Browsing by Subject "motion planning"
Now showing 1 - 9 of 9
Results Per Page
Sort Options
Item A motion planning approach to protein folding(Texas A&M University, 2004-09-30) Song, GuangProtein folding is considered to be one of the grand challenge problems in biology. Protein folding refers to how a protein's amino acid sequence, under certain physiological conditions, folds into a stable close-packed three-dimensional structure known as the native state. There are two major problems in protein folding. One, usually called protein structure prediction, is to predict the structure of the protein's native state given only the amino acid sequence. Another important and strongly related problem, often called protein folding, is to study how the amino acid sequence dynamically transitions from an unstructured state to the native state. In this dissertation, we concentrate on the second problem. There are several approaches that have been applied to the protein folding problem, including molecular dynamics, Monte Carlo methods, statistical mechanical models, and lattice models. However, most of these approaches suffer from either overly-detailed simulations, requiring impractical computation times, or overly-simplified models, resulting in unrealistic solutions. In this work, we present a novel motion planning based framework for studying protein folding. We describe how it can be used to approximately map a protein's energy landscape, and then discuss how to find approximate folding pathways and kinetics on this approximate energy landscape. In particular, our technique can produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap. The roadmap can be computed in a few hours on a desktop PC using a coarse potential energy function. In addition, our motion planning based approach is the first simulation method that enables the study of protein folding kinetics at a level of detail that is appropriate (i.e., not too detailed or too coarse) for capturing possible 2-state and 3-state folding kinetics that may coexist in one protein. Indeed, the unique ability of our method to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics that are not available to other theoretical techniques.Item Intelligent Motion Planning and Analysis with Probabilistic Roadmap Methods for the Study of Complex and High-Dimensional Motions(2011-02-22) Tapia, LydiaAt 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.Item Medial Axis Local Planner: Local Planning for Medial Axis Roadmaps(2012-07-16) Manavi, Kasra MehronIn motion planning, high clearance paths are favorable due to their increased visibility and reduction of collision risk such as the safety of problems involving: human- robot cooperation. One popular approach to solving motion planning problems is the Probabilistic Roadman Method (PRM), which generates a graph of the free space of an environment referred to as a roadmap. In this work we describe a new approach to making high clearance paths when using PRM The medial axis is useful for this since it represents the set of points with maximal clearance and is well defined in higher dimensions. However it can only be computed exactly in workspace. Our goal is to generate roadmaps with paths following the medial axis of an environment without explicitly computing the medial axis. One of the major steps of PRM is local planning: the planning of motion between two nearby nodes PRMs have been used to build roadmaps that have nodes on the medial axis but so far there has been no local planner method proposed for connecting these nodes on the medial axis. These types of high clearance motions are desirable and needed in many robotics applications. This work proposes Medial Axis Local Planner (MALP), a local planner which attempts to connect medial axis configurations via the medial axis. The recursive method takes a simple path between two medial axis configurations and attempts to deform the path to fit the medial axis. This deformation creates paths with high clearance and visibility properties. We have implemented this local planner and have tested it in 2D and 3D rigid body and 8D and 16D fixed base articulated linkage environments. We compare MALP with a straight-line local planner (SL), a typical local planer used in motion planning that interpolated along a line in the planning space. Our results indicate that MALP generated higher clearance paths than SL local planning. As a result, MALP found more connections and generated fewer connected components as compared to connecting the same nodes using SL connections. Using MALP connects noes on the medial axis, increasing the overall clearance of the roadmap generated.Item Motion planning algorithms for a group of mobile agents(Texas A&M University, 2008-10-10) Lal, MayankBuilding autonomous mobile agents has been a major research effort for a while with cooperative mobile robotics receiving a lot of attention in recent times. Motion planning is a critical problem in deploying autonomous agents. In this research we have developed two novel global motion planning schemes for a group of mobile agents which eliminate some of the disadvantages of the current methods available. The first is the homotopy method in which the planning is done in polynomial space. In this method the position in local frame of each mobile agent is mapped to a complex number and a time varying polynomial contains information regarding the current positions of all mobile agents, the degree of the polynomial being the number of mobile agents and the roots of the polynomial representing the position in local frame of the mobile agents at a given time. This polynomial is constructed by finding a path parameterized