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)

We provide an analysis of a recent path planning method which uses probabilistic roadmaps. This method has proven very successful in practice, but the theoretical un- derstanding of its performance is still limited. Assuming that a path 7 exists between two configurations a and b of the robot, we study the dependence of the failure probability to connect a and b on (i) the length of 7, (ii) the distance function of 7 from the obstacles, and (iii) the number of nodes N of the probabilistic roadmap constructed. Importantly, our results do not depend strongly on local irregularities of the configuration space, as was the case with previous analysis. These results are illustrated with a simple but illuminat- ing example. In this example, we provide estimates for N, the principal parameter of the method, in order to achieve failure probability within prescribed bounds. We also compare, through this example, the different approaches to the analysis of the planning method.

We present a randomized approach to path planning for articulated robots that have closed kinematic chains. The approach extends the probabilistic roadmap technique which has previously been applied to rigid and elastic objects, and articulated robots without closed chains. Our work provides a framework for path planning problems that must satisfy closure constraints in addition to standard collision constraints. This expands the power of the probabilistic roadmap technique to include a variety of problems such as manipulation planning using two open-chain manipulators that cooperatively grasp an object, forming a system with a closed chain, and planning for reconfigurable robots where the robot links may be rearranged in a loop to ease manipulation or locomotion. We generate the vertices in our probabilistic roadmap by sampling random con gurations that ignore kinematic closure, and by performing randomized gradient descent to force satisfaction of the closure constraints. We generate...

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.

This paper describes a first step in the direction of solving a variant of the above problem, namely the problem of planning a path for a flexible robot/part. We focus on the case of an elastic metal plate to illustrate our approach and explore some of the issues arising when considering flexible parts

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...

This paper presents an efficient algorithm for optimizing the base location of a robot manipulator in an environment cluttered with obstacles, in order to execute specified tasks as fast as possible. The algorithm uses randomized motion planning techniques and exploits geometric "coherence " in configuration space to achieve fast computation. The performance of the algorithm is demonstrated on both synthetic examples and real-life CAD data from the automotive industry. The computation time ranges from under a minute for simple problems to a few minutes for more complex ones. 1 Introduction The base placement of a robot manipulator is an important issue in many robotics applications. Given a description of a robot manipulator and its environment, the goal is to find a base location for the manipulator so that specified tasks are executed as efficiently as possible. In this paper, we present an algorithm that makes use of randomized motion planning techniques to compute simultaneously ...

This paper investigates the problem of planning paths for elastic objects such as metallic plates or plastic flexible pipes. The problem differs significantly from that of planning for a rigid or articulated object/robot. In our examples the flexibility of the object needs to be exploited to accomplish a task. At the same time, elasticity constraints should be observed to avoid damaging the object. Our work addresses the modeling of the mechanical behavior of the object, the modeling of the geometry of the object, and the development of algorithms for planning paths for the object under manipulation constraints. We discuss the interaction of the three components above and develop a general framework for planning for elastic objects. As a case study, we demonstrate the application of our framework to planning paths for elastic plates and present several experimental results. Our work is motivated by the need to consider the physical properties of objects while planning and has...