Hierarchical Annotated Skeleton-Guided Tree-based Motion Planning

We present a hierarchical tree-based motion planning strategy, HAS-RRT, guided by the workspace skeleton to solve motion planning problems in robotics and computational biology. Relying on the information about the connectivity of the workspace and the ranking of available paths in the workspace, the strategy prioritizes paths indicated by the workspace guidance to find a valid motion plan for the moving object efficiently. In instances of suboptimal guidance, the strategy adapts its reliance on the guidance by hierarchically reverting to local exploration of the planning space. We offer an extensive comparative analysis against other tree-based planning strategies and demonstrate that HAS-RRT reliably and efficiently finds low-cost paths. In contrast to methods prone to inconsistent performance across different environments or reliance on specific parameters, HAS-RRT is robust to workspace variability

Sampling-Based Motion Planning for Tunnel Detection in Protein Structures

Studium proteinů je hlavní oblastí výzkumu molekulární biologie. Jelikož je struktura a funkce proteinů těsně svázaná, získání informací o jejich prostorové konfiguraci patří mezi hlavní cíle jejich zkoumání. V minulosti se již podařilo získat velké množství struktur různých proteinů a volný přístup k těmto informacím nabízí velký potenciál pro teoretické výpočty strukturní bioinformatiky. Jednou z velmi zajímavých oblastí je i hledání tunelů ve strukturách proteinů. Vytvoření spolehlivých algoritmů detekce tunelů ve strukturách proteinů a simulace průchodu chemických látek těmito tunely může značně urychlit a zefektivnit výzkum v oblasti molekulární biologie, a proto se těmto metodám věnuje mnoho výzkumných skupin po celém světě. Plánování cest patří mezi dobře prozkoumané oblasti kybernetiky a robotiky, kde se používá k návrhu možných cest robotů ve stavovém prostoru. Pokud stavový prostor tvoří struktura proteinu a místo robota použijeme sondu definované velikosti, můžeme algoritmy plánování cest převést z robotické do biologické domény. V rámci této diplomové práce byly implementovány algoritmy známé z plánování cest robotů tak, aby nalezly tunely ve statických i dynamických proteinových strukturách. Dále pak byla navržena a implementována metoda pro simulaci průchodu molekul nalezenými tunely.Study of proteins is a major area of molecular biology research. Since the structure and function of proteins are tightly bound, obtaining information about their spatial configuration is one of the main goals of their research. In the past, a large number of different protein structures has been obtained and free access to this information offers a great potential for calculations in structural bioinformatics. One of the most interesting areas is the detection of tunnels in protein structures. Development of reliable algorithms, which detect tunnels in protein structures, and simulation of the passage of chemicals through these tunnels can greatly accelerate research in the field of molecular biology and so, many research groups are devoted to these methods all around the world. Motion planning is a well--known area of cybernetics and robotics, where it is used to design robot paths in state space. If the state space is formed by the structure of protein and instead of the robot we use a probe of a defined size, we can transfer the motion planning algorithms from the robotic to the biological domain. Within this diploma thesis, algorithms known from the motion planning of robots were modified for the task of tunnel detection in both static and dynamic protein structures. Furthermore, a method for geometrical analysis of the passage of molecules through tunnels was proposed and implemented

Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

Motion planning for geometric models in data visualization

Interaktivní geometrické modely pro simulaci přírodních jevů (LH11006)Pokročilé grafické a počítačové systémy (SGS-2016-013)A finding of path is an important task in many research areas and it is a common problem solved in a wide range of applications. New problems of finding path appear and complex problems persist, such as a real-time plan- ning of paths for huge crowds in dynamic environments, where the properties according to which the cost of a path is evaluated as well as the topology of paths may change. The task of finding a path can be divided into path planning and motion planning, which implicitly respects the collision with surroundings in the environment. Within the first group this thesis focuses on path planning on graphs for crowds. The main idea is to group members of the crowd by their common initial and target positions and then plan the path for one representative member of each group. These representative members can be navigated by classic approaches and the rest of the group will follow them. If the crowd can be divided into a few groups this way, the proposed approach will save a huge amount of computational and memory demands in dynamic environments. In the second area, motion planning, we are dealing with another problem. The task is to navigate the ligand through the protein or into the protein, which turns out to be a challenging problem because it needs to be solved in 3D with the collision detection

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 39)

Abstracts are provided for 154 patents and patent applications entered into the NASA scientific and technical information systems during the period Jan. 1991 through Jun. 1991. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent or patent application

Intelligent Motion Planning and Analysis with Probabilistic Roadmap Methods for the Study of Complex and High-Dimensional Motions

At first glance, robots and proteins have little in common. Robots are commonly thought of as tools that perform tasks such as vacuuming the floor, while proteins play essential roles in many biochemical processes. However, the functionality of both robots and proteins is highly dependent on their motions. In order to study motions in these two divergent domains, the same underlying algorithmic framework can be applied. This method is derived from probabilistic roadmap methods (PRMs) originally developed for robotic motion planning. It builds a graph, or roadmap, where configurations are represented as vertices and transitions between configurations are edges. The contribution of this work is a set of intelligent methods applied to PRMs. These methods facilitate both the modeling and analysis of motions, and have enabled the study of complex and high-dimensional problems in both robotic and molecular domains. In order to efficiently study biologically relevant molecular folding behaviors we have developed new techniques based on Monte Carlo solution, master equation calculation, and non-linear dimensionality reduction to run simulations and analysis on the roadmap. The first method, Map-based master equation calculation (MME), extracts global properties of the folding landscape such as global folding rates. On the other hand, another method, Map-based Monte Carlo solution (MMC), can be used to extract microscopic features of the folding process. Also, the application of dimensionality reduction returns a lower-dimensional representation that still retains the principal features while facilitating both modeling and analysis of motion landscapes. A key contribution of our methods is the flexibility to study larger and more complex structures, e.g., 372 residue Alpha-1 antitrypsin and 200 nucleotide ColE1 RNAII. We also applied intelligent roadmap-based techniques to the area of robotic motion. These methods take advantage of unsupervised learning methods at all stages of the planning process and produces solutions in complex spaces with little cost and less manual intervention compared to other adaptive methods. Our results show that our methods have low overhead and that they out-perform two existing adaptive methods in all complex cases studied

Building Occupancy Simulation and Data Assimilation Using a Graph Based Agent Oriented Model

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates the real time spatial distribution of occupants in a building. It can benefit various applications like conserving energy, smart assist, building construction, crowd management, and emergency evacuation. Building occupancy simulation and estimation needs a simulation model and a data assimilation algorithm that assimilates real-time sensor data into the simulation model. Existing build occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating a large number occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of the existing models, in this dissertation, we combine the benefits of agent and graph based modeling and develop a new graph based agent oriented model which can efficiently simulate a large number of occupants in various building structures. To support real-time occupancy dynamics estimation, we developed a data assimilation framework based on Sequential Monte Carol Methods, and apply it to the graph-based agent oriented model to assimilate real time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this dissertation work include 1) it provides an efficient model for building occupancy simulation which can accommodate thousands of occupants; 2) it provides an effective data assimilation framework for real-time estimation of building occupancy