Robot Motion Planning: A Distributed Representation Approach

By J. Barraquand, Jean-Claude Latombe. 1991

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Randomized Potential Planner (RPP) [252] is one of the earliest SBP approaches, where random walks are generated for escaping local minimums.”
The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation (2019, methodology): “Specifically, we develop: 1) A Regret Module that provides a mechanism to allow the agent to learn when to backtrack [10, 3] and 2) We propose a Progress Marker mechanism that allows the agent to incorporate information from previous visits and reason about such visits and their associated progress ”
Dual arm manipulation - A survey (2012, methodology): “The method proposed extends the randomized potential field planning techniques introduced in [116] with the introduction of transit (where the arms move independently and the object remains static) and transfer (where the arms and the object form a closed kinematic chain) subpaths.”
Randomized Kinodynamic Motion Planning with Moving Obstacles (2002, background): “It has been shown that the probability that the randomized potential field planner fails to find a solution path when one exists goes to 0 as the running time increases, hence proving that the planner is probabilistically complete (Barraquand and Latombe 1991).”
Probabilistic roadmaps for path planning in high-dimensional configuration spaces (1996, unknown): “For example, for planar articulated robots with many dofs, the customized implementation of Section V is much more consistent than the Randomized Path Planner (RPP) of [ 6 ].”
Randomized Kinodynamic Planning (1999, methodology): “This idea has been proposed in path-planning literature for nonholonomic planning (Barraquand and Latombe 1991a; Lynch and Mason 1996) and kinodynamic planning (Cherif 1999; Donald et al. 1993).”
Rapidly-exploring random trees : a new tool for path planning (1998, background): “, [2]) and the probabilistic roadmap algorithm (e.”
Incremental Sampling-based Algorithms for Optimal Motion Planning (2010, background): “Practical planners came around with the development of cell decomposition methods [10], [11], potential fields [12], and roadmap methods [13].”
Path Planning and Trajectory Planning Algorithms: A General Overview (2015, background): “Another approach to solve the path planning problem is found in [5], where a special kind of planners, named RPP (Random Path Planners), is proposed: local minima are avoided by combining the concepts of artiﬁcial potential ﬁeld with random search techniques.”
An obstacle-based rapidly-exploring random tree (2006, methodology): “A randomized potential field method, RPP [3], works very well when the C-space is relatively uncluttered, but unfortunately there also exist simple situations in which they are not successful [12].”
Toward Optimal Sampling in the Space of Paths (2007, background): “Independent of optimality, the question of whether an existing solution will be found is typically answered in terms of resolution completeness for deterministically sampled approaches such as [2] and probabilistic completeness for randomly sampled approaches such as [9][11].”
Elastic Strips: A Framework for Motion Generation in Human Environments (2002, methodology): “This approach is described in more detail in Section 3.”
A Complete and Scalable Strategy for Coordinating Multiple Robots Within Roadmaps (2008, methodology): “Decoupled methods may use a decentralized architecture, allowing independent planning-based methods such as maze searching [17] or potential fields [3], [8], or they may use a centralized architecture planning for all robots with a single processor.”
Dynamically-Stable Motion Planning for Humanoid Robots (2002, background): “This limitation, lower-bound hardness results [Rei79], and strong motivation to handle practical planning problems have stimulated the development and success of many path planning methods that use randomization (e.g., [ BL90 , Ove92, Sve93, CG93, KL93, HST94, KKKL94, HLM97, AW96, Ky SLO96, BKL+97, ”
Sampling-based algorithms for optimal motion planning (2011, background): “Important contributions towards broader applicability of these methods include navigation functions (Rimon and Koditschek 1992) and randomization (Barraquand and Latombe 1993).”
Sampling-Based Robot Motion Planning: A Review (2014, background): “Both sampling based planners and the success of random computations inspired the development of the Randomized Potential Planner (RPP) [51].”