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We report on an actuator based on dielectric elastomers that is capable of antagonistic actuation and passive folding. This actuator enables foldability in robots with simple structures. Unlike other antagonistic dielectric elastomer devices, our concept uses elastic hinges to allow the folding of the structure, which also provides an additional design parameter. To validate the actuator concept through a specific application test, a foldable elevon actuator with outline size of 70 mm × 130 mm is developed with angular displacement range and torque specifications matched to a 400-mm wingspan micro-air vehicle (MAV) of mass 130 g. A closed-form analytical model of the actuator is constructed, which was used to guide the actuator design. The actuator consists of 125-μm-thick silicone membranes as the dielectric elastomers, 0.2mm-thick fiberglass plate as the frame structure, and 50-μm-thick polyimide as the elastic hinge. We measured voltage-controllable angular displacement up to ±26° and torque of 2720 mN · mm at 5 kV, with good agreement between the model and the measured data. Two elevon actuators are integrated into the MAV, which was successfully flown, with the foldable actuators providing stable and well-controlled flight. The controllability was quantitatively evaluated by calculating the correlation between the control signal and the MAV motion, with a correlation in roll axis of over 0.7 measured during the flights, illustrating the high performance of this foldable actuator.
Posted on: September 16, 2014
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We demonstrate here a configuration of soft actuator which has several features such as, being completely soft, simple, thin, foldable, and stretchable while having uni/bidirectional bending actuation. Theoretically the actuation can be extended to multidirectional. We used Dielectric Elastomer Actuators (DEA) as a base actuation mechanism, and molded PDMS was used as a substrate of the device.
Posted on: September 5, 2014
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This paper presents the concept and an example of robject, a robotic entity embedded in an everyday object. Robjects use the affordance of the original object to ensure an efficient interaction and a high acceptance. The example of the ranger robot shows that this approach can be applied to the domestic environment. We explore the integration of a robot (robject) into a family household, by regarding the home as a ecosystem, which consists of people, parts, products, activities, and interactions. A test of the ranger robot in families validates this holistic approach and shows the impact of this type of design in respect to the complexity of the robotic system.
Posted on: September 3, 2014
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Analyses of experimental data acquired from humans and other vertebrates have suggested that motor commands may emerge from the combination of a limited set of modules. While many studies have focused on physiological aspects of this modularity, in this paper we propose an investigation of its theoretical foundations. We consider the problem of controlling a planar kinematic chain, and we restrict the admissible actuations to linear combinations of a small set of torque profiles (i.e., motor synergies). This scheme is equivalent to the time-varying synergy model, and it is formalized by means of the dynamic response decomposition (DRD). DRD is a general method to generate open-loop controllers for a dynamical system to solve desired tasks, and it can also be used to synthesize effective motor synergies. We show that a control architecture based on synergies can greatly reduce the dimensionality of the control problem, while keeping a good performance level. Our results suggest that in order to realize an effective and low-dimensional controller, synergies should embed features of both the desired tasks and the system dynamics. These characteristics can be achieved by defining synergies as solutions to a representative set of task instances. The required number of synergies increases with the complexity of the desired tasks. However, a possible strategy to keep the number of synergies low is to construct solutions to complex tasks by concatenating synergy-based actuations associated to simple point-to-point movements, with a limited loss of performance. Ultimately, this work supports the feasibility of controlling a non-linear dynamical systems by linear combinations of basic actuations, and illustrates the fundamental relationship between synergies, desired tasks and system dynamics.
Posted on: August 15, 2014
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Event-based dynamic vision sensors (DVSs) asynchronously report log intensity changes. Their high dynamic range, sub-ms latency and sparse output make them useful in applications such as robotics and real-time tracking. However they discard absolute intensity information which is useful for object recognition and classification. This paper presents a dynamic and active pixel vision sensor (DAVIS) which addresses this deficiency by outputting asynchronous DVS events and synchronous global shutter frames concurrently. The active pixel sensor (APS) circuits and the DVS circuits within a pixel share a single photodiode. Measurements from a 240×180 sensor array of 18.5um^2 pixels fabricated in a 0.18um 6M1P CMOS image sensor (CIS) technology show a dynamic range of 130dB with 11% contrast detection threshold, minimum 3us latency, and 3.5% contrast matching for the DVS pathway; and a 51dB dynamic range with 0.5% FPN for the APS readout.
