A Hormone-Inspired Arbitration System For Self Identifying Abilities Amongst A Heterogeneous Robot Swarm

Current exploration of adaptation in robot swarms requires the swarm or individuals within that swarm to have knowledge of their own capabilities. Across long term use a swarms understanding of its capabilities may become inaccurate due to wear or faults in the system. In addition to this, systems capable of self designing morphologies are becoming increasingly feasible. In these self designing examples it would be impossible to have accurate knowledge of capability before executing a task for the first time. We propose an arbitration system that requires no explicit knowledge of capability but instead uses hormone-inspired values to decide on an environmental preference. The robots in the swarm differ by wheel type and thus how quickly they are able to move across terrain. The goal of this system is to allow robots to identify their strengths within a swarm and allocate themselves to areas of an environment with a floor type that suits their ability. This work shows that the use of a hormone-inspired arbitration system can extrapolate robot capability and adapt the systems preference of terrain to suit said capability.</p

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

An Amalgamation of Hormone Inspired Arbitration Systems For Application In Robot Swarms

Previous work has shown that virtual hormone systems can be engineered to arbitrateswarms of robots between sets of behaviours. These virtual hormones act similarly to theirnatural counterparts, providing a method of online, reactive adaptation. It is yet to be shownhow virtual hormone systems could be used when a robotic swarm has a large variety of task typesto execute. This paper details work that demonstrates the viability of a collection of virtual hormonesthat can be used to regulate and adapt a swarm over time, in response to different environmentsand tasks. Specifically, the paper examines a new method of hormone speed control for energyefficiency and combines it with two existing systems controlling environmental preference as wellas a selection of behaviours that produce an effective foraging swarm. Experiments confirm theeffectiveness of the combined system, showing that a swarm of robots equipped with multiple virtualhormones can forage efficiently to a specified item demand within an allotted period of time

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

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

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Homeostatic action selection for simultaneous multi-tasking

Mobile robots are rapidly developing and gaining in competence, but the potential of available hardware still far outstrips our ability to harness. Domain-specific applications are most successful due to customised programming tailored to a narrow area of application. Resulting systems lack extensibility and autonomy, leading to increased cost of development. This thesis investigates the possibility of designing and implementing a general framework capable of simultaneously coordinating multiple tasks that can be added or removed in a plug and play manner. A homeostatic mechanism is proposed for resolving the contentions inevitably arising between tasks competing for the use of the same robot actuators. In order to evaluate the developed system, demonstrator tasks are constructed to reach a goal location, prevent collision, follow a contour around obstacles and balance a ball within a spherical bowl atop the robot. Experiments show preliminary success with the homeostatic coordination mechanism but a restriction to local search causes issues that preclude conclusive evaluation. Future work identifies avenues for further research and suggests switching to a planner with the sufficient foresight to continue evaluation."This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/K503162/1]." -- Acknowledgement