Declarative vs rule-based control for flocking dynamics

Abstract

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 declarative flocking with Reynolds' classic flocking 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. ACM Reference Format:

Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers

“…Model-Predictive control (MPC) [2] is a well-known control technique that has recently been applied to the flocking problem [11,19,20]. At each control step, an optimization problem is solved to find the optimal sequence of control actions (agent accelerations in our case) that minimizes a given cost function with respect to a predictive model of the system.…”

“…Declarative flocking (DF) is a high-level approach to designing flocking algorithms based on defining a suitable cost function for MPC [11]. This is in contrast to the operational approach, where a set of rules are used to capture flocking behavior, as in Reynolds model.…”

“…We subsequently showed that a simpler, more declarative approach to the flocking problem is possible [11]. In this setting, flocking is achieved when the agents combine to minimize a system-wide cost function.…”

“…We formulate and evaluate NF in a number of essential flocking scenarios: basic flocking with inter-agent collision avoidance, as in [11], and more advanced scenarios with additional objectives, including obstacle avoidance, predator avoidance, and target seeking by the flock. We conduct an extensive performance evaluation of NF.…”

The Influence of Limited Visual Sensing on the Reynolds Flocking Algorithm

“…To evaluate the collective performance, four relevant metrics are introduced. Several metrics for describing the correlation of the agents' movements and the collision risk have been adopted in the literature for describing both robotic systems and animal groups [23], [35], [15]. Furthermore, in swarm robotics, the algebraic connectivity has proven to be useful in many applications, especially the ones where a flow of information has to be ensured among all the robots of the swarm (e.g., exploration and target tracking).…”