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250
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... 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 collisionfree configurations and whose edg ..."
Abstract

Cited by 890 (113 self)
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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 collisionfree 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)
Randomized Kinodynamic Planning
, 1999
"... This paper presents a statespace perspective on the kinodynamic planning problem, and introduces a randomized path planning technique that computes collisionfree kinodynamic trajectories for high degreeof freedom problems. By using a state space formulation, the kinodynamic planning problem is tr ..."
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Cited by 403 (36 self)
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This paper presents a statespace perspective on the kinodynamic planning problem, and introduces a randomized path planning technique that computes collisionfree kinodynamic trajectories for high degreeof freedom problems. By using a state space formulation, the kinodynamic planning problem is treated as a 2ndimensional nonholonomic planning problem, derived from an ndimensional configuration space. The state space serves the same role as the configuration space for basic path planning; however, standard randomized path planning techniques do not directly apply to planning trajectories in the state space. We have developed a randomized planning approach that is particularly tailored to kinodynamic problems in state spaces, although it also applies to standard nonholonomic and holonomic planning problems. The basis for this approach is the construction of a tree that attempts to rapidly and uniformly explore the state space, offering benefits that are similar to those obtained by ...
RapidlyExploring Random Trees: A New Tool for Path Planning
, 1998
"... We introduce the concept of a Rapidlyexploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the beneficial properties of existing randomized planning techniques, RRTs are specifically designed to handle nonho ..."
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Cited by 237 (17 self)
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We introduce the concept of a Rapidlyexploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the beneficial properties of existing randomized planning techniques, RRTs are specifically designed to handle nonholonomic constraints (including dynamics) and high degrees of freedom. An RRT is iteratively expanded by applying control inputs that drive the system slightly toward randomlyselected points, as opposed to requiring pointtopoint convergence, as in the probabilistic roadmap approach. Several desirable properties and a basic implementation of RRTs are discussed. To date, we have successfully applied RRTs to holonomic, nonholonomic, and kinodynamic planning problems of up to twelve degrees of freedom.
OBPRM: An ObstacleBased PRM for 3D Workspaces
, 1998
"... this paper we consider an obstaclebased prm ..."
Path Planning in Expansive Configuration Spaces
 International Journal of Computational Geometry and Applications
, 1997
"... We introduce the notion of expansiveness to characterize a family of robot configuration spaces whose connectivity can be effectively captured by a roadmap of randomlysampled milestones. The analysis of expansive configuration spaces has inspired us to develop a new randomized planning algorithm. T ..."
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Cited by 210 (37 self)
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We introduce the notion of expansiveness to characterize a family of robot configuration spaces whose connectivity can be effectively captured by a roadmap of randomlysampled milestones. The analysis of expansive configuration spaces has inspired us to develop a new randomized planning algorithm. This algorithm tries to sample only the portion of the configuration space that is relevant to the current query, avoiding the cost of precomputing a roadmap for the entire configuration space. Thus, it is wellsuited for problems where a single query is submitted for a given environment. The algorithm has been implemented and successfully applied to complex assembly maintainability problems from the automotive industry.
Randomized Kinodynamic Motion Planning with Moving Obstacles
, 2000
"... We present a randomized motion planner for robots that must avoid moving obstacles and achieve a specified goal under kinematic and dynamic constraints. The planner samples the robot's statetime space by picking control inputs at random and integrating the equations of motion. The result is a roa ..."
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Cited by 190 (12 self)
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We present a randomized motion planner for robots that must avoid moving obstacles and achieve a specified goal under kinematic and dynamic constraints. The planner samples the robot's statetime space by picking control inputs at random and integrating the equations of motion. The result is a roadmap of sampled statetime points, called milestones, connected by short admissible trajectories. The planner does not precompute the roadmap as traditional probabilistic roadmap planners do; instead, for each planning query, it generates a new roadmap to find a trajectory between an initial and a goal statetime point. We prove in this paper that the probability that the planner fails to find such a trajectory when one exists quickly goes to 0, as the number of milestones grows. The planner has been implemented and tested successfully in both simulated and real environments. In the latter case, a vision module estimates obstacle motions just before planning starts; the planner is then allocated a small, fixed amount of time to compute a trajectory. If a change in the obstacle motion is detected while the robot executes the planned trajectory, the planner recomputes a trajectory on the fly. 1
Path Planning Using Lazy PRM
 In IEEE Int. Conf. Robot. & Autom
, 2000
"... This paper describes a new approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner. Our algorithm builds a roadmap in the configu ..."
