Abstract
In this paper, we present a new motion planning strategy for shepherding in environments containing obstacles. This instance of the group motion control problem is applicable to a wide variety of real life scenarios, such as animal herding simulation, civil crowd control training, and oil-spill cleanup simulation. However, the problem is challenging in terms of scalability and robustness because it is dynamic, highly underactuated, and involves multi-agent coordination. Our previous work showed that high-level probabilistic motion planning algorithms combined with simple shepherding behaviors can be beneficial in situations where low-level behaviors alone are insufficient. However, inconsistent results suggested a need for a method that performs well across a wider range of environments. In this paper, we present a new method, called Deform, in which shepherds view the flock as an abstracted deformable shape. We show that our method is more robust than our previous approach and that it scales more effectively to larger teams of shepherds and larger flocks. We also show Deform to be surprisingly robust despite increasing randomness in the motion of the flock.
This work is partially supported by NSF IIS-096053.
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Harrison, J.F., Vo, C., Lien, JM. (2010). Scalable and Robust Shepherding via Deformable Shapes. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds) Motion in Games. MIG 2010. Lecture Notes in Computer Science, vol 6459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16958-8_21
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