Motion Planning

By Lydia E. Kavraki, Steven M. Lavalle. 2008

3,856 citations recorded by the source corpus.

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

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Citations in the corpus

Cognitive Mapping and Planning for Visual Navigation (2017, neutral): “Path planning in these inferred maps, has also been well studied, with pioneering works from LaValle [45], and well summarized in [44].”
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning (2017, methodology): “Most MP methods are either sample based path planners or graph search algorithms [LaValle, 2006].”
Safe receding horizon control for aggressive MAV flight with limited range sensing (2015, methodology): “Now a path between these nodes can be found using Dijkstra’s search [11].”
Interactive Perception: Leveraging Action in Perception and Perception in Action (2016, background): “the quantity of interest and thereby the expected reward. A common way to ﬁnd the optimal sequence of actions that maximizes reward under deterministic dynamics is forward or backward value iteration [125]. As mentioned earlier, a realistic dynamics model should be stochastic to account for uncertai”
Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments (2010, methodology): “In general, such “obstacle-free” heuristics are based on the notion of non- holonomic distance metrics (Laumond et al. 1998 LaValle 2006).”
Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics (2013, neutral): “solving the two-point boundary value problem [14]).”
Trajectory Planning for Quadrotor Swarms (2018, methodology): “A simple approach to multi-robot motion planning is to repurpose a single-robot planner and represent the Cartesian product of the robots’ configuration spaces as a single large joint configuration space [2].”
Optimal kinodynamic motion planning using incremental sampling-based methods (2010, background): “Motion planning is a fundamental problem that is embedded and essential in almost all robotics applications [1], ranging from health care [2] to industrial production [3], and from autonomous urban navigation [4]–[6] to military logistics [7].”
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation (2020, methodology): “Collision-free feasible trajectories to points of interest can be efficiently computed and executed by a motion generator (MG) composed of a motion planner (MP) and a trajectory controller [1, 2].”
A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents (2018, background): “In the present context of autonomy required in the marine environment, autonomous navigation of USVs in a practical marine environment needs to be cognizable of three important issues, namely, safety, reliability of the mission and likelihood of the success (Statheros et al., 2008, LaValle, 2006).”
On Learning Sparse Boolean Formulae for Explaining AI Decisions (2017, methodology): “For example, reviewing decisions made by the A* planning algorithm [20] could reveal that a particular state was never considered in the priority queue.”
Chance-Constrained Optimal Path Planning With Obstacles (2011, background): “We do not intend to provide a review of this field, but instead refer the reader to the review [20] and the books [21] and [22].”