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Proceedings ArticleDOI

A two level fuzzy PRM for manipulation planning

01 Jan 2000-Vol. 3, pp 1716-1721
TL;DR: An algorithm which extends the probabilistic roadmap (PRM) framework to handle manipulation planning by using a two level approach, a PRM of PRMs, made possible by the introduction of a new kind of roadmap, called the fuzzy roadmap.
Abstract: This paper presents an algorithm which extends the probabilistic roadmap (PRM) framework to handle manipulation planning. This is done by using a two level approach, a PRM of PRMs. The first level builds a manipulation graph, whose nodes represent stable placements of the manipulated objects while the edges represent transfer and transit actions. The actual motion planning for the transfer and transit paths is done by PRM planners at the second level. The approach is made possible by the introduction of a new kind of roadmap, called the fuzzy roadmap. The fuzzy roadmap contains edges which are not verified by a local planner during construction. Instead, each edge is assigned a number which represents the probability that it is feasible. Later, if the edge is part of a solution path, the edge is checked for collisions. The overall effect is that our roadmaps evolve iteratively until they contain a solution. The use of fuzzy roadmaps in both levels of our manipulation planner offers many advantages. At the first level, a fuzzy roadmap represents the manipulation graph and addresses the problem of having probabilistically complete planners at the second level. At the second level, fuzzy roadmaps drastically reduce the number of collision checks. The paper contains experimental results demonstrating the feasibility and efficiency of our scheme.

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Citations
More filters
01 Jan 2011
TL;DR: The approach uses an instantiation of a multi-layered synergistic planning framework that has been proposed recently that results in computational speedups of close to 10 times over earlier approaches for second-order nonlinear robot models in challenging workspace environments with obstacles.
Abstract: This paper describes an approach for solving motion planning problems for mobile robots involving temporal goals. The temporal goals are described over subsets of the workspace (called propositions) using temporal logic. The approach uses an instantiation of a multi-layered synergistic planning framework that has been proposed recently. In this framework, a high-level planner constructs high-level plans using a discrete abstraction of the system, the temporal logic specifications and the low- level exploration information. A low-level sampling-based planner uses the suggested high-level plans and the dynamics of the system to explore the state-space of the system for feasible trajectories satisfying the specification. The construction and exploration of the discrete abstraction are critical issues that affect the overall performance of the approach. A geometry-based approach for constructing the abstraction, and a lazy high-level search technique for its exploration are discussed. The proposed techniques result in computational speedups of close to 10 times over earlier approaches for second-order nonlinear robot models in challenging workspace environments with obstacles and for a variety of temporal logic specifications.

3 citations


Cites methods from "A two level fuzzy PRM for manipulat..."

  • ...The framework is inspired by earlier works [61], [62] that introduced a discrete...

    [...]

