Need-to-Know Notes: Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (Full) A project by Miri Kim and Luis Angel as part of the course CS 269: Humanoid Computer Animation.

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Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (Full) A project by Miri Kim and Luis Angel as part of the course CS 269: Humanoid Computer Animation.

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  • Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (Full)
  • A project by Miri Kim and Luis Angel as part of the course CS 269: Humanoid Computer Animation.

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ClearPath: Highly Parallel Collision Avoidance for Multi-agent Simulation

ClearPath: Highly Parallel Collision Avoidance for Multi-agent Simulation

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Multi-Agent Simulation Using Continuum Crowds and the ClearPath Method

Multi-Agent Simulation Using Continuum Crowds and the ClearPath Method

Evacuating a building. A project by Miri Kim and Luis Angel as part of the course CS 269: Humanoid Computer Animation.

Multi-Agent Simulation Using Continuum Crowds and the ClearPath Method

Multi-Agent Simulation Using Continuum Crowds and the ClearPath Method

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Multi-Agent Simulation Using Continuum Crowds and the ClearPath Method

Multi-Agent Simulation Using Continuum Crowds and the ClearPath Method

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Optimal Reciprocal Collision Avoidance basic implementation

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Distributed Multi-agent Navigation Based on ORCA and MAPF solving

Distributed Multi-agent Navigation Based on ORCA and MAPF solving

Theta* for geometric path planning. ORCA for path following with

Optimal Reciprocal Collision Avoidance

Optimal Reciprocal Collision Avoidance

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Multi-agent navigation with reciprocal collision avoidance based on velocity obstacle

Multi-agent navigation with reciprocal collision avoidance based on velocity obstacle

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Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (Full)

Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (Full)

Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation (Full)