L. Jaillet

Publications39
h-index18
Citations1,603
Highly Influential Citations105
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Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain
TLDR
This paper develops and implements a simple new planner which shows significant improvement over existing RRT-based planners and proposes a general framework for minimizing their effect. Expand
Sampling-Based Path Planning on Configuration-Space Costmaps
TLDR
The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap using the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. Expand
Transition-based RRT for path planning in continuous cost spaces
TLDR
This paper presents a new method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces that combines the exploration strength of the RRT algorithm with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. Expand
Adaptive tuning of the sampling domain for dynamic-domain RRTs
TLDR
A new variant of the dynamic-domain RRT, which iteratively adapts the sampling domain for the Voronoi region of each node during the search process, which allows automatic tuning of the parameter and significantly increases the robustness of the algorithm. Expand
Path Deformation Roadmaps: Compact Graphs with Useful Cycles for Motion Planning
TLDR
A method that extends the Visibility-PRM technique to construct compact roadmaps which encode richer and more suitable information than representative paths of the homotopy classes, which enables small roadmaps to reliably capture the multiple connectedness of complex spaces in various problems involving free-flying and articulated robots in both two- and three-dimensional environments. Expand
A PRM-based motion planner for dynamically changing environments
  • L. Jaillet, T. Siméon
  • Computer Science
  • IEEE/RSJ International Conference on Intelligent…
  • 28 September 2004
TLDR
Simulation results show that this combination of techniques yields to efficient global planner capable of solving with a real-time performance problems in geometrically complex environments with moving obstacles. Expand
Randomized tree construction algorithm to explore energy landscapes
TLDR
A new method for exploring conformational energy landscapes, called transition‐rapidly exploring random tree (T‐RRT), combines ideas from statistical physics and robot path planning algorithms to efficiently find both energy minima and transition paths between them. Expand
Path Planning with Loop Closure Constraints Using an Atlas-Based RRT
TLDR
The AtlasRRT algorithm is presented, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation that allows a more efficient extension of the RRT than state of the art approaches. Expand
Path Planning Under Kinematic Constraints by Rapidly Exploring Manifolds
TLDR
AtlasRRT is presented, which is a planner especially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation that produces a more rapid exploration of the configuration space manifolds than existing approaches. Expand
Disassembly Path Planning for Complex Articulated Objects
TLDR
A variant of the rapidly-exploring random tree (RRT) algorithm particularly devised for the disassembly of objects with articulated parts is presented, showing a remarkable performance improvement as compared to standard path planning techniques. Expand
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