Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
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IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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IEEE Transactions on Robotics
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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.