Yara Khaluf

Derivation of a Micro-Macro Link for Collective Decision-Making Systems

Relating microscopic features (individual level) to macroscopic features (swarm level) of self-organizing collective systems is challenging. In this paper, we report the mathematical derivation of a macroscopic model starting from a microscopic one for the example of collective decision-making. The collective system is based on the application of a majority rule over groups of variable size which is modeled by chemical reactions (micro-model). From an approximated master equation we derive the drift term of a stochastic differential equation (macro-model) which is applied to predict the expected swarm behavior. We give a recursive definition of the polynomials defining this drift term. Our results are validated by Gillespie simulations and simulations of the locust alignment.

Task Allocation Strategy for Time-Constrained Tasks in Robot Swarms

Task allocation is a key problem, which has a direct influence on the system performance in all kinds of distributed systems. This paper focuses on a specific kind of task allocation in swarm robotic systems, where the tasks are associated with specific time constraints. The paper presents a self-organized task allocation strategy, which aims to assign robot swarms to time-constrained tasks in a distributed manner. The robots assignment is performed based on particular specifications including task sizes and deadlines in addition to the specification of the single robot performance on the considered tasks. No central control is required to govern the swarm behaviour and no communication is exploited among robots.

Probabilistic analysis of long-term swarm performance under spatial interferences

Swarm robotics is a branch of collective robotics that outperforms many other systems due to its large number of robots. It allows for performing several tasks that are beyond the capability of a single or multi robot systems. Its global behaviour emerges from the local rules implemented on the level of its individual robots. Thus, estimating the obtained performance in a self-organized manner represents one of the main challenges, especially under complex dynamics like spatial interferences. In this paper, we exploit the central limit theorem (CLT) to analyse and estimate the swarm performance over long-term deadlines and under potential spatial interferences. The developed model is tested on the well-known foraging task, however, it can be generalized to be applied on any constrictive robotic task.

Self-Organized Cooperation in Swarm Robotics

Cooperation is a key concept used in multi-robot systems for performing complex tasks. In swarm robotics, a self-organized cooperation is applied, where robots with limited intelligence cooperate and interact locally to build up the desired global behavior. In this paper, we are studying a mobile object tracking scenario performed by a swarm of robots. The robustness, scalability and flexibility of swarm robots make it an attractive approach for missions like object tracking in complex and dynamic environments. As the individual robot capabilities are limited in swarm systems, the robots may not be able to track the mobile object continuously. This limitation is overcome using the robots communication capability. In order to increase the probability of object detection, we propose a greedy self-deployment strategy, where the robots are spread uniformly in the environment to be monitored. For detecting a moving target, the robots use a biologically inspired algorithm for collecting robots currently located in other regions to track the target. In such cooperative tasks the robots normally need to be time synchronized for simultaneous activation. A new proposal for time synchronization in swarm robots is introduced which exploits the mobility of the robots for handling possible disconnections in the network and synchronize them at the beginning of tracking time slots.

Analysis of long-term swarm performance based on short-term experiments

Swarm robotics is a branch of collective robotics systems that offers a set of remarkable advantages over other systems. The global behavior of swarm systems emerges from the local rules implemented at the individual level. Therefore, characterizing a global performance obtained at the swarm level is one of the main challenges, especially under complex dynamics such as spatial interferences. In this paper, we exploit the central limit theorem to analyze and characterize the swarm performance over long-term deadlines. The developed model is verified on two tasks: a foraging task and an object filtering task.

Modeling Robot Swarms Using Integrals of Birth-Death Processes

This article investigates the use of the integral of linear birth-death processes in the context of analyzing swarm robotics systems. We show that when a robot swarm can be modeled as a linear birth-death process, well-established results can be used to compute the expected value and/or the distribution of important swarm performance measures, such as the swarm activity time or the swarm energy consumption. We also show how the linear birth-death model can be used to estimate the long-term value of such performance measures and design robot controllers that satisfy constraints on these measures.

An Efficient Ant Colony System for Edge Detection in Image Processing.

Edge detection is a fundamental procedure in image processing, machine vision, and computer vision. Its application area ranges from astronomy to medicine in which isolating the objects of interest in the image is of a significant importance. However, performing edge detection is a non-trivial task for which a large number of techniques have been proposed to solve it. This paper investigates the use of Ant Colony Optimization — a prominent set of optimization heuristics — to solve the edge detection problem. We propose two modified versions of the algorithm Ant Colony System (ACS) for an efficient and a noise-free edge detection.

Swarm Robotic Time Synchronization for Object Tracking

Wireless sensor and ad-hoc networks have been integrated into many self-organized tasks, including self-organized real-time tasks. Swarm robotics is a new field of research, which offers a set of advantages like motion, redundancy, flexibility, etc. compared to both sensor networks and ad-hoc ones. On the other hand, there are some difficulties in directly using swarm robotics for these kinds of tasks without modifying or even extending some of the strategies and protocols used in wireless sensor and ad-hoc networks. Time synchronization may serve as a prominent example of extensions needed to fit swarm systems. Our article focuses on employing swarm robotics in self-organized object tracking tasks. We develop a new strategy for overcoming the effect of the high degree of motion in swarm robotics via applying time synchronization protocols.

Master Election for Time Synchronization in Swarm Robotic Systems

Swarm robotics is a new area of research which has gained a lot of interest according to the advantages it offers over current distributed systems. It is becoming in many wireless distributed applications, the potential alternative for the existing systems. Time synchronization as a key concept in distributed systems, will be a main requirement in swarm robotics, as soon as the latter is going to substitute any of the current distributed systems. Most of time synchronization protocols are master-based ones, where a master node is selected and is assumed to hold the time reference, which is used to synchronize other nodes. Importing such time synchronization protocols to the high dynamic swarm systems will introduce serious challenges based on their intensive degree of motion. The Master election process will be required continuously because of the permanent connections and disconnections of the swarms, which implies intensive communication overhead. Reducing the communication overhead, saving the robots energy and achieving a single time scale at each swarm connection/disconnection is the main goal of this work.

A swarm robotics approach to task allocation under soft deadlines and negligible switching costs

Developing swarm robotics systems for real-time applications is a challenging mission. Task deadlines are among the kind of constraints which characterize a large set of real applications. This paper focuses on devising and analyzing a task allocation strategy that allows swarm robotics systems to execute tasks characterized by soft deadlines and to minimize the costs associated with missing the task deadlines.