Multipartite RRTs for Rapid Replanning in Dynamic Environments

By Matthew Zucker, James J. Kuffner Jr., Michael S. Branicky. Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007

322 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b23fa6c337968983d0fd1e3eaf7e8eb09e2375e8

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Citations in the corpus

Sampling-Based Robot Motion Planning: A Review (2014, methodology): “It also rebuilds the tree, like DRRT, and maintains separate detached forests, like RRF. MP-RRT distinguishes itself from RRF by only maintaining forests for a limited time so as not to waste computational time in unpromising areas.”
E-Graphs: Bootstrapping Planning with Experience Graphs (2012, background): “Extensions of this approach can be found in [20, 6].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, background): “Other assumptions can be applied to obstacles as well, such as static assumption, constant velocity assumption, bounded velocity, and bounded acceleration, each of which yields a bounded volume of a different shape in space-time [235].”
Real-Time Motion Planning With Applications to Autonomous Urban Driving (2009, background): “Furthermore, real-time execution requirements necessitate reuse of the information from the previous computation cycle [26]–[28].”
Reciprocal Velocity Obstacles for real-time multi-agent navigation (2008, methodology): “Besides the Velocity Obstacle approach [3], [16], many other methods have been proposed for collision-avoidance, navigation, and planning among moving obstacles [4], [7], [8], [14], [23], [21], [20], [24].”
Sampling-based algorithms for optimal motion planning (2011, methodology): “The basic version of the RRT algorithm has been extended in several directions, and found many applications in the robotics domain and elsewhere (see, for instance, Frazzoli et al. 2002; Bhatia and Frazzoli 2004; Branicky et al. 2003, 2006; Cortes et al. 2007; Zucker et al. 2007).”
A robot path planning framework that learns from experience (2012, methodology): “There are several methods in motion planning [3] and trajectory optimization [20] [21] that are capable of this task.”
Reciprocal n-Body Collision Avoidance (2011, background): “This problem is generally referred to as “asteroid avoidance”, and approaches typically extrapolate the observed velocities in order to estimate the future positions of obstacles [8, 9, 12, 19, 22, 28].”