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118
RapidlyExploring Random Trees: Progress and Prospects
 Algorithmic and Computational Robotics: New Directions
, 2000
"... this paper, which presents randomized, algorithmic techniques for path planning that are particular suited for problems that involve dierential constraints. ..."
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Cited by 228 (25 self)
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this paper, which presents randomized, algorithmic techniques for path planning that are particular suited for problems that involve dierential constraints.
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
Geometric Shortest Paths and Network Optimization
 Handbook of Computational Geometry
, 1998
"... Introduction A natural and wellstudied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of t ..."
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Cited by 147 (12 self)
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Introduction A natural and wellstudied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of the edges that comprise it. Efficient algorithms are well known for this problem, as briefly summarized below. The shortest path problem takes on a new dimension when considered in a geometric domain. In contrast to graphs, where the encoding of edges is explicit, a geometric instance of a shortest path problem is usually specified by giving geometric objects that implicitly encode the graph and its edge weights. Our goal in devising efficient geometric algorithms is generally to avoid explicit construction of the entire underlying graph, since the full induced graph may be very large (even exponential in the input size, or infinite). Computing an optimal
RealTime Motion Planning For Agile Autonomous Vehicles
 AIAA JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS
, 2000
"... ..."
A Probabilistic Learning Approach to Motion Planning
 In Proc. Workshop on Algorithmic Foundations of Robotics
, 1994
"... 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 r ..."
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Cited by 112 (4 self)
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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 collisionfree 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...
A Probabilistic Roadmap Approach for Systems with Closed Kinematic Chains
, 1999
"... 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 frame ..."
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Cited by 70 (4 self)
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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 openchain 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...
Guidelines in nonholonomic motion planning for mobile robots
 ROBOT MOTION PLANNNING AND CONTROL
, 1998
"... ..."
Kinodynamic Motion Planning Amidst Moving Obstacles
, 2000
"... This paper presents a randomized motion planner for kinodynamic asteroid avoidanceproblems, in which a robot must avoid collision with moving obstacles under kinematic, dynamic constraints and reach a specified goal state. Inspired by probabilisticroadmap (PRM) techniques, the planner samples the s ..."
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Cited by 60 (8 self)
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This paper presents a randomized motion planner for kinodynamic asteroid avoidanceproblems, in which a robot must avoid collision with moving obstacles under kinematic, dynamic constraints and reach a specified goal state. Inspired by probabilisticroadmap (PRM) techniques, the planner samples the state\Thetatime space of a robot by picking control inputs at random in order to compute a roadmap that captures the connectivity of the space. However, the planner does not precompute a roadmap as most PRM planners do. Instead, for each planning query, it generates, on the fly, a small roadmap that connects the given initial and goal state. In contrast to PRM planners, the roadmapcomputed by our algorithm is a directed graph oriented along the time axis of the space. To verify the planner's effectiveness in practice, we tested it both in simulated environments containing many moving obstacles and on a real robot under strict dynamic constraints. The efficiency of the planner makes it possibl...
Motion planning: A journey of robots, molecules, digital actors, and other artifacts
 International Journal of Robotics Research
, 1999
"... During the last three decades motion planning has emerged as a crucial and productive research area in robotics. In the mid80's the most advanced planners were barely able to compute collisionfree paths for objects crawling in planar workspaces. Today, planners e ciently deal with robots with many ..."
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Cited by 56 (3 self)
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During the last three decades motion planning has emerged as a crucial and productive research area in robotics. In the mid80's the most advanced planners were barely able to compute collisionfree paths for objects crawling in planar workspaces. Today, planners e ciently deal with robots with many degrees of freedom in complex environments. Techniques also exist to generate quasioptimal trajectories, coordinate multiple robots, deal with dynamic and kinematic constraints, and handle dynamic environments. This paper describes some of these achievements, presents new problems that have recently emerged, discusses applications likely to motivate future research, and nally gives expectations for the coming years. It stresses the fact that nonrobotics applications (e.g., graphic animation, surgical planning, computational biology) are growing in importance and are likely to shape future motion planning research more than robotics itself. 1
MultiLevel Path Planning for Nonholonomic Robots using SemiHolonomic Subsystems
 Int. J. Robot. Res
, 1996
"... We present a new and complete multilevel approach for solving path planning problems for nonholonomic robots. At the first level a path is found that disrespects (some of) the nonholonomic constraints. At each next level a new path is generated, by transformation of the path generated at the pre ..."
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Cited by 47 (7 self)
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We present a new and complete multilevel approach for solving path planning problems for nonholonomic robots. At the first level a path is found that disrespects (some of) the nonholonomic constraints. At each next level a new path is generated, by transformation of the path generated at the previous level. The transformation is such that more nonholonomic constraints are respected than at the previous level. At the final level all nonholonomic constraints are respected. We present two techniques for these transformations. The first, which we call the Pick and Link technique, repeatedly picks pieces from the given path, and tries to replace these by more feasible ones. The second technique restricts the free configuration space to a "tube" around the given path, and a roadmap, capturing the free space connectivity within this tube, is constructed by the Probabilistic Path Planner. From this roadmap we retrieve a new, more feasible, path. In the intermediate levels we plan ...