Real-time randomized path planning for robot navigation

By James Bruce, Manuela M. Veloso. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002

559 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9cec1297bfce7ddd63390670467f1af6ccae4165

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Citations in the corpus

Incremental Sampling-based Algorithms for Optimal Motion Planning (2010, methodology): “Moreover, the RRT algorithm was demonstrated in major robotics events on various experimental robotic platforms [38]–[42].”
E-Graphs: Bootstrapping Planning with Experience Graphs (2012, background): “[3] extended the traditional RRT approach to reuse cached plans and bias the search towards waypoints from older plans.”
Approaches for heuristically biasing RRT growth (2003, methodology): “More recently, Bruce adapted RRTs for use with a robotic soccer team [21.”
Multipartite RRTs for Rapid Replanning in Dynamic Environments (2007, unknown): “Conceptually, MP-RRT combines the strengths of biasing the sampling distribution towards previously useful states as in ERRT and analytically computing which segments of previous RRTs can be re-used as in DRRT.”
Replanning with RRTs (2006, methodology): “As a result, they have been widely-used in robotics [3], [4], [5], [6].”
Push-manipulation of complex passive mobile objects using experimentally acquired motion models (2014, background): “It is also possible to bias the tree growth towards the goal and reuse past executions (Bruce and Veloso 2002).”
Sampling-Based Robot Motion Planning: A Review (2014, background): “Multipartite RRT (MP-RRT) [217] combines the strategy of biasing the search towards discarded configurations, similar to ERRT.”
A Survey and Analysis of Multi-Robot Coordination (2013, unknown): “Bruce and Veloso [139] described a RRT-based method for MRMP, called extended RRT (ERRT), in which the mechanism of a waypoint cache was implemented to store the knowledge of where a plan might again be found in the near future, and another mechanism of an adaptive beta search was used as well,…”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “For example, sampling could be biased to sample near waypoints along the previously chosen solution path, as with Extended RRT (ERRT) [255].”
Optimal kinodynamic motion planning using incremental sampling-based methods (2010, background): “In fact, many field implementations of the RRT algorithm do not stop sampling once a feasible trajectory is found, but keep searching for an better solution [4], [15].”
Planning algorithms (2006, methodology): “RRTs have been used in several applications, and many variants have been developed [84, 140, 152, 202, 226, 246, 267, 364, 395, 498, 501, 502, 531, 634, 644, 645, 919, 920, 949, 979, 986].”
Safe motion planning in dynamic environments (2005, unknown): “Beside early work based on dynamic programming, like the dynamic A* algorithm (D*) [20], [21], the approach of motion planning has been mainly reconsidered with the new tools of probabilistic techniques based on graph or tree construction, showing impressive results [10], [11].”
Time-optimal path planning in dynamic flows using level set equations: theory and schemes (2014, background): “123 Their ability to quickly and uniformly explore a large 124 workspace has led to their widespread usage in several 125 path planning applications including robotics (Yang et al, 126 2010; Bruce and Veloso, 2002; Melchior and Simmons, 127 2007) and ocean cases (Rao and Williams, 2009).”