21 research outputs found

    Metrics for sampling-based motion planning

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    A motion planner finds a sequence of potential motions for a robot to transit from an initial to a goal state. To deal with the intractability of this problem, a class of methods known as sampling-based planners build approximate representations of potential motions through random sampling. This selective random exploration of the space has produced many remarkable results, including solving many previously unsolved problems. Sampling-based planners usually represent the motions as a graph (e.g., the Probabilistic Roadmap Methods or PRMs), or as a tree (e.g., the Rapidly exploring Random Tree or RRT). Although many sampling-based planners have been proposed, we do not know how to select among them because their different sampling biases make their performance depend on the features of the planning space. Moreover, since a single problem can contain regions with vastly different features, there may not exist a simple exploration strategy that will perform well in every region. Unfortunately, we lack quantitative tools to analyze problem features and planners performance that would enable us to match planners to problems. We introduce novel metrics for the analysis of problem features and planner performance at multiple levels: node level, global level, and region level. At the node level, we evaluate how new samples improve coverage and connectivity of the evolving model. At the global level, we evaluate how new samples improve the structure of the model. At the region level, we identify groups or regions that share similar features. This is a set of general metrics that can be applied in both graph-based and tree-based planners. We show several applications for these tools to compare planners, to decide whether to stop planning or to switch strategies, and to adjust sampling in different regions of the problem

    Physically-based sampling for motion planning

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    Motion planning is a fundamental problem with applications in a wide variety of areas including robotics, computer graphics, animation, virtual prototyping, medical simulations, industrial simulations, and trac planning. Despite being an active area of research for nearly four decades, prior motion planning algorithms are unable to provide adequate solutions that satisfy the constraints that arise in these applications. We present a novel approach based on physics-based sampling for motion planning that can compute collision-free paths while also satisfying many physical constraints. Our planning algorithms use constrained simulation to generate samples which are biased in the direction of the nal goal positions of the agent or agents. The underlying simulation core implicitly incorporates kinematics and dynamics of the robot or agent as constraints or as part of the motion model itself. Thus, the resulting motion is smooth and physically-plausible for both single robot and multi-robot planning. We apply our approach to planning of deformable soft-body agents via the use of graphics hardware accelerated interference queries. We highlight the approach with a case study on pre-operative planning for liver chemoembolization. Next, we apply it to the case of highly articulated serial chains. Through dynamic dimensionality reduction and optimized collision response, we can successfully plan the motion of \\snake-like robots in a practical amount of time despite the high number of degrees of freedom in the problem. Finally, we show the use of the approach for a large number of bodies in dynamic environments. By applying our approach to both global and local interactions between agents, we can successfully plan for thousands of simple robots in real-world scenarios. We demonstrate their application to large crowd simulations

    Algorithmes pour le (dés)assemblage d'objets complexes et applications à la biologie structurale

