This paper introduces the mechanical design and the control concept of the Series Compliant Articulated Robotic Leg ScarlETH which was developed at ETH Zurich for fast, efficient, and versatile locomotion. Inspired by biological systems, we seek to achieve this through large compliances in the joints which enable natural dynamics, allow temporary energy storage, and improve the passive adaptability. A sophisticated chain and cable pulley design minimizes the segment masses, places the overall CoG close to the hip joint, and maximizes the range of motion. Nonlinearities in the damping and an appropriate low-level controller allow for precise torque control during stance and for fast task space position control during swing. This paved the road for the combined application of a virtual model controller for ground contact and a modified Raibert style controller for flight phase which was successfully tested in planar running. © 2011 IEEE.
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To restore walking after transfemoral amputation, various actuated exoprostheses have been developed, which control the knee torque actively or via variable damping. In both cases, an important issue is to find the appropriate control that enables user-dominated gait. Recently, we suggested a generic method to deduce intended motion of impaired or amputated limbs from residual human body motion. Based on interjoint coordination in physiological gait, statistical regression is used to estimate missing motion. In a pilot study, this complementary limb motion estimation (CLME) strategy is applied to control an active knee exoprosthesis. A motor-driven prosthetic knee with one degree of freedom has been realized, and one above-knee amputee has used it with CLME. Performed tasks are walking on a treadmill and alternating stair ascent and descent. The subject was able to walk on the treadmill at varying speeds, but needed assistance with the stairs, especially to descend. The promising results with CLME are compared with the subject’s performance with her own prosthesis, the C-Leg from Otto Bock.
Background: The overall goal of this paper was to investigate approaches to controlling active participation in stroke patients during robot-assisted gait therapy. Although active physical participation during gait rehabilitation after stroke was shown to improve therapy outcome, some patients can behave passively during rehabilitation, not maximally benefiting from the gait training. Up to now, there has not been an effective method for forcing patient activity to the desired level that would most benefit stroke patients with a broad variety of cognitive and biomechanical impairments. Methods. Patient activity was quantified in two ways: by heart rate (HR), a physiological parameter that reflected physical effort during body weight supported treadmill training, and by a weighted sum of the interaction torques (WIT) between robot and patient, recorded from hip and knee joints of both legs. We recorded data in three experiments, each with five stroke patients, and controlled HR and WIT to a desired temporal profile. Depending on the patient’s cognitive capabilities, two different approaches were taken: either by allowing voluntary patient effort via visual instructions or by forcing the patient to vary physical effort by adapting the treadmill speed. Results: We successfully controlled patient activity quantified by WIT and by HR to a desired level. The setup was thereby individually adaptable to the specific cognitive and biomechanical needs of each patient. Conclusion: Based on the three successful approaches to controlling patient participation, we propose a metric which enables clinicians to select the best strategy for each patient, according to the patient’s physical and cognitive capabilities. Our framework will enable therapists to challenge the patient to more activity by automatically controlling the patient effort to a desired level. We expect that the increase in activity will lead to improved rehabilitation outcome. © 2011 Koenig et al; licensee BioMed Central Ltd.
Robots have often been used as an educational tool in class to introduce kids to science and technology, disciplines that are affected by decreasing enrollments in universities. Consequently, many robotic kits are available off-the-shelf. Even though many of these platforms are easy to use, they focus on a classical top-down engineering approach. Additionally, they often require advanced programming skills. In this paper we introduce an open robotic kit for education (EmbedIT) which currently is under development. Unlike common robot kits EmbedIT enables students to access the technical world in a non-engineering focused way. Through a graphical user interface students can easily build and control robots. We believe that once fascination and a basic understanding of technology has been established, the barrier to learn more advanced topics such as programming and electronics is lowered. Further we describe the hardware and software of EmbedIT, the current state of implementation, and possible applications. © 2011 Springer-Verlag.
Research made over the past decade shows the use of increasingly complex methods and heavy platforms to achieve autonomous flight in cluttered environments. However, efficient behaviors can be found in nature where limited sensing is used, such as in insects progressing toward a light at night. Interestingly, their success is based on their ability to recover from the numerous collisions happening along their imperfect flight path. The goal of the AirBurr project is to take inspiration from these insects and develop a new class of flying robots that can recover from collisions and even exploit them. Such robots are designed to be robust to crashes and can take-off again without human intervention. They navigate in a reactive way and, unlike conventional approaches, they don’t need heavy modelling in order to fly autonomously. We believe that this new paradigm will bring flying robots out of the laboratory environment and allow them to tackle unstructured, cluttered environments. This paper aims at presenting the vision of the AirBurr project, as well as the latest results in the design of a platform capable of sustaining collisions and self-recovering after crashes.
Flying robots have unique advantages in the exploration of cluttered environments such as caves or collapsed buildings. Current systems however have difficulty in dealing with the large amount of obstacles inherent to such environments. Collisions with obstacles generally result in crashes from which the platform can no longer recover. This paper presents a method for designing active uprighting mechanisms for protected rotorcraft-type flying robots that allow them to upright and subsequently take off again after an otherwise mission-ending collision. This method is demonstrated on a tailsitter flying robot which is capable of consistently uprighting after falling on its side using a spring-based ’leg’ and returning to the air to continue its mission.
Our brain-actuated wheelchair uses shared control to couple the user input with the contextual information about the surroundings in order to perform natural manoeuvres both safely and efficiently. In this study, we investigate the feasibility of using our brain–controlled wheelchair with patients in a rehabilitation clinic. Both user and system performance metrics are analysed. We find that the driving performance of a motor-disabled patient at the clinic is comparable with the performance of four healthy subjects. All five participants were able to complete the driving task successfully.
Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Yet, non-line-of-sight (NLOS) positioning scenarios can create large biases in the time-difference-of-arrival (TDOA) measurements, and must be addressed with accurate measurement models in order to avoid significant localization errors. In this work, we first develop an efficient, closed-form TDOA error model and analyze its estimation characteristics by calculating the Cramer-Rao lower bound (CRLB). We subsequently detail how an online Expectation Maximization (EM) algorithm is adopted to find an elegant formalism for the maximum likelihood estimate of the model parameters. We perform real experiments on a mobile robot equipped with an UWB emitter, and show that the online estimation algorithm leads to excellent localization performance due to its ability to adapt to the varying NLOS path conditions over time.
Large numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particle-filter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithm’s overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots.