Trajectory Planning on Grids: Considering Speed Limit Constraints

Trajectory (path) planning is a well known and thoroughly studied field of automated planning. It is usually used in computer games, robotics or autonomous agent simulations. Grids are often used for regular discretization of continuous space. Many methods exist for trajectory (path) planning on grids, we address the well known A* algorithm and the state-of-the-art Theta* algorithm. Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. In this paper, we provide an extension of both these algorithms to enable support for speed limit constraints.We experimentally evaluate and thoroughly discuss how the extensions affect the planning process showing reasonability and justification of our approach

A comprehensive study on pathfinding techniques for robotics and video games

This survey provides an overview of popular pathfinding algorithms and techniques based on graph generation problems. We focus on recent developments and improvements in existing techniques and examine their impact on robotics and the video games industry. We have categorized pathfinding algorithms based on a 2D/3D environment search. The aim of this paper is to provide researchers with a thorough background on the progress made in the last 10 years in this field, summarize the principal techniques, and describe their results. We also give our expectations for future trends in this field and discuss the possibility of using pathfinding techniques in more extensive areas

Expert iteration

In this thesis, we study how reinforcement learning algorithms can tackle classical board games without recourse to human knowledge. Specifically, we develop a framework and algorithms which learn to play the board game Hex starting from random play. We first describe Expert Iteration (ExIt), a novel reinforcement learning framework which extends Modified Policy Iteration. ExIt explicitly decomposes the reinforcement learning problem into two parts: planning and generalisation. A planning algorithm explores possible move sequences starting from a particular position to find good strategies from that position, while a parametric function approximator is trained to predict those plans, generalising to states not yet seen. Subsequently, planning is improved by using the approximated policy to guide search, increasing the strength of new plans. This decomposition allows ExIt to combine the benefits of both planning methods and function approximation methods. We demonstrate the effectiveness of the ExIt paradigm by implementing ExIt with two different planning algorithms. First, we develop a version based on Monte Carlo Tree Search (MCTS), a search algorithm which has been successful both in specific games, such as Go, Hex and Havannah, and in general game playing competitions. We then develop a new planning algorithm, Policy Gradient Search (PGS), which uses a model-free reinforcement learning algorithm for online planning. Unlike MCTS, PGS does not require an explicit search tree. Instead PGS uses function approximation within a single search, allowing it to be applied to problems with larger branching factors. Both MCTS-ExIt and PGS-ExIt defeated MoHex 2.0 - the most recent Hex Olympiad winner to be open sourced - in 9 × 9 Hex. More importantly, whereas MoHex makes use of many Hex-specific improvements and knowledge, all our programs were trained tabula rasa using general reinforcement learning methods. This bodes well for ExIt’s applicability to both other games and real world decision making problems

Implementation of a local path planning algorithm for unmanned aerial vehicles

As the presence of Unmanned Aircraft Systems (UASs) become more prominent today and in the future. They are used in a variety of ways to solve solutions for a variety of tasks. UASs that are battery-powered typically have a flight time of no more than 30 minutes. Some tasks make take the drone beyond visual line of sight (BVLOS). The approach taken within this paper is allocating a secondary flight computer onboard the UAS to calculate paths while the primary computer controls the aircraft and follows the path being generated. With a proper map of the environment and use of a path planning algorithm the safety of the aircraft can be increased in missions that are BVLOS. This thesis will cover the concepts of path planning algorithms and the development of a modified version of a popular path planning algorithm. Show simulations of comparison with other variations of path planning algorithms and software in the loop (SITL) simulations on a fixed-wing aircraft. It will also show this algorithm's results when implemented in flight tests onboard a fixed-wing and multi-rotor UAS

Wall-to-Wall Forest Mapping in Southeast and Southcentral Alaska: A New Application of the Gradient Nearest Neighbor Approach

Boreal and temperate biomes host nearly half of the earth’s forested ecosystems. The temperate rainforests of the Pacific coast of North America constitute nearly half of all temperate rainforests on earth. Along the northern extent of this region, the perhumid and sub-polar rainforests of southeast and southcentral Alaska are among the largest intact tracts of temperate rainforest in existence. These forests are globally significant for their role in storing and cycling carbon and are regionally and locally valued for their cultural significance, their provision of ecosystem services, and their economic importance. The cumulative impacts of historic management and uncertainties regarding future conditions under a changing climate have largely gone understudied in this important ecosystem. A relative dearth of spatially comprehensive information exists to describe detailed forest attributes at a resolution relevant for both informing management decisions and at an extent necessary to meet regional and national monitoring objectives. This study demonstrates one approach to providing wall-to-wall forest attribute data across the forested areas of coastal southeast and southcentral Alaska using the Gradient Nearest Neighbor (GNN) method. I leverage field surveys from the USDA Forest Service Forest Inventory and Analysis (FIA) program collected across a 26-year timespan (1995-2020) with a set of spatially continuous environmental predictors and annual Landsat Timeseries (LTS) to produce spatially explicit 30-m predictions of forest structure and composition across the region. Spectral harmonization across sensors, a multi-step cloud masking procedure, and the spectral segmentation algorithm, LandTrendr, were implemented in Google Earth Engine (LT-GEE), to produce spatially complete annual imagery for model development. Model predictions were generally more precise and less biased in the boreal forest biome of the western Kenai Peninsula, lending support for further exploration of the LandTrendr-GNN approach to broader monitoring efforts across Interior Alaska. In the coastal temperate rainforest ecoprovince, models tended to truncate distributions and overpredict some observation estimates, but overall agreement revealed relatively strong alignment with design-based estimates in this heterogeneous region

A Status of NASA Rotorcraft Research

In 2006, NASA rotorcraft research was refocused to emphasize high-fidelity first-principles predictive tool development and validation. As part of this new emphasis, documenting the status of NASA rotorcraft research and defining the state-of-the-art in rotorcraft predictive capability were undertaken. This report is the result of this two-year effort. Contributors to this work encompass a wide range of expertise covering the technical disciplines of aeromechanics, acoustics, computational fluid dynamics (CFD), flight dynamics and control, experimental capabilities, propulsion, structures and materials, and multi-disciplinary analysis

The measurement of the adhesion of glaze ice.

Icing is a lethal and costly aviation hazard affecting aircraft of all sizes ranging from small UAVs to large commercial craft such as the Boeing 787, resulting in hundreds of deaths over the last century and resulting in billions of dollars of economic impact. Three predominant types of icing, namely, airframe icing, engine icing, and rotorcraft icing, dominate aircraft icing research. Each type poses unique challenges. In the case of rotor icing, attention needs to give to the rotating environment with large oscillatory loads and icing conditions. In the case of airframe icing, special attention needs to be paid to a variety of loading cases from the available options for de-icing and anti-icing equipment. In the case of engine icing, the formation mechanism is poorly understood and the structure of the ice needs to be studied in greater detail. Airframe icing requires However, all three share the same fundamental problem that ice sticks to surfaces. How strongly ice adheres to a surface dictates how hard it is to remove. The adhesion strength then regulates the primary threat from icing - the maximum aerodynamic penalty that accretion will have. It also dictates the threat of impingement from shed ice elsewhere on the aircraft, such that a piece of ice from the main rotor could strike the tail rotor on a helicopter, destroying it. There exist many methods to evaluate the adhesion of ice to a given substrate, the most common being pusher tests and centrifuge tests. These and other methods are problematic in evaluating the adhesive properties of ice to a substrate in aircraft icing conditions; no data in the literature exists that accounts for stress concentrations at the interface and the strain rate at the interface, and no data was found in the literature on the grain structure of impact ice at speeds relevant to aircraft icing. A new method to measure the adhesion of impact ice has been developed based on a lap joint shear test. Lap joint tests are common in adhesion measurements since they produce nearly uniform stress at the interface of interest. In support of this end, a new shear rig and a new wind tunnel model for the Icing Research Tunnel were designed and fabricated. Six nights of testing in the Icing Research Tunnel were conducted to obtain samples, which were later tested in a laboratory environment. The tests were displacement controlled and samples were tested at four crosshead speed rates. The grain structure of the ice was documented using a cross-polarized optical microscope for the first two nights of testing, showing significant differences in the grain structure dependent on velocity and whether the cloud was in the SLD range or not. Correlations with temperature and test section velocity are demonstrated. It was also demonstrated that residual stresses, which are unaccounted for in the literature, play a significant role in the adhesion of impact ice. Options to improve the test methodology further are discussed. Finally, a shedding model predicting the trajectory of ice at a shed event has been developed and validated against test data. This model successfully predicted the front of a multi-break shed event in the Icing Research Tunnel

A Summary of NASA Rotary Wing Research: Circa 20082018

The general public may not know that the first A in NASA stands for Aeronautics. If they do know, they will very likely be surprised that in addition to airplanes, the A includes research in helicopters, tiltrotors, and other vehicles adorned with rotors. There is, arguably, no subsonic air vehicle more difficult to accurately analyze than a vehicle with lift-producing rotors. No wonder that NASA has conducted rotary wing research since the days of the NACA and has partnered, since 1965, with the U.S. Army in order to overcome some of the most challenging obstacles to understanding the behavior of these vehicles. Since 2006, NASA rotary wing research has been performed under several different project names [Gorton et al., 2015]: Subsonic Rotary Wing (SRW) (20062012), Rotary Wing (RW) (20122014), and Revolutionary Vertical Lift Technology (RVLT) (2014present). In 2009, the SRW Project published a report that assessed the status of NASA rotorcraft research; in particular, the predictive capability of NASA rotorcraft tools was addressed for a number of technical disciplines. A brief history of NASA rotorcraft research through 2009 was also provided [Yamauchi and Young, 2009]. Gorton et al. [2015] describes the system studies during 20092011 that informed the SRW/RW/RVLT project investment prioritization and organization. The authors also provided the status of research in the RW Project in engines, drive systems, aeromechanics, and impact dynamics as related to structural dynamics of vertical lift vehicles. Since 2009, the focus of research has shifted from large civil VTOL transports, to environmentally clean aircraft, to electrified VTOL aircraft for the urban air mobility (UAM) market. The changing focus of rotorcraft research has been a reflection of the evolving strategic direction of the NASA Aeronautics Research Mission Directorate (ARMD). By 2014, the project had been renamed the Revolutionary Vertical Lift Technology Project. In response to the 2014 NASA Strategic Plan, ARMD developed six Strategic Thrusts. Strategic Thrust 3B was defined as the Ultra-Efficient Commercial VehiclesVertical Lift Aircraft. Hochstetler et al. [2017] uses Thrust 3B as an example for developing metrics usable by ARMD to measure the effectiveness of each of the Strategic Thrusts. The authors provide near-, mid-, and long-term outcomes for Thrust 3B with corresponding benefits and capabilities. The importance of VTOL research, especially with the rapidly expanding UAM market, eventually resulted in a new Strategic Thrust (to begin in 2020): Thrust 4Safe, Quiet, and Affordable Vertical Lift Air Vehicles. The underlying rotary wing analysis tools used by NASA are still applicable to traditional rotorcraft and have been expanded in capability to accommodate the growing number of VTOL configurations designed for UAM. The top-level goal of the RVLT Project remains unchanged since 2006: Develop and validate tools, technologies and concepts to overcome key barriers for vertical lift vehicles. In 2019, NASA rotary wing/VTOL research has never been more important for supporting new aircraft and advancements in technology. 2 A decade is a reasonable interval to pause and take stock of progress and accomplishments. In 10 years, digital technology has propelled progress in computational efficiency by orders of magnitude and expanded capabilities in measurement techniques. The purpose of this report is to provide a compilation of the NASA rotary wing research from ~2008 to ~2018. Brief summaries of publications from NASA, NASA-funded, and NASA-supported research are provided in 12 chapters: Acoustics, Aeromechanics, Computational Fluid Dynamics (External Flow), Experimental Methods, Flight Dynamics and Control, Drive Systems, Engines, Crashworthiness, Icing, Structures and Materials, Conceptual Design and System Analysis, and Mars Helicopter. We hope this report serves as a useful reference for future NASA vertical lift researchers

Unmanned vehicles formation control in 3D space and cooperative search

The first problem considered in this dissertation is the decentralized non-planar formation control of multiple unmanned vehicles using graph rigidity. The three-dimensional formation control problem consists of n vehicles operating in a plane Q and r vehicles that operate in an upper layer outside of the plane Q. This can be referred to as a layered formation control where the objective is for all vehicles to cooperatively acquire a predefined formation shape using a decentralized control law. The proposed control strategy is based on regulating the inter-vehicle distances and uses backstepping and Lyapunov approaches. Three different models, with increasing level of complexity are considered for the multi-vehicle system: the single integrator vehicle model, the double integrator vehicle model, and a model that represents the dynamics of a class of robotics vehicles including wheeled mobile robots, underwater vehicles with constant depth, aircraft with constant altitude, and marine vessels. A rigorous stability analysis is presented that guarantees convergence of the inter-vehicle distances to desired values. Additionally, a new Neural Network (NN)-based control algorithm that uses graph rigidity and relative positions of the vehicles is proposed to solve the formation control problem of unmanned vehicles in 3D space. The control law for each vehicle consists of a nonlinear component that is dependent on the closed-loop error dynamics plus a NN component that is linear in the output weights (a one-tunable layer NN is used). A Lyapunov analysis shows that the proposed distance-based control strategy achieves the uniformly ultimately bounded stability of the desired infinitesimally and minimally rigid formation and that NN weights remain bounded. Simulation results are included to demonstrate the performance of the proposed method. The second problem addressed in this dissertation is the cooperative unmanned vehicles search. In search and surveillance operations, deploying a team of unmanned vehicles provides a robust solution that has multiple advantages over using a single vehicle in efficiency and minimizing exploration time. The cooperative search problem addresses the challenge of identifying target(s) in a given environment when using a team of unmarried vehicles by proposing a novel method of mapping and movement of vehicle teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, the vehicles have an efficient travel path while performing searches due to this partitioning approach. Second, a team of unmanned vehicles that move in a cooperative manner and utilize the Tabu Random algorithm is used to search for target(s). Due to the ever-increasing use of robotics and unmanned systems, the field of cooperative multi-vehicle search has developed many applications recently that would benefit from the use of the approach presented in this dissertation, including: search and rescue operations, surveillance, data collection, and border patrol. Simulation results are presented that show the performance of the Tabu Random search algorithm method in combination with hexagonal partitioning