Dissertation
01 Jan 2015
TL;DR: This thesis addresses three main problems that permeate protein modeling research and proposes novel probabilistic algorithms to address critical open problems in computational structural biology regarding the relationship between structure, dynamics, and function in protein molecules.
Abstract: PROBABILISTIC ALGORITHMS FOR MODELING PROTEIN STRUCTURE AND DYNAMICS Kevin P Molloy, PhD George Mason University, 2015 Dissertation Director: Dr. Amarda Shehu This thesis proposes novel probabilistic algorithms to address critical open problems in computational structural biology regarding the relationship between structure, dynamics, and function in protein molecules. The focus on protein modeling research is warranted due to the ubiquity and central role of proteins in life-critical processes in the living cell. A study of protein molecules is important for understanding our biology and health. Many disorders in the sick cell are proteinopathies, where a protein disrupts a chemical process, causing the cell to deviate from its intended biological activity. However, unlike other life-critical macromolecules, such as DNA and RNA, where significant information about activity can be extracted from knowledge of the ordering of the constitutive building blocks, proteins exhibit a more complex relationship between the order of building blocks, the structures arising from spatial arrangements of the building blocks in three-dimensional space, and the determination from such arrangements of biological activity. Since studies of proteins pose exceptional challenges in wet laboratories, the work presented in this thesis proposes powerful algorithms to complement wet-laboratory research on understanding the relationship between structure, dynamics, and function in protein molecules. Specifically, this thesis addresses three main problems that permeate protein modeling research. The first problem, known as “from-structure-to-function,” asks how to infer the function of a protein from knowledge of its active structure. The second problem, known as “from-sequence-to-structure,” relates to the open question of how to predict the biologically-active structure of a protein when provided information on the identities and order of constitutive building blocks. The third problem advances the current computational treatment of proteins to alleviate assumptions of their rigidity and instead model them as dynamic macromolecules switching between structures to tune their biological activity. The objective here is to model protein dynamics efficiently by computing the molecular motions employed in structural transitions among diverse functionally-relevant states of a protein. The algorithmic techniques employed in this thesis span machine learning, computational geometry, and stochastic optimization. In particular, we combine computational geometry and machine learning in a novel framework to infer the function of a protein from knowledge of its structure. In our treatment of the de novo structure prediction problem, we employ and investigate in detail an adaptive stochastic optimization framework capable of balancing between search breadth and depth in the exploration of a high-dimensional and nonlinear search space. We pursue such frameworks further and propose novel roboticsinspired probabilistic algorithms to model protein dynamics. In particular, in our treatment of structure and dynamics, we exploit analogies between protein modeling and the motion planning problem in robotics, which allow us to employ relevant concepts from motion planning algorithms and propose powerful algorithms capable of handling highly-constrained articulated systems with hundreds or thousands of continuous and discrete variables. This thesis advances protein modeling research by extending the size and complexity of systems that can be modeled, as well as the detail and accuracy with which relevant biological questions can be answered. For instance, algorithms proposed here to model structural transitions are now able to explain the impact of sequence mutations on protein function. Just as important, the algorithmic techniques proposed in this thesis are of general utility to other domains in computer science focusing on extending optimization algorithms for vast and nonlinear search spaces of complex systems. Chapter 1: Introduction This thesis proposes novel algorithms to unravel the relationship between structure, dynamics, and function in protein molecules. The focus on proteins is warranted for three main reasons. First, proteins play a central role in virtually every chemical process in the living cell [6]. Second, many disorders in the sick cell are already characterized as proteinopathies, where a protein that is central to a chemical process deviates from its intended biological activity [7–10]. Third, unlike other life-critical macromolecules such as DNA and RNA, where significant information about biological activity can be extracted from knowledge of the order of the constitutive building blocks, in proteins there is a more complex relationship between the order of building blocks, their arrangement in three-dimensional space under physiological conditions, and the determination from such an arrangement of biological activity or protein function [11]. For these reasons, a study of protein molecules is both central to molecular biology and our health. More importantly, studies of proteins pose exceptional challenges both in the wet and dry laboratories. In this thesis, we focus on the computational challenges, as our goal is to propose algorithms to complement and aid wet-laboratory investigations. Specifically, this thesis addresses three main problems that currently permeate protein modeling research in computational biology. The first problem, which we address in chapter 3, relates to the open question of how to infer what the function of a given protein is when provided information on the placement of its building blocks in three-dimensional space under physiological conditions, otherwise known as protein structure. This is often known as the “from-structure-to-function” question in computational biology, and in chapter 3 we propose a machine learning approach to address this problem. The second problem, which we address in chapter 4, relates to the open question of how to predict the structure of a protein when provided information on the identities and order of building blocks 1 in the protein chain. This is often known as the “de novo structure prediction problem” and we investigate a robotics-inspired stochastic optimization framework for its ability to balance computational efficiency and accuracy when addressing this problem. The third problem, which we address and study in detail in chapters 5, 6, and 7 advances the current computational treatment of proteins to alleviate assumptions of their rigidity. Indeed, in chapter 5, we model proteins as dynamic macromolecules, and propose a novel roboticsinspired tree-based search framework to compute motions of proteins between two distinct functionally-relevant structures. We pursue this line of investigation deeper in chapter 6, where we demonstrate the promise of combining continuous and discrete modeling in extracting information about structural transition in healthy and aberrant forms of a protein central to many human cancers. In chapter 7 we pursue further a novel algorithmic framework for the computation of structural transitions in proteins and identify both important advances and remaining challenges. It is worth noting that the problems addressed in this thesis remain open in computational biology. More importantly, they pose interesting and challenging settings for novel algorithmic research. In this way, while the research described in this thesis is driven by specific open questions in computational and molecular biology, the algorithms described here make important contributions in computer science, as the study of biologically-realistic systems such as proteins exposes challenging systems where novel modeling and simulation algorithms need to be devised. Such a setting is unforgiving; not only do the algorithms need to be computationally efficient, but they also have to be able to perform well on realistic systems and generate data that can be trusted to make decisions. It is worth noting that computational research in macromolecular modeling research has recently gained an important place in science; all 2013 Nobel laureates in chemistry represented computational research in macromolecular modeling and simulation. The foundation of this thesis is that protein structure determines protein function. This was demonstrated early, by Anfinsen’s experiments [12]. The central role of protein structure is not surprising, as biological activity of a protein molecule is the result of binding with