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    La comprĂ©hension et la prĂ©diction des relations structure-fonction de protĂ©ines par des approches in sillico reprĂ©sentent aujourd'hui un challenge. MalgrĂ© le dĂ©veloppement rĂ©cent de mĂ©thodes algorithmiques pour l'Ă©tude du mouvement et des interactions molĂ©culaires, la flexibilitĂ© de macromolĂ©cules reste largement hors de portĂ©e des outils actuels de modĂ©lisation molĂ©culaire. L'objectif de cette thĂšse est de dĂ©velopper une nouvelle approche basĂ©e sur des algorithmes de planification de mouvement issus de la robotique pour mieux traiter la flexibilitĂ© molĂ©culaire dans l'Ă©tude des interactions protĂ©iques. Nous avons Ă©tendu un algorithme rĂ©cent d'exploration par Ă©chantillonnage alĂ©atoire, ML-RRT pour le dĂ©sassemblage d'objets articulĂ©s complexes. Cet algorithme repose sur la dĂ©composition des paramĂštres de configuration en deux sous-ensembles actifs et passifs, qui sont traitĂ©s de maniĂšre dĂ©couplĂ©e. Les extensions proposĂ©es permettent de considĂ©rer plusieurs degrĂ©s de mobilitĂ© pour la partie passive, qui peut ĂȘtre poussĂ©e ou attirĂ©e par la partie active. Cet outil algorithmique a Ă©tĂ© appliquĂ© avec succĂšs pour l'Ă©tude des changements conformationnels de protĂ©ines induits lors de la diffusion d'un ligand. A partir de cette extension, nous avons dĂ©veloppĂ© une nouvelle mĂ©thode pour la rĂ©solution simultanĂ©e du sĂ©quençage et des mouvements de dĂ©sassemblage entre plusieurs objets. La mĂ©thode, nommĂ©e Iterative-ML-RRT, calcule non seulement les trajectoires permettant d'extraire toutes les piĂšces d'un objet complexe assemblĂ©, mais Ă©galement l'ordre permettant le dĂ©sassemblage. L'approche est gĂ©nĂ©rale et a Ă©tĂ© appliquĂ©e pour l'Ă©tude du processus de dissociation de complexes macromolĂ©culaires en introduisant une fonction d'Ă©valuation basĂ©e sur l'Ă©nergie d'interaction. Les rĂ©sultats prĂ©sentĂ©s dans cette thĂšse montrent non seulement l'efficacitĂ© mais aussi la gĂ©nĂ©ralitĂ© des algorithmes proposĂ©s. ABSTRACT : Understanding and predicting structure-function relationships in proteins with fully in silico approaches remain today a great challenge. Despite recent developments of computational methods for studying molecular motions and interactions, dealing with macromolecular flexibility largely remains out of reach of the existing molecular modeling tools. The aim of this thesis is to develop a novel approach based on motion planning algorithms originating from robotics to better deal with macromolecular flexibility in protein interaction studies. We have extended a recent sampling-based algorithm, ML-RRT, for (dis)-assembly path planning of complex articulated objects. This algorithm is based on a partition of the configuration parameters into active and passive subsets, which are then treated in a decoupled manner. The presented extensions permit to consider different levels of mobility for the passive parts that can be pushed or pulled by the motion of active parts. This algorithmic tool is successfully applied to study protein conformational changes induced by the diffusion of a ligand inside it. Building on the extension of ML-RRT, we have developed a novel method for simultaneously (dis)assembly sequencing and path planning. The new method, called Iterative-ML-RRT, computes not only the paths for extracting all the parts from a complex assembled object, but also the preferred order that the disassembly process has to follow. We have applied this general approach for studying disassembly pathways of macromolecular complexes considering a scoring function based on the interaction energy. The results described in this thesis prove not only the efficacy but also the generality of the proposed algorithm

    (Dis)assembly path planning for complex objects and applications to structural biology

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    Understanding and predicting structure-function relationships in proteins with fully in silico approaches remain today a great challenge. Despite recent developments of computational methods for studying molecular motions and interactions, dealing with macromolecular flexibility largely remains out of reach of the existing molecular modeling tools. The aim of this thesis is to develop a novel approach based on motion planning algorithms originating from robotics to better deal with macromolecular flexibility in protein interaction studies. We have extended a recent sampling-based algorithm, ML-RRT, for (dis)-assembly path planning of complex articulated objects. This algorithm is based on a partition of the configuration parameters into active and passive subsets, which are then treated in a decoupled manner. The presented extensions permit to consider different levels of mobility for the passive parts that can be pushed or pulled by the motion of active parts. This algorithmic tool is successfully applied to study protein conformational changes induced by the diffusion of a ligand inside it. Building on the extension of ML-RRT, we have developed a novel method for simultaneously (dis)assembly sequencing and path planning. The new method, called Iterative-ML-RRT, computes not only the paths for extracting all the parts from a complex assembled object, but also the preferred order that the disassembly process has to follow. We have applied this general approach for studying disassembly pathways of macromolecular complexes considering a scoring function based on the interaction energy. The results described in this thesis prove not only the efficacy but also the generality of the proposed algorithm

