The popularity of rule-based flocking models, such as Reynolds classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational models. We introduce a very simple control law for flocking based on a cost function capturing cohesion (agents want to stay together) and separation (agents do not want to get too close). We refer to it as declarative flocking (DF).We use model-predictive control (MPC) to define controllers for DF in centralized and distributed settings. A thorough performance comparison of our DF-based approach with Reynolds model, and with more recent flocking models that use MPC with a cost function based on lattice structures, demonstrate that DF-MPC yields the best cohesion and least fragmentation, and maintains a surprisingly good level of geometric regularity while still producing natural flock shapes similar to those produced by Reynolds model. We also show that DF-MPC has high resilience to sensor noise.
In Proc. of SAC'18, the 33rd ACM Symposium On Applied Computing, Pau, France, April, 2018, ACM.
*This work was partially supported by the NSF-Frontiers Cyber-Cardia
Award, the US-AFOSR Arrive Award, the EU-Artemis EMC2 Award, the
EU-Ecsel Semi40 Award, the EU-Ecsel Productive 4.0 Award, the
AT-FWF-NFN RiSE Award, the AT-FWF-LogicCS-DC Award, the AT-FFG
Harmonia Award, the AT-FFG Em2Apps Award, and the TUW-CPPS-DK Award.