Torque feedback control and series elastic actuators are widely used to enable compact, highly-geared electric motors to provide low and controllable mechanical impedance. While these approaches provide certain benefits for control, their impact on system energy consumption is not widely understood. This paper presents a model for examining the energy consumption of drivetrains implementing various target dynamic behaviors in the presence of gear reductions and torque feedback. Analysis of this model reveals that under cyclical motions for many conditions, increasing the gear ratio results in greater energy loss. A similar model is presented for series elastic actuators and used to determine the energy consequences of various spring stiffness values. Both models enable the computation and optimization of power based on specific hardware manifestations, and illustrate how energy consumption sometimes defies conventional best-practices. Results of evaluating these two topologies as part of a drivetrain design optimization for two energy-efficient electrically driven humanoids are summarized. The model presented enables robot designers to predict the energy consequences of gearing and series elasticity for future robot designs, helping to avoid substantial energy sinks that may be inadvertently introduced if these issues are not properly analyzed.
A three year LDRD was undertaken to look at the feasibility of using magnetic sensing to determine flows within sealed vessels at high temperatures and pressures. Uniqueness proofs were developed for tracking of single magnetic particles with multiple sensors. Experiments were shown to be able to track up to 3 dipole particles undergoing rigid-body rotational motion. Temperature was wirelessly monitored using magnetic particles in static and predictable motions. Finally high-speed vibrational motion was tracked using magnetic particles. Ideas for future work include using small particles for measuring vorticity and better calibration methods for tracking multiple particles.
Drilling is a repetitive, dangerous and costly process and a strong candidate for automation. We describe a method for autonomously controlling a rotary drilling process as it transitions through multiple materials with very different dynamics. This approach classifies the drilling medium based on real-time measurements and comparison to prior drilling data, and can identify the material type, drilling region, and approximately optimal set-point based on data from as few as one operating condition. The controller uses these set-points as initial conditions, and then conducts an optimal search to maximize performance, e.g. by minimizing mechanical specific energy. The control architecture is described, and the material estimation process is detailed. The results of experiments that implement autonomous drilling through a layered concrete and granite sample are discussed.
Drilling is a repetitive, dangerous and costly process and a strong candidate for automation. We describe a method for autonomously controlling a rotary drilling process as it transitions through multiple materials with very different dynamics. This approach classifies the drilling medium based on real-time measurements and comparison to prior drilling data, and can identify the material type, drilling region, and approximately optimal set-point based on data from as few as one operating condition. The controller uses these set-points as initial conditions, and then conducts an optimal search to maximize performance, e.g. by minimizing mechanical specific energy. The control architecture is described, and the material estimation process is detailed. The results of experiments that implement autonomous drilling through a layered concrete and granite sample are discussed.
This paper describes how parallel elastic elements can be used to reduce energy consumption in the electric-motor-driven, fully actuated, Sandia Transmission-Efficient Prototype Promoting Research (STEPPR) bipedal walking robot without compromising or significantly limiting locomotive behaviors. A physically motivated approach is used to illustrate how selectively engaging springs for hip adduction and ankle flexion predict benefits for three different flat-ground walking gaits: human walking, human-like robot walking, and crouched robot walking. Based on locomotion data, springs are designed and substantial reductions in power consumption are demonstrated using a bench dynamometer. These lessons are then applied to STEPPR, a fully actuated bipedal robot designed to explore the impact of tailored joint mechanisms on walking efficiency. Featuring high-Torque brushless DC motors, efficient low-ratio transmissions, and high-fidelity torque control, STEPPR provides the ability to incorporate novel joint-level mechanisms without dramatically altering high-level control. Unique parallel elastic designs are incorporated into STEPPR, and walking data show that hip adduction and ankle flexion springs significantly reduce the required actuator energy at those joints for several gaits. These results suggest that parallel joint springs offer a promising means of supporting quasi-static joint torques due to body mass during walking, relieving motors of the need to support these torques and substantially improving locomotive energy efficiency.
This paper describes the design and performance of a synthetic rope on sheave drive system. This system uses synthetic ropes instead of steel cables to achieve low weight and a compact form factor. We demonstrate how this system is capable of 28-Hz torque control bandwidth, 95% efficiency, and quiet operation, making it ideal for use on legged robots and other dynamic physically interactive systems. Component geometry and tailored maintenance procedures are used to achieve high endurance. Endurance tests based on walking data predict that the ropes will survive roughly 247,000 cycles when used on large (90 kg), fully actuated bipedal robot systems. The drive systems have been incorporated into two novel bipedal robots capable of three-dimensional unsupported walking. Robot data illustrate effective torque tracking and nearly silent operation. Finally, comparisons with alternative transmission designs illustrate the size, weight, and endurance advantages of using this type of synthetic rope drive system.
Electric motors are a popular choice for mobile robots because they can provide high peak efficiencies, high speeds, and quiet operation. However, the continuous torque performance of these actuators is thermally limited due to joule heating, which can ultimately cause insulation breakdown. In this work we illustrate how motor housing design and active cooling can be used to significantly improve the ability of the motor to transfer heat to the environment. This can increase continuous torque density and reduce energy consumption. We present a novel housing design for brushless DC motors that provides improved heat transfer. This design achieves a 50% increase in heat transfer over a nominal design. Additionally, forced air or water cooling can be easily added to this configuration. Forced convection increases heat transfer over the nominal design by 79%with forced air and 107% with pumped water. Finally, we show how increased heat transfer reduces power consumption and we demonstrate that strategically spending energy on cooling can provide net energy savings of 4%-6%.