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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 in [28]. It(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 problems 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(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)
—This paper addresses path planning considering a cost function defined over the configuration space. The proposed Transition-based RRT planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of RRTs with transition tests used in stochastic optimization methods to accept(More)
475 each represents the command sequence of one user grasping one object. These records along with other sensory data, like vision and tactile data, can then be used as input data to a learning system that can learn how to grasp objects from actual grasping experiments. Moussa [13], [14] tested such a grasping system in a simulated environment using an(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)
— In this paper we introduce a method based on the Probabilistic Roadmap (PRM) Planner to construct robust roadmaps for motion planning in changing environments. PRM's are usually aimed at static environments. In reality though, many environments are not static, but contain moving obstacles as well. Often the motion of these obstacles is not unconstrained,(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)