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Cited by 189 (14 self)
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This paper describes a new approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner. Our algorithm builds a roadmap in the configuration space, whose nodes are the userdefined initial and goal configurations and a number of randomly generated nodes. Neighboring nodes are connected by edges representing paths between the nodes. In contrast with PRMs, our planner initially assumes that all nodes and edges in the roadmap are collisionfree, and searches the roadmap at hand for a shortest path between the initial and the goal node. The nodes and edges along the path are then checked for collision. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap. Our planner either finds a new shortest path, or first updates the roadmap with new nodes and edges, and then searches for a shortest path. The above process is repeated until a collisionfree path is returned.
On Finding Narrow Passages with Probabilistic Roadmap Planners
, 1998
"... ... This paper provides foundations for understanding the effect of passages on the connectedness of probabilistic roadmaps. It also proposes a new random sampling scheme for finding such passages. An initial roadmap is built in a "dilated" free space allowing some penetration distance of the robot ..."
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Cited by 167 (35 self)
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... This paper provides foundations for understanding the effect of passages on the connectedness of probabilistic roadmaps. It also proposes a new random sampling scheme for finding such passages. An initial roadmap is built in a "dilated" free space allowing some penetration distance of the robot into the obstacles. This roadmap is then modified by resampling around the links that do not lie in the true free space. Experiments show that this strategy allows relatively small roadmaps to reliably capture the free space connectivity
A Randomized Roadmap Method for Path and Manipulation Planning
 In IEEE Int. Conf. Robot. & Autom
, 1996
"... This paper presents a new randomized roadmap method for motion planning for many dof robots that can be used to obtain high quality roadmaps even when Cspace is crowded. The main novelty in our approach is that roadmap candidate points are chosen on Cobstacle surfaces. As a consequence, the roadma ..."
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Cited by 130 (14 self)
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This paper presents a new randomized roadmap method for motion planning for many dof robots that can be used to obtain high quality roadmaps even when Cspace is crowded. The main novelty in our approach is that roadmap candidate points are chosen on Cobstacle surfaces. As a consequence, the roadmap is likely to contain difficult paths, such as those traversing long, narrow passages in Cspace. The approach can be used for both collisionfree path planning and for manipulation planning of contact tasks. Experimental results with a planar articulated 6 dof robot show that, after preprocessing, difficult path planning operations can often be carried out in less than a second. 1 Introduction Automatic motion planning has application in many areas such as robotics, virtual reality systems, and computeraided design. Although many different motion planning methods have been proposed, most are not used in practice since they are computationally infeasible except for some restricted cases, e...
Social Potential Fields: A Distributed Behavioral Control for Autonomous Robots
, 1999
"... A Very Large Scale Robotic (VLSR) system may consist of from hundreds to perhaps tens of thousands or more autonomous robots. The costs of robots are going down, and the robots are getting more compact, more capable, and more flexible. Hence, in the near future, we expect to see many industrial and ..."
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Cited by 124 (1 self)
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A Very Large Scale Robotic (VLSR) system may consist of from hundreds to perhaps tens of thousands or more autonomous robots. The costs of robots are going down, and the robots are getting more compact, more capable, and more flexible. Hence, in the near future, we expect to see many industrial and military applications of VLSR systems in tasks such as assembling, transporting, hazardous inspection, patrolling, guarding and attacking. In this paper, we propose a new approach for distributed autonomous control of VLSR systems. We define simple artificial force laws between pairs of robots or robot groups. The force laws are inversepower force laws, incorporating both attraction and repulsion. The force laws can be distinct and to some degree they reflect the 'social relations' among robots. Therefore we call our method social potential fields. An individual robot's motion is controlled by the resultant artificial force imposed by other robots and other components of the system. The approach is distributed in that the force calculations and motion control can be done in an asynchronous and distributed manner. We also extend the social potential fields model to use spring laws as force laws. This paper presents the first and a preliminary study on applying potential fields to distributed autonomous multirobot control. We describe the generic framework of our social potential fields method. We show with computer simulations that the method can yield interesting and useful behaviors among robots, and we give examples of possible industrial and military applications. We also identify theoretical problems for future studies. 1999 Published by Elsevier Science B.V. All rights reserved.