Thermal Recovery of Multi-Limbed Robots with Electric Actuators

The problem of finding thermally minimizing configurations of a humanoid robot to recover its actuators from unsafe thermal states is addressed. A first-order, data-driven, effort based, thermal model of the robots actuators is devised, which is used to predict future thermal states. Given this predictive capability, a map between configurations and future temperatures is formulated to find what configurations, subject to valid contact constraints, can be taken now to minimize future thermal states. Effectively, this approach is a realization of a contact-constrained thermal inverse-kinematics (IK) process. Experimental validation of the proposed approach is performed on the NASA Valkyrie robot hardware

Human detection, gesture recognition, and policy generation for human-aware robots

For robots to be deployable in human occupied environments, the robots must have human-awareness and generate human-aware behaviors and policies. This thesis posits that a human-aware robot must be capable of (1) human detection and tracking, (2) human action or intent recognition and (3) intelligent, human-aware action generation. This work presents and evaluates a methodology for each stated capability. In Chapter 2, a method for practical side-by-side human detection for the Valkyrie robot using the Multisense SL sensor is presented. An explanation of why current off-the-shelf techniques are not suitable and a depth-based algorithm using point cloud descriptors and a Random Forest classifier for detecting humans under occlusion, in close proximity, in varying sparsity, and in random poses on the Multisense SL sensor are presented. In Chapter 3, action recognition of arm motion gestures is framed as a supervised learning problem. A popular technique for gesture representation with dynamic movement primitives (DMPs) and its classification using Gaussian Mixture Models (GMMs) is explored. The approach is tested under various hypotheses to understand the intricacies of using DMPs for movement representation. The following findings are reported: (a) recognition rate is sensitive to the number of basis weights, (b) DMPs can be used to recognize two linear motions, (c) rhythmic gestures can be differentiated with the discrete formulation of DMPs, and (d) DMPs can represent static-type gestures. In Chapter 4, a novel technique for (a) representing Human-Robot- Interaction as a dynamical system, and (b) using model predictive control to generate control policies is presented. The approach is motivated by using a scenario in which an Assistive Robot must be productive by bringing work to the human but must also be mindful of the human's workload. By modeling the interaction as a dynamical system, advances in control theory can be leveraged to generate useful control policies.Mechanical Engineerin

ROS wrapper for real-time multi-person pose estimation with a single camera

For robots to be deployable in human occupied environments, the robots must have human-awareness and generate human-aware behaviors and policies. OpenPose is a library for real-time multi-person keypoint detection. We have considered the implementation of a ROS package that would allow the estimation of 2d pose from simple RGB images, for which we have introduced a ROS wrapper that automatically recovers the pose of several people from a single camera using OpenPose. Additionally, a ROS node to obtain 3d pose estimation from the initial 2d pose estimation when a depth image is synchronized with the RGB image (RGB-D image, such as with a Kinect camera) has been developed. This aim is attained projecting the 2d pose estimation onto the point-cloud of the depth image.Peer ReviewedPreprin

