Segregation of multiple heterogeneous units in a robotic swarm

We present a decentralized algorithm to achieve segregation into an arbitrary number of groups with swarms of autonomous robots. The distinguishing feature of our approach is in the minimalistic assumptions on which it is based. Specifically, we assume that (i) Each robot is equipped with a ternary sensor capable of detecting the presence of a single nearby robot, and, if that robot is present, whether or not it belongs to the same group as the sensing robot; (ii) The robots move according to a differential drive model; and (iii) The structure of the control system is purely reactive, and it maps directly the sensor readings to the wheel speeds with a simple 'if' statement. We present a thorough analysis of the parameter space that enables this behavior to emerge, along with conditions for guaranteed convergence and a study of nonideal aspects in the robot design.

show abstract

“…Santos et al [18] took inspiration from [17] to devise an approach based on the Differential Adhesion Hypothesis, S=1 S=2 S=0 Fig. 1.…”

Section: Related Workmentioning

confidence: 99%

A Minimalistic Approach to Segregation in Robot Swarms

Mitrano

Burklund

Giancola

et al. 2019

2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

View full text Add to dashboard Cite

show abstract

“…Up until relatively recently, the field of swarm robotics has almost exclusively been studied by viewing a system of homogenous agents [18,23]. This has been due to their ability to be easily conceptualized [17].…”

Section: Heterogeneous Swarmsmentioning

confidence: 99%

“…Some examples of heterogeneous behavior can be found in the selforganization present in some biological cells [23,25,26], as well as by viewing cases of natural symbiosis [17].…”

Section: Heterogeneous Swarmsmentioning

confidence: 99%

“…There has been some work conducted in controlling the swarm as a single unit, essentially adapting traditional PSO algorithms that are used by many swarms today [17]. Most of the research into these types of swarms has been involved around either segregating the agents into like groups, or assuming that the agents are already segregated into groups of like agents [23,25,26]. The main reasoning behind segregating these groups is that the system becomes much easier to organize, as each of these groups can be controlled individually, as well as making it easier to assign the different groups to different resources.…”

Section: Heterogeneous Swarmsmentioning

confidence: 99%

See 1 more Smart Citation

Underwater swarm robotics review

Champion

Joordens

2015

2015 10th System of Systems Engineering Conference (SoSE)

View full text Add to dashboard Cite

Underwater robotics is a growing field in which more research is required. A literature review has been conducted on underwater robotics, focusing on the swarm problem with this type of robotics to help overcome this gap. Consensus control of robotic swarms is focused on, with a brief description of formation control and how it can be applied in the underwater setting. The basic concepts behind Particle Swarm Optimization, Ant Colony Optimization, Bees Algorithm and Heterogeneous Swarms has also been presented. The problems that are associated with communicating underwater are shown, with some possible solutions to this problem also being presented. Possible future work is described to conclude the paper. 2015 10th System of Systems Engineering Conference (SoSE) 978-1-4799-7611-9/15/$31.00 ©2015 IEEE effective communication for a swarm. The water quality that the transceiver is in can also make a difference, as when a 75MHz transceiver is used in a chlorinated pool the range of communication is only 2.7m(9') and when the same transceiver is used in a salt water pool, the range of communication is reduced further down to 0.3m(1') [30]. This shows that having water of different conductivity levels change the way that range that communication can be achieved at.Communication can be generally split into two different categories, these being direct and indirect communication [13,21]. The most common form of communication is direct communication. This is where two or more agents have some kind of link between them, whether it be wired or wireless, that allows the two agents to transmit data from one to the other in real time. In most form of robotics this is how communication is achieved. This can be viewed as how two people have a conversation with one another.The other type of communication, indirect communication, is where other means are used to transmit the data from one robot o the other. Some methods that can be used to do is could be robot grouping, intensity of light and modifying the environment to pass a message on. This type of message is not often used as it is slow, can suffer highly from noise and can be much harder for the robot to interpret, causing situations where the robot possibly misses the message. This can be viewed as someone leaving a note for another person to find and read, as opposed to them directly speaking to each other.Due to the limitation that water imposes on communication other forms of communication have been

show abstract

“…Additionally, heterogeneity may arise over time as some agents in the swarm malfunction [25]. An approach to swarm splitting based on the cell segregation model [23] is used to design a potential-based controller that achieves segregation in swarms of self-propelled autonomous robots [26], [27]. Other approaches to swarm segregation utilize graph theory [28], or stochastic processes based on the "Brazil nut effect" [29].…”

Section: Introductionmentioning

confidence: 99%

Collective Motions of Heterogeneous Swarms

Szwaykowska

Romero

Schwartz

2015

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

The emerging collective motions of swarms of interacting agents are a subject of great interest in application areas ranging from biology to physics and robotics. In this paper, we conduct a careful analysis of the collective dynamics of a swarm of self-propelled heterogeneous, delay-coupled agents. We show the emergence of collective motion patterns and segregation of populations of agents with different dynamic properties; both of these behaviors (pattern formation and segregation) emerge naturally in our model, which is based on self-propulsion and attractive pairwise interactions between agents. We derive the bifurcation structure for emergence of different swarming behaviors in the mean field as a function of physical parameters and verify these results through simulation.Note to Practitioners-Our research deals with understanding the emerging behaviors of groups of simple, interacting agents. The motivation for studying this subject is twofold. First, we seek an understanding of the mechanisms that govern collective motions of biological organisms in processes like wound healing, cancer growth, flocking and herding, etc. Second, we plan to apply our insights to the design of controllers for swarms of autonomous robotic agents programmed to carry out different tasks, such as area surveillance or monitoring in uncertain environments. Swarming behavior is typically modeled for groups of identical agents, under the assumption that sensing and processing times are negligibly small. We incorporate the real-world complications of: 1) finite sensing/processing time, which appears as a delay in our model of agent motion and 2) differences in the dynamical capabilities of swarming agents. We conduct a theoretical analysis of the collective motions of the swarm. We show the emergence of large-scale patterns in the swarm motion as a function of the physical parameters of the swarm, as well as segregation of the agents into separate groups where all agents in a given group have identical dynamics.

show abstract

Segregation of multiple heterogeneous units in a robotic swarm

Cited by 21 publications

References 17 publications

A Minimalistic Approach to Segregation in Robot Swarms

A Minimalistic Approach to Segregation in Robot Swarms

Underwater swarm robotics review

Collective Motions of Heterogeneous Swarms

Contact Info

Product

Resources

About