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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 ..."
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Cited by 888 (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)
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.
Probabilistic Roadmaps for Robot Path Planning
, 1998
"... 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 perfo ..."
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Cited by 54 (5 self)
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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, collisionfree 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.
Dynamicallystable Motion Planning for Humanoid Robots
, 2000
"... We present an algorithm for computing stable collisionfree motions for humanoid robots given fullbody posture goals. The motion planner is part of a simulation environment under development for providing highlevel software control for humanoid robots. Given a robot's internal model of the enviro ..."
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Cited by 40 (5 self)
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We present an algorithm for computing stable collisionfree motions for humanoid robots given fullbody posture goals. The motion planner is part of a simulation environment under development for providing highlevel software control for humanoid robots. Given a robot's internal model of the environment and a staticallystable desired posture, we use a randomized path planner to search the configuration space of the robot for a collisionfree path. Balance constraints are imposed on incremental search motions in order to maintain the overall dynamic stability of the computed trajectories. The algorithm is presented along with preliminary results using an experimental implementation on a dynamic model of the H5 humanoid robot.
Randomized Preprocessing of Configuration Space for Path Planning: Articulated Robots
 IN PROC. IEEE INT. CONF. ROBOTICS AND AUTOMATION
, 1994
"... 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, bu ..."
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Cited by 34 (6 self)
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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, collisionfree 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 10dof robot). The approach is particularly attractive for manydof robots which have to perform many successive pointtopoint motions in the same environment.
RRTConnect: An efficient approach to singlequery path planning
 Proc. IEEE Intâ€™l Conf. on Robotics and Automation
, 2000
"... A simple and efficient randomized algorithm is presented for solving singlequery path planning problems in highdimensional configuration spaces. The method works by incrementally building two Rapidlyexploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each expl ..."
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Cited by 19 (0 self)
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A simple and efficient randomized algorithm is presented for solving singlequery path planning problems in highdimensional configuration spaces. The method works by incrementally building two Rapidlyexploring Random Trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7DOF kinematic chain) for the automatic graphic animation of collisionfree grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collisionfree motions for rigid objects in 2D and 3D, and collisionfree manipulation motions for a 6DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.
SliceBased Path Planning
, 1998
"... x 1 Introduction 1 1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Algorithm and Results . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4 Contributions . . . . . . . . . ..."
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Cited by 3 (0 self)
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x 1 Introduction 1 1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Algorithm and Results . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2 Literature Review 19 2.1 Anytime Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Graphbased Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.1 Roadmap Methods . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.2 Cell Decomposition . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3 Potential Field Methods . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.1 Potential Field Methods . . . . . . . . . . . . . . . . . . . . . 28 2.3.2 GridBased Potential Functions . . . . . . . . . . . . . . . . . 29 2.4 Var...