Abstract The protein‐folding problem is a study of how a protein dynamically folds to its so‐called native state—an energetically stable, three‐dimensional conformation. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. We have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein's potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial trajectory. In this paper we report on our experiences parallelizing our method using STAPL (Standard Template Adaptive Parallel Library) that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version will enable us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms, ranging from small Linux clusters to massively parallel machines such as IBM's BlueGene/L, without user code modification. Copyright © 2005 John Wiley & Sons, Ltd.

Given a robot and a workspace, probabilistic roadmap planners (PRMs) build a roadmap of paths sampled from the workspace. A roadmap node is a single collision-free robot configuration, randomly generated. A roadmap edge is a sequence of collision-free robot configurations which interpolate the path from one roadmap node to another. Queries to the roadmap are (start, goal) pairs. If both the start and goal of a pair can be connected to the same connected component of the roadmap, the query is solved. Many promising variants of the PRM have been proposed, each with their own strengths and weaknesses. We propose a meta-planner for using many PRMs in such a way that the strengths are combined and the weaknesses offset. Our meta-planner will perform the combination in the following manner: i) provide a framework in which different motion planners are available and to which new ones are easily added; ii) characterize subregions (possibly overlapping) based on sample characteristics and connection results; iii) assign subregions to one or more planners which are judged promising; and iv) provide stopping criteria for roadmap construction. We present experimental results for four characterization measures. A general technique we call 'filtering' is presented for keeping roadmaps compact.

This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for planning the motion of rigid objects in three-dimensional workspaces. The study concentrates on cluttered three-dimensional workspaces typical of, for example, virtual prototyping applications such as maintainability studies in mechanical CAD designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for such applications. Our study of distance metrics shows that the importance of the translational distance increases relative to the rotational distance as the environment becomes more crowded. We find that each local planner makes some connections that none of the others does-indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method we call rotate-at-s that often outperforms the common straight-line in C-space method in crowded environments.

In this paper we report on our experience in parallelizing probabilistic roadmap motion planning methods (PRMs). We show that significant, scalable speed-ups can be obtained with relatively little effort on the part of the developer. Our experience is not limited to PRMs. In particular, we outline general techniques for parallelizing types of computations commonly performed in motion planning algorithms, and identify potential difficulties that might be faced in other efforts to parallelize sequential motion planning methods.

This paper presents a comparative evaluation of different distance metrics and local planners within the content of probabilistic roadmap methods for motion planning. Both C-space and workspace distance metrics and local planners are considered. The study concentrates on cluttered 3D workspaces, typical of mechanical designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. We find that each local planner makes some connections than none of the others do ndicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method, we call rotate-at-s, that outperforms the common straight-line in C-space method in crowded environments.