A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)

In this paper a new paradigm for robot motion planning is proposed. We split the motion planning process into two phases: the learning phase and the query phase. In the learning phase we construct a probabilistic roadmap in configuration space. This roadmap is a graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collision-free motions between the nodes. These simple motions are computed using a fast local method. The longer we learn, the denser the roadmap becomes and the better it is for motion planning. In the query phase we can use this roadmap to find paths between different pairs of configurations. If a possible path is not found one can always extend the roadmap by learning further. This gives a very flexible scheme in which learning time and success for queries can be balanced. We will demonstrate the power of the paradigm by applying it to various instances of motion planning : free flying planar robots, plan...

The motion planning problem asks for determining a collision-free path for a robot amidst a set of obstacles. In this paper we present a new approach for solving this problem, based on the construction of a random network of possible motions, connecting the source and goal configuration of the robot.

In this paper a recently developed learning approach for robot motion planning is extended and applied to two types of car-like robots: normal ones and robots which can only move forwards. In this learning approach the motion planning process is split into two phases: the learning phase and the query phase. In the learning phase a probabilistic roadmap is incrementally constructed in configuration space. This roadmap is an undirected graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collision-free paths between the nodes. These simple motions are computed using a fast local method. In the query phase this roadmap can be used to find paths between different pairs of configurations. The approach can be applied to normal car-like robots (with nonholonomic constraints) by using suitable local methods, which compute paths feasible for the robots. Application to car-like robots which can move only forwards demands a more fundamental a...