A motion planning method for simulating a virtual crowd

Abstract

A model of motion planning for agent-based crowd simulation is one of the key techniques for simulating how an agent selects its velocity to move towards a given goal in each simulation time step. If there is no on-coming collision with other agents or obstacles around, the agent moves towards the designated goal directly with the desired speed and direction. However, the desired velocity may lead the agent to collide with other agents or obstacles, especially in a crowded scenario. In this case, the agent needs to adjust its velocity to avoid potential collisions, which is the main issue that a motion planning model needs to consider. This paper proposes a method for modelling how an agent conducts motion planning to generate velocity for agent-based crowd simulation, including collision detection, valid velocity set determination, velocity sampling, and velocity evaluation. In addition, the proposed method allows the agent to really collide with other agents. The performance of the model has been proven to be efficient.-

Congestion-free multi-agent navigation based on velocity space by using cellular automata

“…Although the RVO model is robust and efficient, it is not perfect. Therefore, many extensions have been proposed, such as incorporating an actual collision response model (Xiong & Tang, 2014), a goal-oriented velocity determination model (Snape & Manocha, 2013), human kinematic constraints (Guy, Lin, & Manocha, 2010) and a physical model (Kim, Guy, & Manocha, 2013). However, an agent-based model can accurately simulate only local trajectories of the agents.…”