ClearPath extends the velocity obstacle from robotics and formulates the conditions for collision-free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent and parallelize the algorithm by exploiting data and thread-level parallelism. ClearPath can robustly handle dense scenarios with hundreds of thousands of heterogeneous agents in a few milliseconds. Scenes such as trees, houses are quite similar to reality.
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Crowd and multi-agent simulation is the process of simulating large numbers of people, creatures, or other characters, each interacting in one environment. These actors are expected to move to their goals, interact with their environment, and respond to each other. Our group has worked on many problems in crowd simulation, including fast, guaranteed, collision avoidance, real-time path and motion planning, crowd flows, and directed behaviors.
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Our approach aims to generate pedestrian trajectories that correspond to the speed/density relationships that are typically expressed using the Fundamental Diagram. The algorithm’s formulation can be easily combined with well-known multi-agent simulation techniques that use social forces or reciprocal velocity obstacles for local navigation. Our approach results in better utilization of free space by the pedestrians and has a small computational overhead. We are able to generate human-like dense crowd behaviors in large indoor and outdoor environments; we validate our results by comparing them with captured crowd trajectories. Selecting the optimal action for each agent is reduced to solving a low-dimensional linear program, and we prove that the resulting motions are smooth.
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