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Sampling-based planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidly-exploring Random Trees (RRTs) have generated highly successful single-query planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the(More)
Sampling based planners have become increasingly efficient in solving the problems of classical motion planning and its applications. In particular, techniques based on the rapidly-exploring random trees (RRTs) have generated highly successful single-query planners. Recently, a variant of this planner called dynamic-domain RRT was introduced by Yershova et(More)
This paper presents a path planner for robots operating in dynamically changing environments with both static and moving obstacles. The proposed planner is based on probabilistic path planning techniques and it combines techniques originally designed for solving multiple-query and single-query problems. The planner first starts with a preprocessing stage(More)
This paper presents a new method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new(More)
In this work, a new method for exploring conformational energy landscapes is described. The method, called transition-rapidly exploring random tree (T-RRT), combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double(More)
This paper addresses the motion planning problem while considering Human-Robot Interaction (HRI) constraints. The proposed planner generates collision-free paths that are acceptable and legible to the human. The method extends our previous work on human-aware path planning to cluttered environments. A randomized cost-based exploration method provides an(More)
This paper describes a new approach to sampling-based motion planning with PRM methods. Our aim is to compute good quality roadmaps that encode the multiple connectedness of the configuration space inside small but yet representative graphs that capture well the different varieties of free paths. The proposed Path Deformation Roadmaps (PDR) rely on a notion(More)
This paper addresses the problem of computing pathways for a ligand to exit from the active site of a protein. Such problem can be formulated as a mechanical disassembly problem for two articulated objects. Its solution requires searching paths in a constrained high-dimensional configuration-space. Indeed, the ligand passageway inside the protein is often(More)
Existing sampling-based robot motion planning methods are often inefficient at finding trajectories for kinodynamic systems, especially in the presence of narrow passages between obstacles and uncertainty in control and sensing. To address this, we propose EG-RRT, an Environment-Guided variant of RRT designed for kinodynamic robot systems that combines(More)
—This paper presents an approach for optimal path planning on implicitly-defined configuration spaces such as those arising, for instance, when manipulating an object with two arms or with a multifingered hand. In this kind of situations, the kinematic and contact constraints induce configuration spaces that are manifolds embedded in higher dimensional(More)