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.
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.
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.
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.
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.
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.
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.
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.
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.
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.