Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots

Planning for legged-wheeled machines is typically done using trajectory optimization because of many degrees of freedom, thus rendering legged-wheeled planners prone to falling prey to bad local minima. We present a combined sampling and optimization-based planning approach that can cope with challenging terrain. The sampling-based stage computes whole-body configurations and contact schedule, which speeds up the optimization convergence. The optimization-based stage ensures that all the system constraints, such as non-holonomic rolling constraints, are satisfied. The evaluations show the importance of good initial guesses for optimization. Furthermore, they suggest that terrain/collision (avoidance) constraints are more challenging than the robot model's constraints. Lastly, we extend the optimization to handle general terrain representations in the form of elevation maps

자동화 굴착기를 위한 숙련자 굴착력 패턴 기반 굴착 작업 궤적 생성

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이동준.In this thesis, we propose an excavation trajectory generation framework for autonomous excavators based on expert operator forcing pattern. The primary focus is to develop autonomous excavator system which is stable and guarantees a certain quantity of excavation in various surroundings. We nd the excavation trajectories based on the terrain features and the excavation forcing patterns from the excavation data of expert operators. The expert excavation trajectories are encoded with dynamic movement primitives (DMP) and learn through multilayer perceptron (MLP). The excavation trajectory is generated according to the terrain feature using the trained model. The excavator is modeled with 3-DoF rigid body system, and the excavation force on the bucket tip is estimated online by using the momentum-based disturbance observer(DOB). The estimated force is added to the DMP as a coupling term to modulate the excavation trajectory in real-time so that the estimated force can follow the expert excavation force pattern. Lastly, we verify the performance of the suggested framework through simulation and actual excavator test.본 논문에서는 자동화 굴착기를 위한 숙련자 굴착력 패턴 기반 굴착 작업 궤적 계획 프레임워크를 제시한다. 본 프레임워크는 자동화 굴착기의 다양한 작업 환경에서 숙련자와 유사하게 안정된 굴착 작업을 수행하며, 굴착량이 보장되는 작업을 하는 것이 목표이다. 우선 숙련된 굴착기 작업자들의 굴착 작업 데이터로부터 지형 특징에 기반한 작업 궤적과 굴착력 패턴을 찾아내었다. 숙련자의 굴착 궤적은 dynamic movement primitives(DMP)으로 encoding하여 neural network의 한 기법인 multi-layer perceptron(MLP)을 통해 학습하고, 학습된 모델을 기반으로 지형에 따른 굴착 궤적을 생성하였다. 굴착기를 다자유도 강체 시스템으로 모델링 하고, 실시간으로 버켓 끝단에 걸리는 굴착력을 momentum-based disturbance observer를 이용하여 추정하였다. 추정된 굴착력은 실시간으로 굴착 궤적을 재생성 하기위해 DMP에 coupling term으로 추가하였고, 이를 통해 추정되는 굴착력이 숙련자의 굴착 패턴을 따라갈 수 있도록 제어하였다. 마지막으로 제안한 프레임워크에 대해서는 시뮬레이션 실험과 실제 굴착기를 이용한 실험을 통해 정합성 검증을 진행하였다.1 Introduction 1 1.1 Motivation and Objectives 1 1.2 Related Work 2 1.3 Contribution 4 2 Preliminary 6 2.1 System Description 6 2.2 Excavator Dynamic Modeling 7 2.3 Force Estimation via Momentum Based Disturbance Observer 9 2.4 Dynamic Movement Primitives 10 3 Excavation Trajectory Generation 13 3.1 Analysis of Expert's Excavation Trajectory 13 3.2 Generate Nominal Excavation Trajectory by Imitating Expert Operator 19 3.3 Modulate Excavation Trajectory by Force Pattern of Expert Operator 22 4 Experiments 26 4.1 Excavation Simulation 26 4.1.1 Excavation on Flat and Slope Terrain 26 4.1.2 Excavation using Trajectory Generated by Incorrect Terrain Recognition 31 4.1.3 Excavation with Obstacle in the Ground 33 4.2 Excavation Test Result using Excavator 35 5 Conclusion and Future Work 40 5.1 Conclusion 40 5.2 Future Work 41Maste

