Planning algorithms

By Steven M. LaValle. 2006

3,904 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Robotics & Control

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In Robotics & Control

Citations in the corpus

Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Task and motion planning [22, 23] requires simultaneously solving a high-level, discrete task planning problem [24, 25, 26], and a low-level, continuous motion planning problem [27].”
Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “ommands that steer the agent. Classical motion planning ignores dynamics and differential constraints while using translations and rotations required to move an agent from source to destination poses [11]. A robotic agent capable of controlling 6-degrees of freedom (DOF) is said to be holonomic, whi”
Deep Imitative Models for Flexible Inference, Planning, and Control (2018, methodology): “Together with the same LIDAR map χ our method uses to locate obstacles, this baseline uses its dynamics model to plan a reachability tree LaValle (2006) through the free-space to the waypoint while avoiding obstacles.”
On Evaluation of Embodied Navigation Agents (2018, background): “There is a large body of work on motion planning, where a collision-free path in workspace or conﬁguration space is sought, given a geometric model of the environment [18].”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, background): “In the remainder of the section we discuss the responsibilities of each of these components in more detail.”
Deep reinforcement learning with successor features for navigation across similar environments (2016, background): “Although individually the problems of SLAM, localization, path planning and control are well understood [1], [2], [3], and a lot of progress has been made on learning control [4], they have mainly been treated as separable problems within robotics and some often require human assistance during”
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning (2019, background): “Planning Algorithms : Planning algorithms (Choset et al., 2005; LaValle, 2006) efﬁciently solve long-horizon tasks, including those that stymie RL algorithms (see, e.g., Kavraki et al. (1996); Lau and Kuffner (2005); Levine et al. (2011)).”
Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning (2018, background): “Unfortunately, efficient general inversion of a physical simulator is implausible as it defies what we know about the fundamental complexities of task and path level planning [15],”
CoverNet: Multimodal Behavior Prediction Using Trajectory Sets (2019, methodology): “We use a standard vehicle dynamical model [24] as similar models are effective for planning at urban (nonhighway) driving speeds [23].”
Unfreezing the robot: Navigation in dense, interacting crowds (2010, background): “Unfortunately, classical algorithms for navigation in static or dynamic environments typically ignore uncertainty [15], and thus do not generalize well to situations where the behavior of the environment is stochastic.”
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, methodology): “Lee et al. (2018) proposed an online monitoring temperature system for orthogonal dry cutting, which includes an infrared imager using an ANN model (Lee et al., 2018). Kovac et al. (2018) presented a model for predicted tool life and cutting temperatures for face milling of AISI 1060 steel using the”
The Algorithm Design Manual (2020, background): “Two other worthy books on motion planning are freely available: by LaValle [LaV06] (http://planning.cs.uiuc. edu/) and Laumond [Lau98] (https://www.laas.fr/~jpl/book.html) respectively.”
Neural SLAM (2017, methodology): “Traditional methods in robotics thus develop a series of methods like Simultaneous Localization and Mapping (SLAM), localization in a given map, path planning and motion control, to enable robots to complete such challenging tasks [21] [11] [10].”
Survey on Coverage Path Planning with Unmanned Aerial Vehicles (2019, methodology): “With the DLS, the exploration length is restricted to the quantity of vertices and do not gets stuck in loops or revisits a node.”
Rearrangement: A Challenge for Embodied AI (2020, background): “LaValle [47] is a good entry into this literature.”