Motion planning in crowds using statistical model checking to enhance the social force model

Crowded environments pose a challenge to the comfort and safety of those with impaired ability. To address this challenge we have developed an efficient algorithm that may be embedded in a portable device. The algorithm anticipates undesirable circumstances in real time, by verifying simulation traces of local crowd dynamics against temporal logical formulae. The model incorporates the objectives of the user, pre-existing knowledge of the environment and real time sensor data. The algorithm is thus able to suggest a course of action to achieve the user's changing goals, while minimising the probability of problems for the user and others in the environment. To demonstrate our algorithm we have implemented it in an autonomous computing device that we show is able to negotiate complex virtual environments. The performance of our implementation demonstrates that our technology can be successfully applied in a portable device or robot.