V-like formations in flocks of artificial birds

We consider flocks of artificial birds and study the emergence of V-like formations during flight. We introduce a small set of fully distributed positioning rules to guide the birds' movements and demonstrate, by means of simulations, that they tend to lead to stabilization into several of the well-known V-like formations that have been observed in nature. We also provide quantitative indicators that we believe are closely related to achieving V-like formations, and study their behavior over a large set of independent simulations

Effects of anisotropic interactions on the structure of animal groups

This paper proposes an agent-based model which reproduces different structures of animal groups. The shape and structure of the group is the effect of simple interaction rules among individuals: each animal deploys itself depending on the position of a limited number of close group mates. The proposed model is shown to produce clustered formations, as well as lines and V-like formations. The key factors which trigger the onset of different patterns are argued to be the relative strength of attraction and repulsion forces and, most important, the anisotropy in their application.Comment: 22 pages, 9 figures. Submitted. v1-v4: revised presentation; extended simulations; included technical results. v5: added a few clarification

ARES:Adaptive receding-horizon synthesis of optimal plans

We introduce ARES, an efficient approximation algorithm for generating optimal plans (action sequences) that take an initial state of a Markov Decision Process (MDP) to a state whose cost is below a specified (convergence) threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both the receding horizon and the particle swarm. Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state. The level relation on states and the plans constructed by ARES implicitly define a Lyapunov function and an optimal policy, respectively, both of which could be explicitly generated by applying ARES to all states of the MDP, up to some topological equivalence relation. We also assess the effectiveness of ARES by statistically evaluating its rate of success in generating optimal plans. The ARES algorithm resulted from our desire to clarify if flying in V-formation is a flocking policy that optimizes energy conservation, clear view, and velocity alignment. That is, we were interested to see if one could find optimal plans that bring a flock from an arbitrary initial state to a state exhibiting a single connected V-formation. For flocks with 7 birds, ARES is able to generate a plan that leads to a V-formation in 95% of the 8,000 random initial configurations within 63 s, on average. ARES can also be easily customized into a model-predictive controller (MPC) with an adaptive receding horizon and statistical guarantees of convergence. To the best of our knowledge, our adaptive-sizing approach is the first to provide convergence guarantees in receding-horizon techniques

Numerical investigation of the aerodynamic breakup of droplets in tandem

The present work examines the aerodynamic breakup of four liquid droplets in tandem formation at Diesel engine conditions using the Volume of Fluid (VOF) method. The examined Weber (We) numbers range from 15 up to 64 and the non-dimensional distances between the droplet centres (L/D0) vary from 1.25 up to 20. Focus is given on the breakup process of the third droplet of the row, which is regarded as a “representative chain droplet”; its development is compared against that of an isolated droplet at the same flow conditions. It is found that for small distances and depending on the We number, the obtained shapes and breakup modes between the droplets are different, with the representative chain droplet experiencing a new breakup mode in the multi-mode regime, termed as “shuttlecock”. This is characterized by an oblique peripheral stretching of the droplet caused by the acting of pressure forces at an off-centre region. Moreover, the drag coefficient and liquid surface area of the representative chain droplet are lower than the corresponding ones of the isolated droplet, while the breakup initiation time is longer and the minimum We number required for breakup (critical We) is higher; correlations are provided to quantify the effect of droplet distance on the aforementioned quantities. Generally, the droplet proximity becomes important for L/D0< 9. Finally, the predicted drag coefficient is utilised in a simplified 0-D model that is capable of estimating the temporal evolution of droplet velocity of the representative chain droplet