in time from the initial to the goal polynomial (represent the initial and goal positions in local frame of the mobile agents) so that the discriminant variety or the set of polynomials with multiple roots is avoided in polynomial space. This is equivalent to saying that there is no collision between any two agents in going from initial position to goal position. The second is the homogeneous deformation method. It is based on continuum theory for motion of deformable bodies. In this method a swarm of vehicles is considered at rest in an initial configuration with no restrictions on the initial shape or the locations of the vehicles within that shape. A motion plan is developed to move this swarm of vehicles from the initial configuration to a new configuration such that there are no collisions between any vehicles at any time instant. It is achieved via a linear map between the initial and desired final configuration such that the map is invertible at all times. Both the methods proposed are computationally attractive. Also they facilitate motion coordination between groups of mobile agents with limited or no sensing and communication.Item Motion planning of mobile robot in dynamic environment using potential field and roadmap based planner(Texas A&M University, 2004-09-30) Malik, Waqar AhmadMobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently locate and interact with objects in their environment. My research focuses on developing algorithms to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field, is proposed for real time robot path planning. An algorithm for probabilistic collision detection and avoidance is used to enhance the planner. The aim of the robot is to select avoidance maneuvers to avoid the dynamic obstacles. The navigation of a mobile robot in a real-world dynamic environment is a complex and daunting task. Consider the case of a mobile robot working in an office environment. It has to avoid the static obstacles such as desks, chairs and cupboards and it also has to consider dynamic obstacles such as humans. In the presence of dynamic obstacles, the robot has to predict the motion of the obstacles. Humans inherently have an intuitive motion prediction scheme when planning a path in a crowded environment. A technique has been developed which predicts the possible future positions of obstacles. This technique coupled with the generalized Voronoi diagram enables the robot to safely navigate in a given environment.Item Multi-layer approach to motion planning in obstacle rich environment(2009-05-15) Kim, Sung HyunA widespread use of robotic technology in civilian and military applications has generated a need for advanced motion planning algorithms that are real-time implementable. These algorithms are required to navigate autonomous vehicles through obstacle-rich environments. This research has led to the development of the multilayer trajectory generation approach. It is built on the principle of separation of concerns, which partitions a given problem into multiple independent layers, and addresses complexity that is inherent at each level. We partition the motion planning algorithm into a roadmap layer and an optimal control layer. At the roadmap layer, elements of computational geometry are used to process the obstacle rich environment and generate feasible sets. These are used by the optimal control layer to generate trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the dynamics of the system, and the optimal control layer ignores the complexity of the environment, thus achieving a separation of concern. This decomposition enables computationally tractable methods to be developed for addressing motion planning in complex environments. The approach is applied in known and unknown environments. The methodology developed in this thesis has been successfully applied to a 6 DOF planar robotic testbed. Simulation results suggest that the planner can generate trajectories that navigate through obstacles while satisfying dynamical constraints.Item Rigidity Analysis for Modeling Protein Motion(2010-07-14) Thomas, Shawna L.Protein structure and motion plays an essential role in nearly all forms of life. Understanding both protein folding and protein conformational change can bring deeper insight to many biochemical processes and even into some devastating diseases thought to be the result of protein misfolding. Experimental methods are currently unable to capture detailed, large-scale motions. Traditional computational approaches (e.g., molecular dynamics and Monte Carlo simulations) are too expensive to simulate time periods long enough for anything but small peptide fragments. This research aims to model such molecular movement using a motion framework originally developed for robotic applications called the Probabilistic Roadmap Method. The Probabilistic Roadmap Method builds a graph, or roadmap, to model the connectivity of the movable object?s valid motion space. We previously applied this methodology to study protein folding and obtained promising results for several small proteins. Here, we extend our existing protein folding framework to handle larger proteins and to study a broader range of motion problems. We present a methodology for incrementally constructing roadmaps until they satisfy a set of evaluation criteria. We show the generality of this scheme by providing evaluation criteria for two types of motion problems: protein folding and protein transitions. Incremental Map Generation eliminates the burden of selecting a sampling density which in practice is highly sensitive to the protein under study and difficult to select. We also generalize the roadmap construction process