The Hybrid Reciprocal Velocity Obstacle

By Jamie Snape, Jur van den Berg, Stephen J. Guy, Dinesh Manocha. IEEE Transactions on robotics, 2011

440 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/21f66f0794efc8ff2a2096560cafdb3425962095

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Citations in the corpus

Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning (2016, methodology): “Reaction-based methods [9]–[11] specify one-step interaction rules for the current geometric configuration.”
Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey (2014, methodology): “257,258,285 The method may be extended to arbitrary vehicle models, as rigorous avoidance is achieved via the addition of a generic bounded-deviation path tracking system.”
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning (2017, methodology): “ORCA and its extensions [2], [5] used heuristics or first principles to construct a complex model for the collision avoidance policy, which has many parameters that are tedious and difficult to be tuned properly.”
Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation (2016, methodology): “Other approaches [3], [5] avoided the difficulty in sensing uncertainty by using an overhead motion capture system to obtain the global position observation for all agents.”
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning (2018, background): “Traditional approaches for robot navigation often consider moving agents as static obstacles [1]–[4] or react to them through a one-step lookahead [5]–[7], resulting in short-sighted, unsafe and unnatural behaviors.”
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios (2020, methodology): “In addition, ORCA and its variants (Bareiss and van den Berg, 2015; Snape et al., 2011) used heuristics to construct a parametric collision-avoidance model, which are tedious to tune for satisfactory performance when a scenario contains heterogeneous robots with a wide variety of behaviors.”
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation (2015, background): “…environments emphasize deterministic and decoupled prediction algorithms (such as in LaValle, 2006, Latombe, 1991 and Choset et al., 2005), and are thus inappropriate for applications in highly uncertain environments or for situations in which the agent and the robot are dependent on one another.”