A Neural-Endocrine Architecture for Foraging in Swarm Robotic Systems

Abstract This paper presents the novel use of the Neural-endocrine architecture for swarm robotic systems. We make use of a number of behaviours to give rise to emergent swarm behaviour to allow a swarm of robots to collaborate in the task of foraging. Results show that the architecture is amenable to such a task, with the swarm being able to successfully complete the required task.

A Hormone Inspired System for On-line Adaptation in Swarm Robotic Systems

Individual robots, while providing the opportunity to develop a bespoke and specialised system, suffer in terms of performance when it comes to executing a large number of concurrent tasks. In some cases it is possible to drastically increase the speed of task execution by adding more agents to a system, however this comes at a cost. By mass producing relatively simple robots, costs can be kept low while still gaining the benefit of large scale multi-tasking. This approach sits at the core of swarm robotics. Robot swarms excel in tasks that rely heavily on their ability to multi-task, rather than applications that require bespoke actuation. Swarm suited tasks include: exploration, transportation or operation in dangerous environments. Swarms are particularly suited to hazardous environments due to the inherent expendability that comes with having multiple, decentralised agents. However, due to the variance in the environments a swarm may explore and their need to remain decentralised, a level of adaptability is required of them that can't be provided before a task begins. Methods of novel hormone-inspired robotic control are proposed in this thesis, offering solutions to these problems. These hormone inspired systems, or virtual hormones, provide an on-line method for adaptation that operates while a task is executed. These virtual hormones respond to environmental interactions. Then, through a mixture of decay and stimulant, provide values that grant contextually relevant information to individual robots. These values can then be used in decision making regarding parameters and behavioural changes. The hormone inspired systems presented in this thesis are found to be effective in mid-task adaptation, allowing robots to improve their effectiveness with minimal user interaction. It is also found that it is possible to deploy amalgamations of multiple hormone systems, controlling robots at multiple levels, enabling swarms to achieve strong, energy-efficient, performance

Bioinspired approaches for coordination and behaviour adaptation of aerial robot swarms

Behavioural adaptation is a pervasive component in a myriad of animal societies. A well-known strategy, known as Levy Walk, has been commonly linked to such adaptation in foraging animals, where the motion of individuals couples periods of localized search and long straight forward motions. Despite the vast number of studies on Levy Walks in computational ecology, it was only in the past decade that the first studies applied this concept to robotics tasks. Therefore, this Thesis draws inspiration from the Levy Walk behaviour, and its recent applications to robotics, to design biologically inspired models for two swarm robotics tasks, aiming at increasing the performance with respect to the state of the art. The first task is cooperative surveillance, where the aim is to deploy a swarm so that at any point in time regions of the domain are observed by multiple robots simultaneously. One of the contributions of this Thesis, is the Levy Swarm Algorithm that augments the concept of Levy Walk to include the Reynolds’ flocking rules and achieve both exploration and coordination in a swarm of unmanned aerial vehicles. The second task is adaptive foraging in environments of clustered rewards. In such environments behavioural adaptation is of paramount importance to modulate the transition between exploitation and exploration. Nature enables these adaptive changes by coupling the behaviour to the fluctuation of hormones that are mostly regulated by the endocrine system. This Thesis draws further inspiration from Nature and proposes a second model, the Endocrine Levy Walk, that employs an Artificial Endocrine System as a modulating mechanism of Levy Walk behaviour. The Endocrine Levy Walk is compared with the Yuragi model (Nurzaman et al., 2010), in both simulated and physical experiments where it shows its increased performance in terms of search efficiency, energy efficiency and number of rewards found. The Endocrine Levy Walk is then augmented to consider social interactions between members of the swarm by mimicking the behaviour of fireflies, where individuals attract others when finding suitable environmental conditions. This extended model, the Endocrine Levy Firefly, is compared to the Levy+ model (Sutantyo et al., 2013) and the Adaptive Collective Levy Walk Nauta et al. (2020). This comparison is also made both in simulated and physical experiments and assessed in terms of search efficiency, number of rewards found and cluster search efficiency, strengthening the argument in favour of the Endocrine Levy Firefly as a promising approach to tackle collaborative foragin