NASA Tech Briefs, January 2013

Topics include: Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector; Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators; 3D Hail Size Distribution Interpolation/Extrapolation Algorithm; Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels; Artificial Intelligence Software for Assessing Postural Stability; Transformers: Shape-Changing Space Systems Built with Robotic Textiles; Fibrillar Adhesive for Climbing Robots; Using Pre-Melted Phase Change Material to Keep Payloads in Space Warm for Hours without Power; Development of a Centrifugal Technique for the Microbial Bioburden Analysis of Freon (CFC-11); Microwave Sinterator Freeform Additive Construction System (MS-FACS); DSP/FPGA Design for a High-Speed Programmable S-Band Space Transceiver; On-Chip Power-Combining for High-Power Schottky Diode-Based Frequency Multipliers; FPGA Vision Data Architecture; Memory Circuit Fault Simulator; Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination; Regolith Advanced Surface Systems Operations Robot Excavator; Magnetically Actuated Seal; Hybrid Electrostatic/Flextensional Mirror for Lightweight, Large-Aperture, and Cryogenic Space Telescopes; System for Contributing and Discovering Derived Mission and Science Data; Remote Viewer for Maritime Robotics Software; Stackfile Database; Reachability Maps for In Situ Operations; JPL Space Telecommunications Radio System Operating Environment; RFI-SIM: RFI Simulation Package; ION Configuration Editor; Dtest Testing Software; IMPaCT - Integration of Missions, Programs, and Core Technologies; Integrated Systems Health Management (ISHM) Toolkit; Wind-Driven Wireless Networked System of Mobile Sensors for Mars Exploration; In Situ Solid Particle Generator; Analysis of the Effects of Streamwise Lift Distribution on Sonic Boom Signature; Rad-Tolerant, Thermally Stable, High-Speed Fiber-Optic Network for Harsh Environments; Towed Subsurface Optical Communications Buoy; High-Collection-Efficiency Fluorescence Detection Cell; Ultra-Compact, Superconducting Spectrometer-on-a-Chip at Submillimeter Wavelengths; UV Resonant Raman Spectrometer with Multi-Line Laser Excitation; Medicine Delivery Device with Integrated Sterilization and Detection; Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME; Airborne Tomographic Swath Ice Sounding Processing System; flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter; Estimating Torque Imparted on Spacecraft Using Telemetry; PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils; Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences; Metric Learning to Enhance Hyperspectral Image Segmentation; Basic Operational Robotics Instructional System; Sheet Membrane Spacesuit Water Membrane Evaporator; Advanced Materials and Manufacturing for Low-Cost, High-Performance Liquid Rocket Combustion Chambers; Motor Qualification for Long-Duration Mars Missions

Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation

Effective and causal observable functions for low-order lifting linearization of nonlinear controlled systems are learned from data by using neural networks. While Koopman operator theory allows us to represent a nonlinear system as a linear system in an infinite-dimensional space of observables, exact linearization is guaranteed only for autonomous systems with no input, and finding effective observable functions for approximation with a low-order linear system remains an open question. Dual Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system. Here, a data-driven method is presented for generating a set of nonlinear observable functions that can accurately approximate a nonlinear control system to a low-order linear control system. A caveat in using data of measured variables as observables is that the measured variables may contain input to the system, which incurs a causality contradiction when lifting the system, i.e. taking derivatives of the observables. The current work presents a method for eliminating such anti-causal components of the observables and lifting the system using only causal observables. The method is applied to excavation automation, a complex nonlinear dynamical system, to obtain a low-order lifted linear model for control design

Computational dynamics and virtual dragline simulation for extended rope service life

The dragline machinery is one of the largest equipment for stripping overburden materials in surface mining operations. Its effectiveness requires rigorous kinematic and dynamic analyses. Current dragline research studies are limited in computational dynamic modeling because they eliminate important structural components from the front-end assembly. Thus, the derived kinematic, dynamic and stress intensity models fail to capture the true response of the dragline under full operating cycle conditions. This research study advances a new and robust computational dynamic model of the dragline front-end assembly using Kane\u27s method. The model is a 3-DOF dynamic model that describes the spatial kinematics and dynamics of the dragline front-end assembly during digging and swinging. A virtual simulator, for a Marion 7800 dragline, is built and used for analyzing the mass and inertia properties of the front-end components. The models accurately predict the kinematics, dynamics and stress intensity profiles of the front-end assembly. The results showed that the maximum drag force is 1.375 MN, which is within the maximum allowable load of the machine. The maximum cutting resistance of 412.31 KN occurs 5 seconds into digging and the maximum hoist torque of 917. 87 KN occurs 10 seconds into swinging. Stress analyses are carried out on wire ropes using ANSYS Workbench under static and dynamic loading. The FEA results showed that significant stresses develop in the contact areas between the wires, with a maximum von Mises stress equivalent to 7800 MPa. This research study is a pioneering effort toward developing a comprehensive multibody dynamic model of the dragline machinery. The main novelty is incorporating the boom point-sheave, drag-chain and sliding effect of the bucket, excluded from previous research studies, to obtain computationally dynamic efficient models for load predictions --Abstract, page iii