Randomized Kinodynamic Motion Planning with Moving Obstacles

By David Hsu, Robert Kindel, Jean-Claude Latombe, Stephen M. Rock. Int. J. Robotics Res., 2002

878 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b0501adbca9917c19ae7b343e74b6e163753a227

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Citations in the corpus

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].”
A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking (2001, background): “…roadmaps (PRM) have proven to be an effective tool to capture the connectivity of a robot’s collision-free space and solve path-planning problems with many degrees of freedom (dofs) [1, 2, 3] and/or complex admissibility (e.g., nonholonomic, stability, dynamic, and visibility constraints) [4, 5].”
Inevitable collision states. A step towards safer robots? (2003, background): “Similarly, the time-state space is appropriate to address trajectory planning problems involving moving obstacles [6, 7, 8].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “Paths can be generated from directly sampling admissible controls [240], however these methods do not optimize paths through tree rewiring, and popular asymptotically optimal planners, such as RRT* [207] require sampling from the configuration space.”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, positive): “A generalization of the idea for planning with kinodynamic constraints in dynamic environments was introduced in [115], where the capabilities of the algorithm were demonstrated on different non-holonomic robotic systems and the authors use an idealized version of the algorithm to establish that the”
Safe motion planning in dynamic environments (2005, background): “In [14] the authors complement the work presented in [10] and deﬁne safe planning as the capacity of the planner to calculate an escape plan in case it has failed ﬁnding a complete trajectory to the goal during the allotted time.”
Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic (2014, background): “Stochastic searches, such as Rapidly-exploring Random Trees (RRTs) [2], Probabilistic Roadmaps (PRMs) [3], and Expansive Space Trees (ESTs) [4], use sampling-based methods to avoid requiring a discretization of the state space.”
Incremental Sampling-based Algorithms for Optimal Motion Planning (2010, methodology): “Incremental sampling-based algorithms have been developed for single-query, real-time applications; among the most influential of these, one can mention Rapidlyexploring Random Trees (RRT) [9], and the algorithm in [10].”
Manipulation Planning for Deformable Linear Objects (2007, methodology): “is similar to the approach previously used in [7] and [13] to plan trajectories under kinodynamic constraints.”
Asymptotically optimal sampling-based kinodynamic planning (2014, methodology): “(1993)), the early but practical tree-based planners (Hsu et al., 2002; LaValle and Kuffner, 2001b) aimed for even and fast exploration of X even in challenging high-dimensional cases where greedy, heuristic expansion towards the goal would fail.”
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].”
Reciprocal collision avoidance with acceleration-velocity obstacles (2011, background): “Many works in robotics have addressed the problem of collision avoidance with moving obstacles [5], [9], [14].”
Path Planning and Trajectory Planning Algorithms: A General Overview (2015, background): “Examples of PRM can be found in [1, 24, 45, 66].”
Sampling-based algorithms for optimal motion planning (2011, background): “Tailored mainly for these applications, incremental samplingbased planning algorithms such as RRTs have emerged as an online, single-query counterpart to PRMs (see, e.g., Kuffner and LaValle 2000; Hsu et al. 2002).”
Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs (2014, background): “Email: siddh@cs.cmu.edu for nonholonomoic robots [9], [10], kinodynamic planning [11], [12], and manipulation planning [13].”