Distributed Cognition as the Basis for Adaptation and Homeostasis in Robots

Many researchers approach the problem of building autonomous systems by looking to biology for inspiration. This has given rise to a wide-range of artificial systems mimicking their biological counterparts—artificial neural networks, artificial endocrine systems, and artificial musculoskeletal systems are prime examples. While these systems are succinct and work well in isolation, they can become cumbersome and complicated when combined to perform more complex tasks. Autonomous behaviour is one such complex task. This thesis considers autonomy as the complex behaviour it is, and proposes a bottom-up approach to developing autonomous behaviour from cognition. This consists of investigating how cognition can provide new approaches to the current limitations of swarm systems, and using this as the basis for one type of autonomous behaviour: artificial homeostasis. Distributed cognition, a form of emergent cognition, is most often described in terms of the immune system and social insects. By taking inspiration from distributed cognition, this thesis details the development of novel algorithms for cognitive decision-making and emergent identity in leaderless, homogenous swarms. Artificial homeostasis is provided to a robot through an architecture that combines the cognitive decision-making algorithm with a simple associative memory. This architecture is used to demonstrate how a simple architecture can endow a robot with the capacity to adapt to an unseen environment, and use that information to proactively seek out what it needs from the environment in order to maintain its internal state

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

HCC Architecture - Hormonal Communications and Control Architecture

This thesis aims to provide a novel framework for a multiagent system implementation. The major feature of the proposed architecture is the introduction of the biological concept of hormones. The hormones are passed via the communication network to convey limited global system state knowledge. The agents\u27 response to a hormone is interpreted depending on its own local agent state. The primary focus of this thesis is the development of the particulars of the architecture. Prior work of multiagent systems research is reviewed and studied for contributions. Biological studies of hormones are employed to draw out interaction rules and analyze control mechanisms in a biological organism. The hormonal communication and control architecture is constructed, with major components detailed by flowcharts. The proposal is tested with two simulations: A minesweeping problem that has been modeled by other models, and an application of the architecture to a hypothetical ant colony. Research on biological ants is presented to suggest the behavior and goals of a model configured to employ the HCC architecture. The model is fleshed out, and the decisions made by considerations to the architecture are explained. The implementation of the simulation programming with the SWARM programming libraries for the Objective-C language is discussed. The data from experimental runs are analyzed with attention to global action

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Verification of the emergence in an architecture for multi-robot systems (AMEB)

This article analyzes the emerging behavior of a multi-robot system managed by an architecture structured in three layers: the first provides local support to the robot, manages its processes of action, perception and communication, as well as its behavioral aspect, which considers the reactive, cognitive and social aspects of the robot. In addition, it introduces an affective component that influences its behavior and the way it relates to the environment and to the other individuals in the system, based on an emotional model that takes into account fourArticle history:Received 12 September 2018Accepted 08 November 2018A Gil, pertenece al Laboratorio de Prototipos en la Universidad Nacional Experimental del Táchira y a Tepuy R+D Group. Artificial Intelligence Software Development. Mérida, Venezuela (email: [email protected])basic emotions. The second provides support to the collective processes of the system, based on the concept of emerging coordination. The latter is responsible for knowledge management and learning processes, both individually and collectively, in the system. In this article the metrics are defined to verify the emergency in the system, by means of the use of a method of verification of emergent behaviors based on Fuzzy Cognitive Maps

Self-sufficiency of an autonomous self-reconfigurable modular robotic organism

In recent years, getting inspiration from simple but complex biological organisms, several advances have been seen in autonomous systems to mimic different behaviors that emerge from the interactions of a large group of simple individuals with each other and with the environment. Among several open issues a significantly important issue, not addressed so far, is the self-sufficiency, or in other words, the energetic autonomy of a modular robotic organism. This feature plays a pivotal role in maintaining a robotic organism\u27s autonomy for a longer period of time. To address the challenges of self-sufficiency, a novel dynamic power management system (PMS) with fault tolerant energy sharing is proposed, realized in the form of hardware and software, and tested. The innate fault tolerant feature of the proposed PMS ensures power sharing in an organism despite docked faulty robotic modules. Due to the unavailability of sufficient number of real robotic modules a simulation framework called Replicator Power Flow Simulator is devised for the implementation of application software layer power management components. The simulation framework was especially devised because at the time of writing this work no simulation tool was available that could be used to perform power sharing and fault tolerance experiments at an organism level. The simulation experiments showed that the proposed application software layer dynamic power sharing policies in combination with the distributed fault tolerance feature in addition to self-sufficiency are expected to enhance the robustness and stability of a real modular robotic organism under varying conditions.Inspiriert von einfachen aber komplexen biologischen Organismen wurden in den letzten Jahren verschiedenste autonome Systeme entwickelt, welche die Verhaltensweisen einer großen Gruppe einfacher Individuen nachahmen. Das zentrale und bis heute ungelöste Problem dieser Organismen ist deren autonome Energieversorgung. Zur Sicherstellung der Energieversorgung eines aus mehreren Robotern zusammengesetzten Organismus wurde in dieser Arbeit ein neuartiges Power-Management-System (PMS) konzipiert, aufgebaut und an einzelnen Robotermodulen und einem Roboterorganismus getestet. Die Hardware eines bestehenden Roboters wurde um ein neues Konzept erweitert, das auch bei fehlerhaften Robotermodulen einen Energieaustausch sicherstellt und so zu einer erhöhten Robustheit des PMS führen soll. Das entwickelte PMS wurde in modulare Roboter integriert und beispielhaft anhand eines Roboterorganismus getestet. In Ermangelung einer ausreichenden Anzahl von Robotermodulen wurde eine Simulationsumgebung entwickelt und die Software des PMS im Simulationsprogramm, anstatt im Roboter, implementiert. Dieses Simulationswerkzeug ist momentan das Einzige, das unter Berücksichtigung des Bewegungsmodells des Organismus den Energietransport im Roboterorganismus visuell darstellt und das Verhalten in verschiedenen Fehlerfällen simulieren kann. Die Simulationen und Messungen zeigen, dass das entwickelte PMS geeignet ist, die Energieversorgung von Roboterorganismen auch in Fehlerfällen sicherzustellen und so die Stabilität und Robustheit zu erhöhen