3 citations

01 Jan 2007
TL;DR: The utility-guided framework for motion planning is extended to develop a planner that can successfully plan despite inaccuracies in its perception of the environment and to guide further sensing to reduce uncertainty and maximally improve the utility of the path.
Abstract: Robots already impact the way we understand our world and live our lives. However, their impact and use is limited by the skills they possess. Currently deployed autonomous robots lack the manipulation skills possessed by humans. To achieve general autonomy and applicability in the real world, robots must possess such skills. Autonomous manipulation requires algorithms that rapidly and reliably compute collision-free motion for robotic limbs with many degrees of freedom. Unfortunately, adequate algorithms for this task do not currently exist. Though there are many dimensions of the real-world planning task that require further research. A central problem of reliable real-world planning is that planners must rely on incomplete and inaccurate information about the world in which they are planning. The motion planning problem has exponential complexity in the robot's degrees of freedom. Consequently, the most successful planning algorithms use incomplete information obtained via sampling a subset of all possible movements. Additionally, real-world robots generally obtain information about the state of their environment through lasers, cameras and other sensors. The information obtained from these sensors contains noise and error. Thus the planner's incomplete information about the world is possibly inaccurate as well. Despite such limited information, a planner must be capable of quickly generating collision free motions to facilitate general purpose autonomous robots. This thesis proposes a new utility-guided framework for motion planning that can reliably compute collision-free motions with the efficiency required for real-world planning. The utility-guided approach begins with the observation there is regularity in space of possible motions available to a robot. Further, certain motions are more crucial than others for computing collision free paths. Together these observations form structure in the robot's space of possible movements. This structure provides a guide for the planner's exploration of possible motions. Because a complete understanding of this structure is computationally intractable, the utility-guided framework incrementally develops an approximate model discovered by past exploration. This model of the structure is used to select explorations that maximally benefit the planner. Information provided by each exploration improves the planner's approximation. The process of incremental improvement and further guided exploration iterates until an adequate model of configuration space is constructed. Discovering and exploiting structure in a robot's configuration space enables a utility-guided planner to achieve the performance and reliability required by real-world motion planning. This thesis describes applications of the utility-guided motion-planning framework to multi-query sampling-based roadmap and random-tree motion planning. Additionally, the utility-guided framework is extended to develop a planner that can successfully plan despite inaccuracies in its perception of the environment and to guide further sensing to reduce uncertainty and maximally improve the utility of the path.

3 citations


Cites background from "A two level fuzzy PRM for manipulat..."

  • ...While other methods [14, 21, 78] do this to improve computational efficiency, the presence of uncertainty introduces additional reasons why this is useful....

    [...]

  • ...FuzzyPRM [78] estimates the probability that each edge in the roadmap is obstructed or free....

    [...]