Flight Test of an Intelligent Flight-Control System

The F-15 Advanced Controls Technology for Integrated Vehicles (ACTIVE) airplane (see figure) was the test bed for a flight test of an intelligent flight control system (IFCS). This IFCS utilizes a neural network to determine critical stability and control derivatives for a control law, the real-time gains of which are computed by an algorithm that solves the Riccati equation. These derivatives are also used to identify the parameters of a dynamic model of the airplane. The model is used in a model-following portion of the control law, in order to provide specific vehicle handling characteristics. The flight test of the IFCS marks the initiation of the Intelligent Flight Control System Advanced Concept Program (IFCS ACP), which is a collaboration between NASA and Boeing Phantom Works. The goals of the IFCS ACP are to (1) develop the concept of a flight-control system that uses neural-network technology to identify aircraft characteristics to provide optimal aircraft performance, (2) develop a self-training neural network to update estimates of aircraft properties in flight, and (3) demonstrate the aforementioned concepts on the F-15 ACTIVE airplane in flight. The activities of the initial IFCS ACP were divided into three Phases, each devoted to the attainment of a different objective. The objective of Phase I was to develop a pre-trained neural network to store and recall the wind-tunnel-based stability and control derivatives of the vehicle. The objective of Phase II was to develop a neural network that can learn how to adjust the stability and control derivatives to account for failures or modeling deficiencies. The objective of Phase III was to develop a flight control system that uses the neural network outputs as a basis for controlling the aircraft. The flight test of the IFCS was performed in stages. In the first stage, the Phase I version of the pre-trained neural network was flown in a passive mode. The neural network software was running using flight data inputs with the outputs provided to instrumentation only. The IFCS was not used to control the airplane. In another stage of the flight test, the Phase I pre-trained neural network was integrated into a Phase III version of the flight control system. The Phase I pretrained neural network provided realtime stability and control derivatives to a Phase III controller that was based on a stochastic optimal feedforward and feedback technique (SOFFT). This combined Phase I/III system was operated together with the research flight-control system (RFCS) of the F-15 ACTIVE during the flight test. The RFCS enables the pilot to switch quickly from the experimental- research flight mode back to the safe conventional mode. These initial IFCS ACP flight tests were completed in April 1999. The Phase I/III flight test milestone was to demonstrate, across a range of subsonic and supersonic flight conditions, that the pre-trained neural network could be used to supply real-time aerodynamic stability and control derivatives to the closed-loop optimal SOFFT flight controller. Additional objectives attained in the flight test included (1) flight qualification of a neural-network-based control system; (2) the use of a combined neural-network/closed-loop optimal flight-control system to obtain level-one handling qualities; and (3) demonstration, through variation of control gains, that different handling qualities can be achieved by setting new target parameters. In addition, data for the Phase-II (on-line-learning) neural network were collected, during the use of stacked-frequency- sweep excitation, for post-flight analysis. Initial analysis of these data showed the potential for future flight tests that will incorporate the real-time identification and on-line learning aspects of the IFCS

Phase separation in t-J ladders

The phase separation boundary of isotropic t-J ladders is analyzed using density matrix renormalization group techniques. The complete boundary to phase separation as a function of J/t and doping is determined for a chain and for ladders with two, three and four legs. Six-chain ladders have been analyzed at low hole doping. We use a direct approach in which the phase separation boundary is determined by measuring the hole density in the part of the system which contains both electrons and holes. In addition we examine the binding energy of multi-hole clusters. An extrapolation in the number of legs suggests that the lowest J/t for phase separation to occur in the two dimensional t-J model is J/t~1.Comment: 8 pages in revtex format including 13 embedded figures, one reference adde

Spatially-resolved electronic and vibronic properties of single diamondoid molecules

Diamondoids are a unique form of carbon nanostructure best described as hydrogen-terminated diamond molecules. Their diamond-cage structures and tetrahedral sp3 hybrid bonding create new possibilities for tuning electronic band gaps, optical properties, thermal transport, and mechanical strength at the nanoscale. The recently-discovered higher diamondoids (each containing more than three diamond cells) have thus generated much excitement in regards to their potential versatility as nanoscale devices. Despite this excitement, however, very little is known about the properties of isolated diamondoids on metal surfaces, a very relevant system for molecular electronics. Here we report the first molecular scale study of individual tetramantane diamondoids on Au(111) using scanning tunneling microscopy and spectroscopy. We find that both the diamondoid electronic structure and electron-vibrational coupling exhibit unique spatial distributions characterized by pronounced line nodes across the molecular surfaces. Ab-initio pseudopotential density functional calculations reveal that the observed dominant electronic and vibronic properties of diamondoids are determined by surface hydrogen terminations, a feature having important implications for designing diamondoid-based molecular devices.Comment: 16 pages, 4 figures. to appear in Nature Material