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)

Maintainability is an important issue in design where the accessibility of certain parts is determined for routine maintenance. In the past its study has been largely manual and labor intensive. Either by using physical mockup or computer animation with CAD models of a design, the task relies on human to provide an access path for the part. In this paper, we present an automated approach to replace this manual process. By applying results from and developing extensions to research in motion planning and other fields, we demonstrate that an automated maintainability study system is feasible. We describe general extensions needed to adapt robotic motion planning techniques in maintainability studies. We show results from applying such a system to two classes of industrial application problems. Key Words: Maintainability Study, Motion Planning, Design Automation, Rapid Prototyping, Visualization. 1 Introduction Assembly maintainability studies attempt to find whether it is possible to ...

The Probabilistic RoadMap planner (PRM) has been applied with success to multiple planning problems involving robots with 3 to 16 degrees of freedom (dof) operating in known static environments. This paper describes the planner and reports on experimental and theoretical results related to its performance. PRM computation consists of a preprocessing and a query phase. Preprocessing, which is done only once for a given environment, generates a roadmap of randomly, but properly selected, collision-free configurations (nodes). Planning then connects any given initial and final configurations of the robot to two nodes of the roadmap and computes a path through the roadmap between these two nodes. The planner is able to find paths involving robots with 10 dof in a fraction of a second after relatively short times for preprocessing (a few dozen seconds). Theoretical analysis of the PRM algorithm provides bounds on the number of roadmap nodes needed for solving planning problems in spaces with certain geometric properties. A number of theoretical results are presented in this paper under a unified framework.

We show how paths for articulated robots with many degrees of freedom can be generated in a few seconds or less using nonsystematic parallel search. We present experimental results obtained on a multicomputer for an accurate model of a 7-jointed manipulator arm operating in realistic 3D workspaces. We then present and discuss a fast method for smoothing the paths delivered by the parallel algorithm. 1 Introduction Among the many skills robots require is the ability to plan the paths they must take while executing their tasks. The intent of this paper is to provide experimental evidence that very fast path planning can be performed using parallel algorithms. By very fast we mean systems that produce solutions in fractions of seconds or a few seconds for complex environments and robots with over four degrees of freedom (dof). We have developed a parallel formulation of the Randomized Path Planner proposed by Barraquand and Latombe [2]. Our parallel formulation has proved extremely eff...

This paper presents a new approach to path planning for robots with many degrees of freedom (dof) operating in known static environments. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given environment, generates a network of randomly, but properly selected, collision-free configurations (nodes). Planning then connects any given initial and final configurations of the robot to two nodes of the network and computes a path through the network between these two nodes. Experiments show that after paying the preprocessing cost (on the order of hundreds of seconds), planning is extremely fast (on the order of a fraction of a second for many difficult examples involving a 10-dof robot). The approach is particularly attractive for many-dof robots which have to perform many successive point-to-point motions in the same environment.

In a constantly changing and partially unpredictable environment, robot motion planning must be on-line. The planner receives a continuous flow of information about occurring events and generates new plans, while previously planned motions are being executed. This paper describes an on-line planner for two cooperating arms whose task is to grab parts of various types on a conveyor belt and transfer them to their respective goals while avoiding collision with obstacles. Parts arrive on the belt in random order, at any time. Both goals and obstacles may be dynamically changed. This scenario is typical of manufacturing cells serving machine-tools, assembling products, or packaging objects. The proposed approach breaks the overall planning problem into subproblems, each involving a low-dimensional configuration or configuration\Thetatime space, and orchestrates very fast primitives solving these subproblems. The resulting planner has been implemented and extensively tested in a simulated e...

. We present an ongoing research work on robot motion planning using genetic algorithms. Our goal is to use this technique to build fast motion planners for robot with six or more degree of freedom. After a short review of the existing methods, we will introduce the genetic algorithms by showing how they can be used to solve the invers kinematic problem. In the second part of the paper, we show that the path planning problem can be expressed as an optimization problem and thus solved with a genetic algorithm. We illustrate the approach by building a path planner for a planar arm with two degree of freedom, then we demonstrate the validity of the method by planning paths for an holonomic mobile robot. Finally we describe an implementation of the selected genetic algorithm on a massively parallel machine and show that fast planning response is made possible by using this approach. Keywords: robot motion planning, genetic algorithms, parallel algorithms. 1 Introduction Today most of the...

One of the many features needed to support the activities of autonomous systems is the ability of motion planning. It enables robots to move in their environment securely and to accomplish given tasks. Unfortunately, the control loop comprising sensing, planning, and acting has not yet been closed for robots in dynamic environments. One reason involves the long execution times of the motion planning component. A solution for this problem is offered by the use of highly computational parallelism. Thus, an important task is the parallelization of existing motion planning algorithms for robots so that they are suitable for highly computational parallelism. In several cases, completely new algorithms have to be designed, so that a parallelization is feasible. In this survey, we review recent approaches to motion planning using parallel computation. As a classification scheme, we use the structure given by the different approaches to the robot's motion planning. For each approach, the available parallel processing methods are discussed. Each approach is uniquely assigned a class. Finally, for each referenced research work, a list of keywords is given.

Abstract — This paper shows how to effectively combine a sampling-based method primarily designed for multiple query motion planning (Probabilistic Roadmap Method- PRM) with sampling-based tree methods primarily designed for single query motion planning (Expansive Space Trees, Rapidly-Exploring Random Trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple query and single query planning but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently withPRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled thanPRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners. Index Terms — Motion planning, sampling-based planning, parallel algorithms, roadmap, tree, PRM, EST, RRT, SRT.

Abstract. The paper presents a novel approach to parallel motion planning for robot manipulators in 3D workspaces. The approach is based on a randomized parallel search algorithm and focuses on solving the path planning problem for industrial robot arms working in a reasonably cluttered workspace. The path planning system works in the discretized con guration space which needs not to be represented explicitly. The parallel search is conducted by anumber of rule-based sequential search processes, which work to nd a path connecting the initial con guration to the goal via a number of randomly generated subgoal con gurations. Since the planning performs only on-line collision tests with proper proximity information without using pre-computed information, the approach is suitable for planning problems with multirobot or dynamic environments. The implementation has been carried out on the parallel virtual machine (PVM) of a cluster of SUN4 workstations and SGI machines. The experimental results haveshown that the approachworks well for a 6-dof robot arm in a reasonably cluttered environment, and that parallel computation increases the e ciency of motion planning signi cantly. 1