Improving Robot Team's performance by Passing Objects between Robots

Department of Computer Science and EngineeringA transport robot system is a robotic system in which robots move objects from one place to another place. Most existing transport robot systems perform three tasks: loading an item, moving to another location, and unloading the item. Traditional mobile robots, which carry objects one at a time, is not suitable for repeatedly transporting objects over a long distance. Therefore, in the factory or warehouse environment, they still use conveyor belts to transport a large number of objects. However, the existing conveyor belts are physically fixed in their environments, and it is difficult to reconfigure the layout of a conveyor network. In this thesis, I presente three new robotic systems that have the ability to pass objects at a distance between mobile robots. These three robotic systems are mobile conveyor belts, dynamic robot chains, and mobile workstations. First, conveyor belts are commonly used to transport many objects rapidly and effectively. I present a novel conveyor system called a mobile conveyor line that can autonomously organize itself to transport objects to a given location. In this thesis, I analyze the reachability of multiple mobile conveyor belts and present an algorithm to verify the reachability of a specified destination, as well as a way to gen- erate a configuration for connecting conveyor belts to reach the destination. The key results include a complete set of equations describing the reachable set of a mobile conveyor belt on a flat surface, which leads to an effective probabilistic strategy for autonomous configuration. The results of the experiment demonstrated the overlap effect, which states that reachable sets frequently overlap. This system can be suitable for locations where it is difficult to install a conveyor line, such as disaster zones. Second, I present to use mobile conveyor belts in foraging tasks in environments with obstacles. Foraging robots can form a dynamic robot chain network that can quickly send resources received from other foraging robots to a collecting zone called a depot area. A robot chain is essentially a sequence of mobile robots with the ability to quickly pass resources at a long distance. A dynamic robot chain network is a network of robot chains that allow the branches of the robot chains to connect multiple resource clusters. By allowing branching, the traffic near the end of the robot chain network can be dis- tributed to several branches, and congestion can be avoided. The dynamic robot chain network leverages mobility to relocate, reduce collection time for other robots, and quickly send resources received from other foraging robots to the depot area. The key result is the formation of robot chains capable of over- coming the two major limitations of existing dynamic depot foraging systems: the long travel distance for delivery and congestion near the central collection zone. In the experiments, given the same num- ber of robots, a dynamic robot chain network outperformed existing dynamic depots in multiple-place foraging problems. Third, I consider the idea of mobile workstations, which integrate mobile platforms with production machinery to improve efficiency by overlapping production time and delivery time. I describe a task planning algorithm for multiple mobile workstations and offer a model of mobile workstations and their jobs. This planning problem for mobile workstations includes the features of both traveling salesman problems (TSP) and job shop scheduling problems (JSP). For planning, I presente two algorithms: a) a complete search algorithm that offers a minimum makespan plan and b) a local search in the space of task graphs to offer suboptimal plans quickly. According to the experiments, the second algorithm can generate near-optimal temporal plans when the number of jobs is small. In addition, the second algorithm can generate noticeably shorter plans than a version of the job shop scheduling algorithm and SGPlan 5 when the number of jobs is large. This research shows that transport robot systems could work together with other robots or machines in various environments to overcome the limitations of existing systems for the environments. A mobile conveyor line can pass quickly objects at a long distance and can apply to many different environments by overcoming the existing problem of conveyor belts. By using mobile conveyor belts, the robots have the ability to pass objects at a distance between mobile robots to improve the performance of foraging tasks by overcoming the long travel distance for delivery and congestion near the central collection zone. In addition, a mobile workstation can handle the tasks that transport the production of goods to users. By paralleling the production time and the movement, a mobile workstation can substantially shorten the time it takes to deliver products to customers.ope