We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called Search and Explore, applied in an interleaved manner. Explore builds a representation of the accessible space while Search looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing

No abstract

No abstract

The goal of the work described in this paper is to build a path planner able to drive a robot in a dynamic environment where the obstacles are moving. In order to do so, we propose a method, called "ARIADNE'S CLEW algorithm", to build a global path planner based on the combination of two local planning algorithms : an EXPLORE algorithm and a SEARCH algorithm. The purpose of the EXPLORE algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the SEARCH algorithm is to opportunistically check if the target can be easily reached from any given placed landmark. The ARIADNE'S CLEW algorithm is shown to be very fast in most cases allowing planning in dynamic environments. Hence, it is shown to be complete, which means that it is sure to find a path when one exists. Finally, we describe a massively parallel implementation of this algorithm.

An emerging paradigm in solving the classical motionplanning problem (among static obstacles) is to capture the connectivity of the configuration space using a finite (but possibly large) set of landmarks (or nodes) in it. In this paper, we extend this paradigm to manipulation-planning problem, where the goal is to plan the motion of a robot so that it can move a given object from an initial configuration to a final configuration while avoiding collisions with the static obstacles and other movable objects in the environment. Our specific approach adapts Adriadne's clew algorithm, which has been shown effective for classical motion-planning problems (Mazer et al. 1994;Ahuactzin 1994). In our approach, landmarks are placed in lower dimensional submanifolds of the composite configuration space. These landmarks represent stable grasps that are reachable from the initial configuration. From each new landmark, the planner attempts to reach the goal configuration by executing a local planner, again in a lower (but different) dimensional submanifold of the composite configuration space. The approach is probabilistically resolution complete, does not assume that a closed-form inverse-kinematics solution for the manipulator is available, and is particularly suitable for redundant manipulators. We also demonstrate that our approach is practical for realistic problems in three-dimensional environments with manipulator arms having fairly large numbers of degrees of freedom. We have experimented with this approach for a 7-DOF manipulator in 3-D environments with one movable object, and computation times range between a few minutes and a few tens of minutes-in our experiments, between 3 min to 15 min, depending on the task difficulty.