials and morphologies in robotic systems because the underlying mechanical properties (such as body shapes, elasticity, viscosity, softness, density and stickiness) are crucial research topics for our in-depth understanding of embodied intelligence. The detailed investigations of physical system-environment interactions are particularly important for systematic development of technologies and theories of emergent adaptive behaviors. Based on the presentations and discussion in the Future Emerging Technology (fet11) conference, this article introduces the recent technological development in the field of soft robotics, and speculates about the implications and challenges in the robotics and embodied intelligence research. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V.