Journal ArticleDOI
TL;DR: This work presents solutions for multi-modal scalability in motion planning and task satisfaction, and presents an experience-based planning framework ALEF which reuses experience from similar modes both online and from training data.
Abstract: Robotic manipulation is inherently continuous, but typically has an underlying discrete structure, such as if an object is grasped. Many problems like these are multimodal, such as pick-and-place tasks where every object grasp and placement is a mode. Multimodal problems require finding a sequence of transitions between modes—for example, a particular sequence of object picks and placements. However, many multimodal planners fail to scale when motion planning is difficult (e.g., in clutter) or the task has a long horizon (e.g., rearrangement). This work presents solutions for multimodal scalability in both these areas. For motion planning, we present an experience-based planning framework alef which reuses experience from similar modes both online and from training data. For task satisfaction, we present a layered planning approach that uses a discrete lead to bias search toward useful mode transitions, informed by weights over mode transitions. Together, these contributions enable multimodal planners to tackle complex manipulation tasks that were previously infeasible or inefficient, and provide significant improvements in scenes with high-dimensional robots.

3 citations

Book ChapterDOI
01 Jan 2006
TL;DR: This survey reviews the main algorithms based on randomized sampling, outlining their advantages and drawbacks, as well as the knowledge recently acquired in the field.
Abstract: Algorithms based on randomized sampling proved to be the only viable algorithmic tool for quickly solving motion planning problems involving many degrees of freedom. Information on the configuration space is acquired by generating samples and finding simple paths among them. Paths and samples are stored in a suitable data structure. According to this paradigm, in the recent years a wide number of algorithmic techniques have been proposed and some approaches are now widely used. This survey reviews the main algorithms, outlining their advantages and drawbacks, as well as the knowledge recently acquired in the field.

3 citations


Cites methods from "A two level fuzzy PRM for manipulat..."

  • ...A similar technique, called fuzzy roadmap, has also been used while applying PRM like algorithms to manipulation problems ( [64])....

    [...]

References
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Journal ArticleDOI
01 Aug 1996
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Abstract: A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).

4,977 citations

Proceedings Article
01 Aug 1998
TL;DR: This paper presents a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms), which use randomization to construct a graph of representative paths in C-space whose vertices correspond to collision-free con gurations of the robot.
Abstract: Recently, a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms) have shown great potential for solving complicated high-dimensional problems. prms use randomization (usually during preprocessing) to construct a graph of representative paths in C-space (a roadmap) whose vertices correspond to collision-free con gurations of the robot and in which two vertices are connected by an edge if a path between the two corresponding con gurations can be found by a local planning method.

533 citations

Proceedings Article
01 Aug 1998
TL;DR: Y@lBEDGF HEIKJ L„DzMPORQSIUT MV@CW2X >A@CBEDGF HeIKJRX\[^]\X,L>z]  ~Uw ’Nw2x  “eUfa3Vx=
Abstract: .0/2143658709;:=A@CBEDGF HEIKJ L DNMPORQSIUT MV@CW2X >Y@CBEDZF HEIUJRX\[^]\X,L=>N] _?`N5a3P/(b-cYd,/21aeZ/Rfa3CN] vpw /Rxzy;{ |6/R:Y54w}_=/R~Kp€(‚wƒ<‚>Y@lBEDGF HEIUJ L„DNMPO QSI…T MV@lWmX >Y@lBEDGF HEIKJRX\[^]†XsL=>N] ‡†/ ˆ‰w2w21‹ŠŒp~‰Ž/ xN3CY@lBEDGF HEIKJ L„DzMPORQSIUT MV@CW2X >A@CBEDGF HEIKJRX\[^]\X,L>z]  ~Uw ‘N’Nw2x  “eUfa3Vx=Y@lBEDGF HEIKJ L„DzMPORQSIUT MV@CW2X >A@CBEDGF HEIKJRX\[^]\X,L>z]

364 citations

Proceedings Article
12 May 1995
TL;DR: This paper addresses the motion planning problem for a robot in presence of movable objects with an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions.
Abstract: This paper addresses the motion planning problem for a robot in presence of movable objects. Motion planning in this context appears as a constrained instance of the coordinated motion planning problem for multiple movable bodies. Indeed, a solution path in the configuration space of the robot and all movable objects is a sequence of transit paths where the robot moves alone and transfer paths where a movable object follows the robot. A major problem is to find the set of configurations where the robot has to grasp or release objects. The paper gives an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions. Two planners developed at LAAS-CNRS illustrate how the general formulation can be instantiated in specific cases.

175 citations

Proceedings Article
12 May 1995

160 citations