Posted on: August 15, 2014
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Learning motion control as a unified process of designing the reference trajectory and the controller is one of the most challenging problems in robotics. The complexity of the problem prevents most of the existing optimization algorithms from giving satisfactory results. While model-based algorithms like iterative linear-quadratic-Gaussian (iLQG) can be used to design a suitable controller for the motion control, their performance is strongly limited by the model accuracy. An inaccurate model may lead to degraded performance of the controller on the physical system. Although using machine learning approaches to learn the motion control on real systems have been proven to be effective, their performance depends on good initialization. To address these issues, this paper introduces a two-step algorithm which combines the proven performance of a model-based controller with a model-free method for compensating for model inaccuracy. The first step optimizes the problem using iLQG. Then, in the second step this controller is used to initialize the policy for our PI$^2$-01 reinforcement learning algorithm. This algorithm is a derivation of the PI$^2$ algorithm enabling more stable and faster convergence. The performance of this method is demonstrated both in simulation and experimental results.
Posted on: August 15, 2014
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This paper proposes an optical motion sensor aimed towards small robotic platforms. It incorporates a 20×20 pixel continuous-time CMOS silicon retina vision sensor with pixels that have local gain control and adapt to background lighting and a DSP microcontroller which computes the global optical flow from the sampled sensor output. The system allows the user to validate various motion algorithms suitable for the platform. Measurements are presented that show that the system can compute global 2D translational motion from complex natural scenes using the image interpolation algorithm at a sample rate of 1 kHz and for speeds up to ±1000 pixels/s using <5k instruction cycles per frame.
Posted on: August 15, 2014
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This demonstration will show the features of the Dynamic and Active-Pixel Vision Sensor (DAVIS) reported at the VLSI Symposium and the International Imager Sensor Workshop in 2013. This sensor concurrently outputs conventional CMOS image sensor frames and sparse, low-latency dynamic vision sensor events from the same pixels, sharing the same photodiodes. The setup will allow visitors to explore the advantages of combining of fast and computationally-efficient neuromorphic event-driven vision with the existing body of methods for frame-based computer and machine vision.
Posted on: August 15, 2014
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Neuromorphic spike event-based dynamic vision sensors (DVS) offer the possibility of fast, computationally efficient visual processing for navigation in mobile robotics. To extract motion parallax cues relating to 3D scene structure, the uninformative camera rotation must be removed from the visual input to allow the un-blurred features and informative relative optical flow to be analyzed. Here we describe the integration of an inertial measurement unit (IMU) with a 240×180 pixel DVS. The algorithm for electronic stabilization of the visual input against camera rotation is described. Examples are presented showing the stabilization performance of the system.
Posted on: August 15, 2014
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Dynamic and active pixel vision sensors (DAVISs) are a new type of sensor that combine a frame-based intensity readout with an event-based temporal contrast readout. This paper demonstrates that these sensors inherently perform high-speed, video compression in each pixel by describing the first decompression algorithm for this data. The algorithm performs an online optimization of the event decoding in real time. Example scenes were recorded by the 240×180 pixel sensor at sub-Hz frame rates and successfully decompressed yielding an equivalent frame rate of 2kHz. A quantitative analysis of the compression quality resulted in an average pixel error of 0.5DN intensity resolution for non-saturating stimuli. The system exhibits an adaptive compression ratio which depends on the activity in a scene; for stationary scenes it can go up to 1862. The low data rate and power consumption of the proposed video compression system make it suitable for distributed sensor networks.
Posted on: August 15, 2014
