This work demonstrates dual-arm lifting of bulky objects based on inferred object properties (center of mass (COM) location, weight, and shape) using proprioception (i.e. force torque measurements). Data-driven Bayesian models describe these quantities, which enables subsequent behaviors to depend on confidence of the learned models. Experiments were conducted using the NASA Jet Propulsion Laboratory's (JPL) RoboSimian to lift a variety of cumbersome objects ranging in mass from 7kg to 25kg. The position of a supporting second manipulator was determined using a particle set and heuristics that were derived from inferred object properties. The supporting manipulator decreased the initial manipulator's load and distributed the wrench load more equitably across each manipulator, for each bulky object. Knowledge of the objects came from pure proprioception (i.e. without reliance on vision or other exteroceptive sensors) throughout the experiments.

Barycentric spherical robots (BSRs) rely on a noncollocated center of mass and center of rotation for propulsion. Unique challenges inherent to BSRs include a nontrivial correlation between internal actuation, momentum, and net vehicle motion. A new method is presented for deriving reduced dynamical equations of motion (EOM) for a general class of BSRs which extends and synthesizes prior efforts in geometric mechanics. Our method is an extension of the BKMM approach [1], allowing Lagrangian reduction and reconstruction to be applied to dynamical systems with symmetry-breaking potential energies, such as those encountered by BSRs rolling on a surface. The resulting dynamical equations are of minimal dimension and vehicle motion due to actuation and momenta appear linearly in a simple first-order differential equation. The EOM of a BSR named Moball [2] [3] are derived to illustrate the approach's utility. A simple table summarizes our algorithm's application to popular BSRs in the literature, and the approach is extended to sloped terrains.

Moball is a wind-driven spherical robot equipped with sensors for in-situ observation of scientifically important and windy environments, e.g., the Earth's polar regions, Mars, and Saturn's moon Titan. More importantly, Moball incorporates an internal triaxial set of linear electromagnetic generators which can be used to harvest wind energy for long-duration self-sustained operation, or to bias its' wind-driven motions as a form of steering. This paper describes our process to optimize the design of a coreless tubular linear generator for Moball so as to improve energy generation and motion control capabilities with the minimal moving generator mass. The performance of three different types of movers was analyzed with the help of finite element analysis. We determined a final optimized structure and its' dimensions involving a single dipole PM and novel slope-shaped back-irons. A prototype of a single-axis linear generator with a length of 0.8 m was fabricated and assembled. Drop and rotating tests were performed to measure the generated power with this machine. The maximum generated power in the rotating test was 1.05 W at 19 rpm when the load resistance was 40 Ω. The experimental results agreed well with our model predictions. The paper concludes with an overview of the current Moball prototype and ongoing work. The design process developed in this paper can serve as a guideline for future design of energy scavenging systems for robots.

This paper considers the design and optimization of an autonomous electromechanical control and energy scavenging system for the wind-propelled Moball, a spherical mobile sensor platform concept [1, 2]. This mechanism converts mechanical motion to electrical energy, and the same mechanism can function as an actuator to self-generate motion. Simulations of a simplified model on flat ground show that a 2m diameter Moball operating in typical Arctic conditions can generate 1. 8-2.7W of power continuously while being wind-propelled. We also demonstrate a simple motion control algorithm, showing that self-propulsion in windless conditions requires 1-1.5W. Hence, using this mechanism, a Moball can self-generate sufficient energy for long duration missions involving self-propulsion, sensing, and communication in harsh, cold, windy climates (e.g., Polar regions on Earth, or the surface of Titan or Mars) where solar energy may be limited. Simulations with key design parameters are also used to draw general conclusions regarding optimal design for energy recovery. The addition of springs inside the generating mechanism greatly increases the range of wind speeds over which Moball can harvest energy.