Robust Optimization and Groundwork for Problem Mapping

Goals of this research were to develop a conceptual algorithm that can optimize execution time for generating a solution set and demonstrate that a solution set of one sub-problem can be applied to another sub-problem within the same problem set. To achieve the proposed goals, GloPro was developed to generate rule sets for different sub-problems within a problem set, as well as identifying which rule sets are to be utilized for a given instance of the problem. The algorithm was to be robust, as to be applicable to a wide array of problems without radical re-design per problem. This idea was fueled by the concept of Structure-Mapping Theory, where a set of knowledge is mapped from one domain to another based on the shared baseline characteristics. Utilizing a Genetic Algorithm (GA), plus A* with a classifier hybrid, the algorithm includes a period of supervised learning followed by execution in an operational environment. Progressive learning occurred through application of the algorithm to multiple sub-problems, each having unique characteristics. The algorithm was applied to a simulated robotic agent in a maze environment as a proxy for other problems. This problem is well known, but still an active problem in the field of robotics. The experimental results indicate that the hybrid GA with A* technique is feasible, and that progressive learning is enhanced through application of previous learning results to a period of learning. In addition, the evolved solutions were unique to the sub-problems, indicating that this technique can be used to develop robust solutions across sub-problems

Integrating UAS Flocking Operations with Formation Drag Reduction

Craig Reynolds, in the seminal research into simulated flocking, developed a methodology to guide a flock of birds using three rules: collision avoidance, flock centering, and velocity matching. By modifying these rules, a methodology was created so that each aircraft in a flock maintains a precise position relative to the preceding aircraft. By doing so, each aircraft experiences a decrease in induced aerodynamic drag and increase in fuel efficiency. Flocks of semi-autonomous aircraft present the warfighter with a wide array of capabilities for accomplishing missions more effectively. By introducing formation drag reduction, overall fuel consumption is reduced while range and endurance increase, expanding war planners\u27 options. A simulation was constructed to determine the feasibility of the drag reduction flock in a two-dimensional environment using a drag benefit map constructed from existing research. Due to both agent interaction and wind gust variability, the optimal position for drag reduction presented a severe collision hazard, and drag savings were much more sensitive to lateral (wingtip) position than longitudinal (streamwise) position. By increasing longitudinal spacing, the collision hazard was greatly reduced and a 10-aircraft flock demonstrated a 9.7% reduction in total drag and 14.5% increase in endurance over a mock target

Fly with me : algorithms and methods for influencing a flock

As robots become more affordable, they will begin to exist in the world in greater quantities. Some of these robots will likely be designed to act as components in specific teams. These teams could work on tasks that are too large or complex for a single robot - or that are merely more efficiently accomplished by a team - such as surveillance in a large building or product delivery to packers in a warehouse. Multiagent systems research studies how these teams are formed and how they work together. Ad hoc teamwork, a newer area of multiagent systems research, studies how new robots can join these pre-existing teams and assist the team in accomplishing its goal. This dissertation extends and applies research in ad hoc teamwork towards the general area of flocking, which is an emergent swarm behavior. In particular, the work in this dissertation considers how ad hoc agents - called influencing agents in this dissertation - can join a flock, be recognized by the rest of the flock as part of the flock, influence the flock towards particular behaviors through their own behavior, and then separate from the flock. Specifically, the primary research question addressed in this dissertation is How can influencing agents be utilized in various types of flocks to influence the flock towards a particular behavior? In order to address this research question, this dissertation makes six main types of contributions. First, this dissertation formalizes the problem of using influencing agents to influence a flock. Second, this dissertation contributes and analyzes algorithms for influencing a flock to a desired orientation. Third, this dissertation presents methods for determining how to best add influencing agents to a flock. Fourth, this dissertation provides methods by which influencing agents can join and then leave a flock in motion. Fifth, this dissertation evaluates some of the influencing agent algorithms on a robot platform. Sixth, although the majority of this dissertation assumes the influencing agents will join a flock that behaves similarly to European starlings, this dissertation also provides insight into when and how its algorithms are generalizable to other types of flocks as well as to general teamwork and coordination research. All of the methods presented in this dissertation are empirically evaluated using a simulator that can support large flocks.Computer Science