An investigation into the design of an ants-inspired ontology for coordinating pathfinding robotic devices

Abstract

Swarm intelligence systems, wherein robotic devices are created with abilities executed at individual level to create swarm level emergent behaviour, are appealing to industries such as nanotechnology, amorphous computing, and engineering. They are often considered suitable for determining solutions to computationally complex problems within a context where large, sophisticated robots are too expensive to procure. For instance, bee colony models, fish schooling solutions, and ant colony systems have been proposed for solving complex problems by imitating the behaviours of related natural species. Ant colony systems, in particular, have been used to offer solutions to complex optimisation issues such as the traveling salesman problem, the vehicle routing problem, and the bridge crossing task. Compelling robust and fault tolerant solutions have been produced and reported. However, formal representation of the knowledge of the ant-like robotic devices thereof has not been fully explored. In this context, ant-like robotic devices are interchangeably referred to as antbots. An ant-bot, on its own, may not have capabilities to accomplish notable solutions to problems. However, as a swarm, ant-bots can produce compelling emergent behaviour more than the sum of the actions of the individual ant-bots in the swarm. This study proposed the development of an ontology for characterising the low-level behaviours and capabilities of simulated ant-like robotic devices tasked to path find. First, we considered the distinctive qualities of ant-bots and found out that foraging is key. Ant-bots require five key actions; dropping pheromone-like indicators, flipping between different internal states, orientating based on the amount of the virtual pheromone within their local environment, making informed movements when it becomes necessary, and no action as self-explanatory. The key meta needs include the environment that holds information about the targets, and pheromone dissipation attributes. Data flow diagrams, entity relationship diagrams, and Warnier-Orr figures were derived as part of the ingredients for the proposed ontology. The study investigated the validity of the ontology that arose from the study through a triangulated mix of [Date] V simulations, experimentation, and data visualisation. Precisely, three experiments were administered to evaluate the usefulness of the proposed ontology. Speed of emergence was the main metric for this assessment. Visual emergent behaviour augmented the outcomes obtained from evaluating speed of emergency. Also, assessment of the resource demand from using the ontology sealed the evaluations. Results indicated that the proposed ontology captured and represented the knowledge required by ant-bots to achieve path finding. The ontology provided a knowledge representation approach for swarms of ant-like robotic devices, providing a roadmap to the low-level behaviours of ant-bots towards convergence. Representation of swarm knowledge in the form of an ontology offers the potential to reshape the field of swarm robotics. Such formal knowledge representation technique brings about effectiveness, adaptability, and reliability in swarm systems.