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Probabilistic Roadmap Methods (PRMs) are widely used motion planning methods that sample robot configurations (nodes) and connect them to form a graph (roadmap) containing feasible trajectories. Many PRM variants propose different strategies for each of the steps and choosing among them is problem dependent. Planning in heterogeneous environments and/or on(More)
Protein folding plays an essential role in protein function and stability. Despite the explosion in our knowledge of structural and functional data, our understanding of protein folding is still very limited. In addition, methods such as folding core identification are gaining importance with the increased desire to engineer proteins with particular(More)
Probabilistic Roadmap Methods (PRMs) solve the motion planing problem by constructing a roadmap (or graph) that models the motion space when feasible local motions exist. PRMs and variants contain several phases during roadmap generation i.e., sampling, connection, and query. Some work has been done to apply machine learning to the connection phase to(More)
Modeling large-scale protein motions, such as those involved in folding and binding interactions, is crucial to better understanding not only how proteins move and interact with other molecules but also how proteins misfold, thus causing many devastating diseases. Robotic motion planning algorithms, such as Rapidly Exploring Random Trees (RRTs), have been(More)
Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the(More)
— Sampling-Based Motion Planning (SBMP) has been successful in planning the motion for a wide variety of robot types. An important primitive of these methods is selecting candidate neighbors and validating/invalidating the pathways between nodes of the map. These neighbors are commonly selected based on some distance metric (DM). An ideal distance metric(More)
Sampling-based motion planning has been successful in planning the motion for a wide variety of robot types. An important primitive of these methods involves connecting nodes by selecting candidate neighbors and checking the path between them. Recently, an approach called Adaptive Neighbor Connection (ANC) was proposed to automate neighbor selection using(More)
• Adaptive methods for robotic motion planning using machine learning. • Robotic motion planning algorithms and their application to computational biology problems such as protein folding and RNA folding. Fellowship awarded to doctoral students in good academic standing who intend to pursue an academic career. Fellowship awarded to women from developing(More)
—In this paper we present a simulated annealing-based method for planning efficient paths with a tether which avoid entanglement in an obstacle-filled environment. By evaluating total path cost as a function of both path length and entanglements, a robot can plan a path through multiple points of interest while avoiding becoming entangled in any obstacle.(More)
2 ABSTRACT Every motion made by a moving object is either planned implicitly, e.g., human natural movement from one point to another, or explicitly, e.g., pre-planned information about where a robot should move in a room to effectively avoid colliding with obstacles. Motion planning is a well studied concept in robotics and it involves moving an object from(More)
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