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Abstract

The paper describes a new method for simultaneously (dis)assembly sequencing and path planning. Indeed, both are parts of a same problem that can be formulated in a general path planning framework. Based on this formulation, the algorithm proposed in this paper extends a popular sampling-based path planner, RRT, to solve general (dis)assembly planning problems involving objects with arbitrary shapes, and possibly requiring non-monotonic (dis)assembly sequences. The method does not require complex geometric computations, and is easy to implement. Experimental results show the efficiency of the method for solving a large class of problems.

Abstract

In this paper we describe a new approach to sampling-based motion planning with Probabilistic Roadmap Planner (PRM) methods. Our aim is to compute good quality roadmaps which encode the multiple connectedness of the configuration space inside small but yet representative graphs which capture the different varieties of free paths well. The proposed Path Deformation Roadmaps (PDRs) rely on a notion of path deformability indicating whether or not a given path can be continuously deformed into another existing path. By considering a simpler form of deformation than that allowed between homotopic paths, we propose a method that extends the Visibility-PRM technique to construct compact roadmaps which encode richer and more suitable information than representative paths of the homotopy classes. PDRs contain additional useful cycles between paths in the same homotopy class that can be hardly deformed into each other. Experimental results show that the technique enables small roadmaps to reliably capture the multiple connectedness of complex spaces in various problems involving free-flying and articulated robots in both two- and three-dimensional environments.

Abstract

This paper presents a method to compute collision-free coordinated manipulation paths for multi-arm robot systems. The method extends previous work [8] by incorporating an explicit treatment of singular configurations, which permit to find solution paths requiring the robot reconfiguration. Such reconfiguration motions are neglected in most of related work. The performance of the planner is analyzed through experiments with academic examples. The generality of the approach is demonstrated by its application to a model of the complex DLR's Justin robot.

Abstract

A novel approach based on efficient path-planning algorithms was applied to investigate the influence of substrate access on Burkholderia cepacia lipase enantioselectivity. The system studied was the transesterification of 2-substituted racemic acid derivatives catalysed by B. cepacia lipase. In silico data provided by this approach showed a fair qualitative agreement with experimental results, and hence the potential of this computational method for fast screening of racemates. In addition, a collision detector algorithm used during the pathway searches enabled the rapid identification of amino acid residues hindering the displacement of substrates along the deep, narrow active-site pocket of B. cepacia lipase and thus provided valuable information to guide the molecular engineering of lipase enantioselectivity.

Mots-Clés / Keywords

Abstract

Sampling-based path planning algorithms are powerful tools for computing constrained disassembly motions. This paper presents a variant of the Rapidly-exploring Random Tree (RRT) algorithm particularly devised for the disassembly of objects with articulated parts. Configuration parameters generally play two different roles in this type of problems: some of them are essential for the disassembly task, while others only need to move if they hinder the progress of the disassembly process. The proposed method is based on such a partition of the configuration parameters. Results show a remarkable performance improvement as compared to standard path planning techniques. The paper also shows practical applications of the presented algorithm in robotics and structural bioinformatics.

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Abstract

This paper presents a new method for computing macromolecular motions based on the combination of path planning algorithms, originating from robotics research, and elastic network normal mode analysis. The low-frequency normal modes are regarded as the collective degrees of freedom of the molecule. Geometric path planning algorithms are used to explore these collective degrees of freedom in order to find possible large-amplitude conformational changes. To overcome the limits of the harmonic approximation, which is valid in the vicinity of the minimum energy structure, and to get larger conformational changes, normal mode calculations are iterated during the exploration. Initial results show the efficiency of our method, which requires a small number of normal mode calculations to compute large-amplitude conformational transitions in proteins. A detailed analysis is presented for the computed transition between the open and closed structures of adenylate kinase. This transition, important for its biological function, involves large-amplitude domain motions. The obtained motion correlates well with results presented in related works.

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Abstract

Robot navigation in the presence of humans raises new issues for motion planning and control when the humans must be taken explicitly into account. We claim that a human aware motion planner (HAMP) must not only provide safe robot paths, but also synthesize good, socially acceptable and legible paths. This paper focuses on a motion planner that takes explicitly into account its human partners by reasoning about their accessibility, their vision field and their preferences in terms of relative human-robot placement and motions in realistic environments. This planner is part of a human-aware motion and manipulation planning and control system that we aim to develop in order to achieve motion and manipulation tasks in the presence or in synergy with humans.

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