to be biased towards multiple conformations of interest thereby allowing it to model transitions, i.e., motions between multiple known conformations, instead of just folding to a single known conformation. We provide evidence that this generalized motion framework models large-scale conformational change more realistically than competing methods. We use rigidity theory to increase the efficiency of roadmap construction by introducing a new sampling scheme and new distance metrics. It is only with these rigidity-based techniques that we were able to detect subtle folding differences between a set of structurally similar proteins. We also use it to study several problems related to protein motion including distinguishing secondary structure formation order, modeling hydrogen exchange, and folding core identification. We compare our results to both experimental data and other computational methods.Item Roadmap-Based Techniques for Modeling Group Behaviors in Multi-Agent Systems(2012-07-16) Rodriguez, Samuel OscarSimulating large numbers of agents, performing complex behaviors in realistic environments is a difficult problem with applications in robotics, computer graphics and animation. A multi-agent system can be a useful tool for studying a range of situations in simulation in order to plan and train for actual events. Systems supporting such simulations can be used to study and train for emergency or disaster scenarios including search and rescue, civilian crowd control, evacuation of a building, and many other training situations. This work describes our approach to multi-agent systems which integrates a roadmap-based approach with agent-based systems for groups of agents performing a wide range of behaviors. The system that we have developed is highly customizable and allows us to study a variety of behaviors and scenarios. The system is tunable in the kinds of agents that can exist and parameters that describe the agents. The agents can have any number of behaviors which dictate how they react throughout a simulation. Aspects that are unique to our approach to multi-agent group behavior are the environmental encoding that the agents use when navigating and the extensive usage of the roadmap in our behavioral framework. Our roadmap-based approach can be utilized to encode both basic and very complex environments which include multi- level buildings, terrains and stadiums. In this work, we develop techniques to improve the simulation of multi-agent systems. The movement strategies we have developed can be used to validate agent movement in a simulated environment and evaluate building designs by varying portions of the environment to see the effect on pedestrian flow. The strategies we develop for searching and tracking improve the ability of agents within our roadmap-based framework to clear areas and track agents in realistic environments. The application focus of this work is on pursuit-evasion and evacuation planning. In pursuit-evasion, one group of agents, the pursuers, attempts to find and capture another set of agents, the evaders. The evaders have a goal of avoiding the pursuers. In evacuation planning, the evacuating agents attempt to find valid paths through potentially complex environments to a safe goal location determined by their environmental knowledge. Another group of agents, the directors may attempt to guide the evacuating agents. These applications require the behaviors created to be tunable to a range of scenarios so they can reflect real-world reactions by agents. They also potentially require interaction and coordination between agents in order to improve the realism of the scenario being studied. These applications illustrate the scalability of our system in terms of the number of agents that can be supported, the kinds of realistic environments that can be handled, and behaviors that can be simulated.Item Techniques for modeling and analyzing RNA and protein folding energy landscapes(2009-05-15) Tang, XinyuRNA and protein molecules undergo a dynamic folding process that is important to their function. Computational methods are critical for studying this folding pro- cess because it is difficult to observe experimentally. In this work, we introduce new computational techniques to study RNA and protein energy landscapes, includ- ing a method to approximate an RNA energy landscape with a coarse graph (map) and new tools for analyzing graph-based approximations of RNA and protein energy landscapes. These analysis techniques can be used to study RNA and protein fold- ing kinetics such as population kinetics, folding rates, and the folding of particular subsequences. In particular, a map-based Master Equation (MME) method can be used to analyze the population kinetics of the maps, while another map analysis tool, map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways from the map. To validate the results, I compared our methods with other computational meth- ods and with experimental studies of RNA and protein. I first compared our MMC and MME methods for RNA with other computational methods working on the com- plete energy landscape and show that the approximate map captures the major fea- tures of a much larger (e.g., by orders of magnitude) complete energy landscape. Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental findings. I present comparisons with two experimental cases to show how I can pre- dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their mutants using our MME and MMC tools respectively. I also show that the MME and MMC tools can be applied to map-based approximations of protein energy energy landscapes and present kinetics analysis results for several proteins.