System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

CEU Session #4 - Space Robotics for On-Orbit Servicing and Space Debris Removal

The next ten years will see an unprecedented increase in the number of spacecraft deployed in Earth orbit and the number of commercial ventures operating space assets. The large increase in the number of spacecraft and the large increase in the commercial value of space will lead to renewed interest in robotic on-orbit servicing (OOS) and active debris removal (ADR). The lecture will provide a brief overview over the history of crewed and robotic OOS and discuss the missions planned for the near future. It will then proceed to identify the critical enabling technologies for a future, operational OOS and ADR infrastructure, discuss the technical challenges and present promising concepts and demonstrated technologies that can make routine OOS and ADR a possibility. The focus will be on robotics technologies and spacecraft guidance, navigation and control systems

Advancing Embedded and Extrinsic Solutions for Optimal Control and Efficiency of Energy Systems in Buildings

Buildings account for approximately 40% of all U.S. energy usage and carbon emissions. Reducing energy usage and improving efficiency in buildings has the potential for significant environmental and economic impacts. To do so, reoccurring identification of hardware and operational opportunities is needed to maintain building efficiency. Additionally, the development of controls that continually operate building systems and equipment at energy optimal conditions is required. This dissertation provides contributions to both of the aforementioned areas, which can be divided into two distinct portions. The first presents the framework for the development of an automated energy audit process, termed Autonomous Robotic Assessments of Energy (AuRAE). The automation of energy audits would decrease the cost of audits to customers, reduce the time auditors need to invest in an audit, and provide repeatable audit processes with enhanced data collection. In this framework of AuRAE, novel, audit-centric navigational strategies are presented that enable the complete exploration of a previously unknown space in a building while identifying and navigating to objects of interest in real-time as well as navigation around external building perimeters. Simulations of the navigational strategies show success in a variety of building layouts and size of objects of interest. Additionally, prototypes of robotic audit capabilities are demonstrated in the form of a lighting identification and analysis package on a ground vehicle and an environmental baseline measurement package on an aerial vehicle. The second portion presents the development and simulation of two advanced economic building energy controllers: one utilizes steady-state relationships for optimizing control setpoints while the other is an economic MPC method using dynamic models to optimize the same control setpoints. Both control methods balance the minimization of utility cost from energy usage with the cost of lost productivity due to occupant discomfort, differing from standard building optimal control that generally addresses occupant comfort through setpoint limits or comfort measure constraints. This is accomplished through the development of component-level economic objective functions for each subsystem in the modeled building. The results show that utility cost and the cost of occupant productivity from optimal comfort can be successfully balanced, and even improved over current control methods. The relative magnitude of the cost of lost productivity is shown to be significantly higher than the cost of utilities, suggesting that building operators, technicians, and researchers should make maintaining occupant comfort a top priority to achieve the greatest economic savings. Furthermore, the results demonstrate that by using steady-state predictions, the majority of the performance gains produced with a fully dynamic MPC solution can be recovered

Research and Technology Objectives and Plans Summary (RTOPS)

This publication represents the NASA research and technology program for FY89. It is a compilation of the Summary portions of each of the RTOPs (Research and Technology Objectives and Plans) used for management review and control of research currently in progress throughout NASA. The RTOP Summary is designed to facilitate communication and coordination among concerned technical personnel in government, in industry, and in universities. The first section containing citations and abstracts of the RTOPs is followed by four indexes: Subject, Technical Monitor, Responsible NASA Organization, and RTOP Number

A Scalable Framework for Parallelizing Sampling-Based Motion Planning Algorithms

Motion planning is defined as the problem of finding a valid path taking a robot (or any movable object) from a given start configuration to a goal configuration in an environment. While motion planning has its roots in robotics, it now finds application in many other areas of scientific computing such as protein folding, drug design, virtual prototyping, computer-aided design (CAD), and computer animation. These new areas test the limits of the best sequential planners available, motivating the need for methods that can exploit parallel processing. This dissertation focuses on the design and implementation of a generic and scalable framework for parallelizing motion planning algorithms. In particular, we focus on sampling-based motion planning algorithms which are considered to be the state-of-the-art. Our work covers the two broad classes of sampling-based motion planning algorithms--the graph-based and the tree-based methods. Central to our approach is the subdivision of the planning space into regions. These regions represent sub- problems that can be processed in parallel. Solutions to the sub-problems are later combined to form a solution to the entire problem. By subdividing the planning space and restricting the locality of connection attempts to adjacent regions, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We also describe how load balancing strategies can be applied in complex environments. We present experimental results that scale to thousands of processors on different massively parallel machines for a range of motion planning problems

Automated Find Fix and Track with a Medium Altitude Long Endurance Remotely Piloted Aircraft

A limitation in RPA ISR operations is loss of target track if the command link is severed. For an RPA to effectively execute the ISR mission without a command link, it needs the capability to F2T targets autonomously. Automated Find Fix and Track (AFFTRAC) was developed to help solve this problem by demonstrating a proof of concept tactical autopilot. Monocular stereo vision was used to process sequential images acquired during orbit to produce a partial structural point cloud of the original structure. This partial structural point cloud was then exploited to create a holding area density for the aircraft to stay within. A simple greedy algorithm exploited this holding area density to produce aircraft turn commands to approximate tactical ISR holding. The result was that imagery from existing MQ-9 sensors was used to provide command guidance to autonomously to maintain line of sight to a target. Overall, AFFTRAC is a promising initial framework for a tactical autopilot, but additional development is needed to mature component algorithms

A hybrid approach to simultaneous localization and mapping in indoors environment

This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment

Proceedings of the 2017 Coal Operators\u27 Conference

Proceedings of the 2017 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978174128261