The "Ariadne's clew" algorithm: global planning with local methods

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.

Soft-Computing Techniques for the Trajectory Planning of Multi-Robot Manipulator Systems

“…These algorithms use the same chromosome representation. In a different approach for the path planning in the C-space, (Bessiere, Ahuactzin et al, 1993) propose a path planner that uses a local GA to define Manhattan motions to a series of sub-goals or landmarks. A second GA planner then attempts to connect the landmarks with the goal.…”

“…Due to the natural dexterity of robot manipulators, the space of solution is non-linear and multidimensional, where more than a single solution exists to solve a particular point in the Cartesian space and choosing the appropriate solution requires an optimisation approach. Taking this into consideration, the solution of the motion planning problem of robot manipulators is an ideal candidate for the use of soft-computing techniques such as genetic algorithms and fuzzy logic, as both approaches are known to perform well under multidimensional non-linear spaces without the need for complex mathematic manipulation to find a suitable solution (Zadeh, 1965;Mamdani, 1974;Goldberg, 1983;Bessiere et al, 1993;Doyle, 1995;Doyle & Jones, 1996).…”

MPK: An open extensible motion planning kernel

“…The inclusion of more complex tasks is something that is being actively investigated. Examples include grasping planning, partially specified goal configurations, inverse kinematics, both point-to-point 25 and along a specific tool frame path, and manipulation planning, 26 etc.…”

An incremental learning approach to motion planning with roadmap management

“…Several planners in the literature took a learning approach [4,6,11,21]. In one of the early papers proposing the idea of probabilistic roadmap, roadmap was used as a way to learn the freespace [21].…”

“…In [6], a planner called ERPP uses the local minima learned in an RPP-based planner to build a roadmap for a static environment. The work in [4] uses a genetic algorithm to evolve critical configurations, called landmarks, for freespace connectivity. The RRT-based planners are incremental in nature because of the way that it explores the search space.…”