One-dimensional array of ion chains coupled to an optical cavity

We present a novel hybrid system where an optical cavity is integrated with a microfabricated planar-electrode ion trap. The trap electrodes produce a tunable periodic potential allowing the trapping of up to 50 separate ion chains spaced by 160 $\mu$m along the cavity axis. Each chain can contain up to 20 individually addressable Yb\textsuperscript{+} ions coupled to the cavity mode. We demonstrate deterministic distribution of ions between the sites of the electrostatic periodic potential and control of the ion-cavity coupling. The measured strength of this coupling should allow access to the strong collective coupling regime with $\lesssim$10 ions. The optical cavity could serve as a quantum information bus between ions or be used to generate a strong wavelength-scale periodic optical potential.Comment: 15 pages, 6 figures, submitted to New Journal of Physic

One-dimensional array of ion chains coupled to an optical cavity

We present a novel system where an optical cavity is integrated with a microfabricated planar-electrode ion trap. The trap electrodes produce a tunable periodic potential allowing the trapping of up to 50 separate ion chains aligned with the cavity and spaced by 160 μm in a one-dimensional array along the cavity axis. Each chain can contain up to 20 individually addressable Yb+ ions coupled to the cavity mode. We demonstrate deterministic distribution of ions between the sites of the electrostatic periodic potential and control of the ion–cavity coupling. The measured strength of this coupling should allow access to the strong collective coupling regime with lesssim10 ions. The optical cavity could serve as a quantum information bus between ions or be used to generate a strong wavelength-scale periodic optical potential.United States. Army Research OfficeNational Science Foundation (U.S.)National Science Foundation (U.S.). Graduate Research Fellowship Program (0645960)National Science Foundation (U.S.) (Interdisciplinary Quantum Information Science and Engineering (iQuISE) Program 0801525

A new biarticular actuator design facilitates control of leg function in BioBiped3

Bioinspired legged locomotion comprises different aspects, such as (i) benefiting from reduced complexity control approaches as observed in humans/animals, (ii) combining embodiment with the controllers and (iii) reflecting neural control mechanisms. One of the most important lessons learned from nature is the significant role of compliance in simplifying control, enhancing energy efficiency and robustness against perturbations for legged locomotion. In this research, we investigate how body morphology in combination with actuator design may facilitate motor control of leg function. Inspired by the human leg muscular system, we show that biarticular muscles have a key role in balancing the upper body, joint coordination and swing leg control. Appropriate adjustment of biarticular spring rest length and stiffness can simplify the control and also reduce energy consumption. In order to test these findings, the BioBiped3 robot was developed as a new version of BioBiped series of biologically inspired, compliant musculoskeletal robots. In this robot, three-segmented legs actuated by mono- and biarticular series elastic actuators mimic the nine major human leg muscle groups. With the new biarticular actuators in BioBiped3, novel simplified control concepts for postural balance and for joint coordination in rebounding movements (drop jumps) were demonstrated and approved

On the Design and Development of Musculoskeletal Bipedal Robots

Bio-inspired musculoskeletal bipedal robots with tendon driven series elastic actuation including biarticular structures have the potential to outperform rigidly actuated robots. But the design, the control and the tuning of these bio-inspired robots are more complex than for their rigidly actuated counterparts. In this thesis new approaches to solving the problems arising from the bio-inspired design of robots are proposed and evaluated using a prototype series of musculoskeletal bipedal robots developed in the BioBiped project. This includes a systematic approach to tuning of hardware and software parameters in a hardware-in-the-loop optimization process with increased efficiency through the use of expert knowledge

Efficient design parameter optimization for musculoskeletal bipedal robots combining simulated and hardware-in-the-loop experiments

