Sampling-Based Path Planning on Configuration-Space Costmaps

By Leonard Jaillet, Juan Cortés, Thierry Siméon. IEEE Transactions on robotics, 2010

406 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/22b4c53d3c98cf91875a3349d208f4d5f77da63c

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In Aerospace Engineering

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Citations in the corpus

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions (2016, methodology): “DT-RRT manages two trees called state tree and workspace tree.”
Sampling-Based Robot Motion Planning: A Review (2014, background): “Transition-RRT (T-RRT) [125] were proposed to handle nonuniform cost C-space, referred to as cost maps.”
Terrain traversability analysis methods for unmanned ground vehicles: A survey (2013, background): “…terrain analysis enables the application of efficient graph search algorithms for the purpose of planning, that are currently still very popular and extensively used (see Kavraki et al., 1996; Pivtoraiko and Kelly, 2005; Melchior and Simmons, 2007; Howard et al., 2008; Jaillet et al., 2010).”
Planning human-aware motions using a sampling-based costmap planner (2011, methodology): “Finally, we present a refined description of the HRI constraints, in particular the ”arm comfort constraint” only used in [18] for computing the object transfer point of hand over tasks while considered here for planning robot motions.”
Learning preferences for manipulation tasks from online coactive feedback (2015, background): “Several works build upon the sampling-based planner RRT (LaValle and Kuffner, 2001) to optimize various cost heuristics (Ettlin and Bleuler, 2006; D. Ferguson and Stentz, 2006; Jaillet et al., 2010).”
Sampling-based Planning of In-Hand Manipulation with External Pushes (2017, methodology): “One of the variants of RRT* that we particularly find useful in this work is T-RRT*, which is developed for path planning on configuration-space cost maps [6, 9].”
Sampling-based algorithms for optimal motion planning (2011, methodology): “A more recent approach is the transitionbased RRT (T-RRT) designed to combine rapid exploration properties of the RRT with stochastic global optimization methods (Jaillet et al. 2010; Berenson et al. 2011).”