Differentially constrained mobile robot motion planning in state lattices

By Mihail Pivtoraiko, Ross A. Knepper, Alonzo James Kelly. J. Field Robotics, 2009

500 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/98ef1476ec32d5d60e0a6ebbd7ed3176672f9d39

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In Aerospace Engineering

In Biomedical Engineering

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Citations in the corpus

Motion planning for autonomous driving with a conformal spatiotemporal lattice (2011, methodology): “We adapt the state lattice of Pivtoraiko et al.[2] to a structured environment.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, background): “Graph search algorithm searches for a minimum cost path on a graph, in which conﬁguration space is explicitly represented in a graph, like an occupancy grid or lattice [386], [387].”
A Review of Motion Planning for Highway Autonomous Driving (2020, background): “The advantage of a lattice representation is the consideration of the kinematic constraints implicitly handled by the motion on the lattice [75], as well as a spatiotemporal consideration.”
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions (2015, background): “, 2009) but have difficulties in accounting for nonlinear dynamics in a robust way (Kushleyev and Likhachev, 2009), and in the presence of obstacles (Pivtoraiko et al., 2009).”
Self-Driving Cars: A Survey (2019, background): “A state lattice (Pivtoraiko et al., 2009) is a search graph in which vertices denote states and edges denote paths that connect states satisfying the robot’s kinematic constraints.”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, methodology): “use the term ”state lattice” to describe such graphs in [101] and point out that a set”
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles (2012, background): “have been studied by adapting applications such as executing U-turns on a blocked road, parking, and navigating a road in unstructured environments [10]–[12].”
Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems (2018, background): “The lattice-based algorithms [27], [25] are commonly affordable for real-time processing.”
Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project (2013, methodology): “The path planning algorithm is a State Lattice Planner [26] which executes the A ∗ path search on the discretized state space.”
An Open Approach to Autonomous Vehicles (2015, methodology): “In unstructured environments, such as parking lots, we provide graph-search algorithms, such as A*,(8) to find a minimum-cost path to the goal in space lattices.(9) In structured environments, such as roads and traffic lanes, on the other hand, the density of vertices and edges is likely high and no”