Musad A. Haque

Biologically inspired confinement of multi-robot systems

Confinement of a group of mobile robots is of significant interest to the multi-agent robotics community. We develop confinement strategies through simple biological models; in particular, we draw inspiration from the foraging techniques used by bottlenose dolphins to catch fish. For a multi-agent system, we achieve the following goals: 1) provide an algorithm for one group of agents to perpetually confine the other group; 2) characterise the regions from which the herded agents are guaranteed to be captured. The simplicity of the model allows easy implementation in engineered devices (e.g., exploiting the collision avoidance modules already embedded in unmanned air and ground vehicles) and the richness of the model allows replication of a complex biological phenomenon, such as capturing of prey.

Geometric foraging strategies in multi-agent systems based on biological models

In nature, communal hunting is often performed by predators by charging through an aggregation of prey. However, it has been noticed that variations exist in the geometric shape of the charging front; in addition, distinct differences arise between the shapes depending on the particulars of the feeding strategy. For example, each member of a dolphin foraging group must contribute to the hunt and will only be able to eat what it catches. On the other hand, some lions earn a “free lunch” by feigning help and later feasting on the prey caught by the more skilled hunters in the foraging group. We model the charging front of the predators as a curve moving through a prey density modeled as a reaction-diffusion process and we optimize the shape of the charging front in both the free lunch and no-free-lunch cases. These different situations are simulated under a number of varied types of predator-prey interaction models, and connections are made to multi-agent robot systems.

Multilevel Coalition Formation Strategy for Suppression of Enemy Air Defenses Missions

This paper investigates the problem of how to form coalitions in teams of heterogeneous vehicles. In particular, a coordination strategy is designed for unmanned vehicles to autonomously carry out the suppression of enemy air defenses (SEAD) mission, which would benefit from a heterogeneous network of unmanned vehicles to search and destroy threats in an unexplored area. Inspiration for this work is drawn from natural systems, and we consider the alliance-forming behavior of bottlenose dolphins as a guiding example. A two-phased approach is taken to develop the bioinspired strategy. First, in the context of multi-agent systems, a mathematical model is produced that expressively captures the alliance-forming behavior. Next, this model is tailored to the suppression of enemy air defenses mission: the target application. Advantages of using this bioinspired approach are discussed and simulations are provided to demonstrate its operation.

A Hybrid, Multi-Agent Model of Foraging Bottlenose Dolphins

Abstract Social behavior of animals can offer solution models for missions involving a large number of heterogeneous vehicles, such as light combat ships, unmanned aerial vehicles, and unmanned underwater vehicles. We draw inspiration from the foraging techniques of bottlenose dolphins to address the problem of heterogeneous multi-agent herding. We produce a hybrid automaton model of the entire foraging method - search, detect, and capture - where agents are modeled as first-order systems in which interactions are defined through spatial proximity. Finally, simulations are provided to illustrate that our model is expressive enough to capture this complex biological phenomenon.

First-order, networked control models of swarming silkworm moths

Social insects have long served as inspiration to the multi-agent community. In this paper, we take the opposite approach and see if tools from decentralized, networked control can be used to predict observed, biological behaviors. In particular, we study the silkworm moth, the Bombyx Mori, and we model these moths as first-order networks in which the male- male interactions are defined through a proximity graph. The male-female interactions are given by a broadcast protocol in which the females that are releasing pheromones are visible to all the males. Using barrier certificates, the resulting, switched network is analyzed and it is shown that the males are attracted to and trapped in a region defined by the female moths, as is the case in actual silkworm moths as well.

Coalition formation in multi-agent systems based on bottlenose dolphin alliances

Male bottlenose dolphins, Tursiops truncatus, found off the coast of Western Australia and Florida, often form varied levels of alliances to capture females and increase their chances of mating. One such alliance, known as the first-order alliance, consists of 2–3 dolphins that share a very strong “bond”, formally known as the Association coefficient in behavioral biology. We formalize factors that affect the coefficient, and analyze their influence in building alliances in the context of multi-agent coalition formation. We produce a model of the first-order alliance as a hybrid automaton, based solely on local information evolving over spatially defined interaction topologies, where the model is expressive enough to capture the biological phenomenon, yet simple enough to derive results through analysis.

Efficient Foraging Strategies in Multi-Agent Systems Through Curve Evolutions

In nature, communal hunting is often performed by predators charging through an aggregation of prey. Variations exist in the geometric shape of the charging front depending on the particulars of the feeding strategy. Inspired by biology, this technical note investigates these geometric variations, and we model the predator front as a curve moving through a prey density. Using variational arguments for evolving the curve shape, we optimize the shape of the front.

Biologically motivated shape optimization of foraging fronts

Social animals often form a predator front to charge through an aggregation of prey. It is observed that the nature of the feeding strategy dictates the geometric shape of these charging fronts. Inspired by this observation, we model foraging multi-robot fronts as a curve moving through a prey density. We optimize the shape of the curve using variational arguments and simulate the results to illustrate the operation of the proposed curve optimization algorithm.

Optimization of foraging multi-agent system front: A flux-based curve evolution method

Numerous social foragers form a foraging front that sweeps through the aggregation of prey. Based on this strategy, and using variational arguments, we develop an algorithm to provide a group-level specification of the shape of the sweeping front for a foraging multi-robot system. The presented flux-based algorithm has the desired property of generating more regular shapes than previously introduced algorithms.