The design and tuning of bio-inspired musculoskeletal bipedal robots with tendon driven series elastic actuation (TD-SEA) including biarticular structures is more complex than for conventional rigid bipedal robots. To achieve a desired dynamic motion goal additional hardware parameters (spring coefficients, rest lengths, lever arms) of both, the TDSEAs and the biarticular structures, need to be adjusted. Furthermore, the biarticular structures add correlations over multiple joints which increase the complexity of tuning of these parameters. Parameter adaption and tuning is needed to fit active and passive dynamics of the actuators and the control system. For the considered class of musculoskeletal bipedal robots no fully satisfying systematic approach to efficiently tune all of these parameters has been demonstrated yet. Conventional approaches for tuning of hardware parameters in rigid robots are either simulation based or use a hardware-in-the-loop optimization. This paper presents a new approach to efficiently optimize these parameters, by combining the advantages of simulation-in-the-loop and hardware-in-the-loop optimizations. Grahical interpretation of suitable metrics, like resulting quality values, are used to interpret the simulation results in order to efficiently guide the hardware experiments. By carefully considering the simulation results and adjusting the sequence of robot experiments based on biomechanical insights, the required number of hardware experiments can be significantly reduced. This approach is applied to the musculoskeletal BioBiped2 robot where the hardware parameters of the elastic actuation of the Gastrocnemius and Soleus structures are optimized. A comparison with a state-of-the-art hardware-in-the-loop optimization method demonstrates the efficiency of the presented approach

Fast, Robust and Versatile Humanoid Robot Locomotion with Minimal Sensor Input

The generation of fast and robust locomotion is one of the crucial problems to be solved for a competitive autonomous humanoid soccer robot. During the last decades many different approaches to solve this problem have been investigated. In this paper a simplified yet powerful approach for generation of locomotion for an autonomous humanoid robot is described. It is based on an open loop trajectory generation with an overlying gyroscope-based closed loop postural stabilization. Unlike other widely used approaches in humanoid robotics the trajectory generation is completely decoupled from the stabilization algorithm, thus simplifying design, implementation and testing of either algorithm. The only sensor required for postural stabilization is a two axis gyroscope in the robot"s hip. No further sensors like foot-ground contact or force sensors, which are typically applied in many other approaches, are required. Nevertheless the presented approach exhibits remarkable performance. Furthermore this approach can be implemented easily in many available robots without complex modifications of the hardware. Experimental results for various types of locomotion are presented for two different robots used in the 2009 RoboCup Humanoid KidSize competition

Bio-inspired motion control of the musculoskeletal BioBiped1 robot based on a learned inverse dynamics model

Based on the central hypothesis that a humanoid robot with human-like walking and running performance requires a bio-inspired embodiment of the musculoskeletal functions of the human leg as well as of its control structure, a bio-inspired approach for joint position control of the BioBiped1 robot is presented in this paper. This approach combines feedforward and feedback control running at 1 kHz and 40 Hz, respectively. The feed-forward control is based on an inverse dynamics model which is learned using Gaussian process regression to account for the robot’s body dynamics and external influences. For evaluation the learned model is used to control the robot purely feed-forward as well as in combination with a slow feedback controller. Both approaches are compared to a basic feedback PD-controller with respect to their tracking ability in experiments. It is shown, that the combined approach yields good results and outperforms the basic feedback controller when applied to the same set-point trajectories for the leg joints

Sophisticated Offline Analysis of Teams of Autonomous Mobile Robots

Debugging control software for an autonomous mobile robot is a dif?cult and time consuming task. But it gets even harder, when analyzing a whole team of robots and their team behavior. The quality of the robots" decisions based on their current knowledge cannot be judged anymore by merely looking at their actions from the outside. In this paper an approach for collecting intrinsic and extrinsic data of a team of robots during full operation and analyzing this data of?ine is described. Since the amount of data to be collected is quite large a method for automated and semi-automated analysis is shown - making it possible to detect known problems in an automated process and mark potentially interesting events for manual review. Furthermore a solution to reuse existing single robot debugging tools on teams of robots, without rewriting each tool, is presented