    Parallel transit methods for arterial spin labelling magnetic resonance imaging

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    Vessel selective arterial spin labelling (ASL) is a magnetic resonance imaging technique which permits the visualisation and assessment of the perfusion territory of a specific set of feeding arteries. It is of clinical importance in both acute and chronic cerebrovascular disease, and the mapping of blood supplied to tumours. Continuous ASL is capable of providing the highest signal-to-noise (SNR) ratio of the various ASL methods. However on clinical systems it suffers from high hardware demands, and the control of systematic errors decreases perfusion sensitivity. A separate labelling coil avoids these problems, enabling high labelling efficiency and subsequent high SNR, and vessel specificity can be localised to one carotid artery. However this relies on the careful and accurate positioning of the labelling coil over the common carotid arteries in the neck. It is proposed to combine parallel transmission (multiple transmit coils, each transmitting with different amplitudes and phases) to spatially tailor the labelling field, removing the reliance on coil location for optimal labelling efficiency, and enabling robust vessel selective labelling with a high degree of specificity. Presented is the application of parallel transmission methods to continuous ASL, requiring the development of an ASL labelling coil array, and a two channel transmitter system. Coil safety testing was performed using a novel MRI temperature mapping technique to accurately measure small temperature changes on the order of 0.1 ⁰C. A perfusion phantom with distinct vascular territories was constructed for sequence testing and development. Phantom and in-vivo testing of parallel transmit CASL using a 3D-GRASE acquisition showed an improvement of up to 35% in vessel specificity when compared with using a single labelling coil, whilst retaining the high labelling efficiency and associated SNR of separate coil CASL methods

    Real-Time Path Planning for Automating Optical Tweezers based Particle Transport Operations

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    Optical tweezers (OT) have been developed to successfully trap, orient, and transport micro and nano scale components of many different sizes and shapes in a fluid medium. They can be viewed as robots made out of light. Components can be simply released from optical traps by switching off laser beams. By utilizing the principle of time sharing or holograms, multiple optical traps can perform several operations in parallel. These characteristics make optical tweezers a very promising technology for creating directed micro and nano scale assemblies. In the infra-red regime, they are useful in a large number of biological applications as well. This dissertation explores the problem of real-time path planning for autonomous OT based transport operations. Such operations pose interesting challenges as the environment is uncertain and dynamic due to the random Brownian motion of the particles and noise in the imaging based measurements. Silica microspheres having diameters between (1-20) ”m are selected as model components. Offline simulations are performed to gather trapping probability data that serves as a measure of trap strength and reliability as a function of relative position of the particle under consideration with respect to the trap focus, and trap velocity. Simplified models are generated using Gaussian Radial Basis Functions to represent the data in a compact form. These metamodels can be queried at run-time to obtain estimated probability values accurately and efficiently. Simple trapping probability models are then utilized in a stochastic dynamic programming framework to compute optimum trap locations and velocities that minimizes the total, expected transport time by incorporating collision avoidance and recovery steps. A discrete version of an approximate partially observable Markov decision process algorithm, called the QMDP_NLTDV algorithm, is developed. Real-time performance is ensured by pruning the search space and enhancing convergence rates by introducing a non-linear value function. The algorithm is validated both using a simulator as well as a physical holographic tweezer set-up. Successful runs show that the automated planner is flexible, works well in reasonably crowded scenes, and is capable of transporting a specific particle to a given goal location by avoiding collisions either by circumventing or by trapping other freely diffusing particles. This technique for transporting individual particles is utilized within a decoupled and prioritized approach to move multiple particles simultaneously. An iterative version of a bipartite graph matching algorithm is also used to assign goal locations to target objects optimally. As in the case of single particle transport, simulation and some physical experiments are performed to validate the multi-particle planning approach

    Brain and Human Body Modeling

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    This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

    Brain and Human Body Modeling

    Get PDF
    This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields
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