Computational Intelligence for Cooperative Swarm Control

Over the last few decades, swarm intelligence (SI) has shown significant benefits in many practical applications. Real-world applications of swarm intelligence include disaster response and wildlife conservation. Swarm robots can collaborate to search for survivors, locate victims, and assess damage in hazardous environments during an earthquake or natural disaster. They can coordinate their movements and share data in real-time to increase their efficiency and effectiveness while guiding the survivors. In addition to tracking animal movements and behaviour, robots can guide animals to or away from specific areas. Sheep herding is a significant source of income in Australia that could be significantly enhanced if the human shepherd could be supported by single or multiple robots. Although the shepherding framework has become a popular SI mechanism, where a leading agent (sheepdog) controls a swarm of agents (sheep) to complete a task, controlling a swarm of agents is still not a trivial task, especially in the presence of some practical constraints. For example, most of the existing shepherding literature assumes that each swarm member has an unlimited sensing range to recognise all other members’ locations. However, this is not practical for physical systems. In addition, current approaches do not consider shepherding as a distributed system where an agent, namely a central unit, may observe the environment and commu- nicate with the shepherd to guide the swarm. However, this brings another hurdle when noisy communication channels between the central unit and the shepherd af- fect the success of the mission. Also, the literature lacks shepherding models that can cope with dynamic communication systems. Therefore, this thesis aims to design a multi-agent learning system for effective shepherding control systems in a partially observable environment under communication constraints. To achieve this goal, the thesis first introduces a new methodology to guide agents whose sensing range is limited. In this thesis, the sheep are modelled as an induced network to represent the sheep’s sensing range and propose a geometric method for finding a shepherd-impacted subset of sheep. The proposed swarm optimal herding point uses a particle swarm optimiser and a clustering mechanism to find the sheepdog’s near-optimal herding location while considering flock cohesion. Then, an improved version of the algorithm (named swarm optimal modified centroid push) is proposed to estimate the sheepdog’s intermediate waypoints to the herding point considering the sheep cohesion. The approaches outperform existing shepherding methods in reducing task time and increasing the success rate for herding. Next, to improve shepherding in noisy communication channels, this thesis pro- poses a collaborative learning-based method to enhance communication between the central unit and the herding agent. The proposed independent pre-training collab- orative learning technique decreases the transmission mean square error by half in 10% of the training time compared to existing approaches. The algorithm is then ex- tended so that the sheepdog can read the modulated herding points from the central unit. The results demonstrate the efficiency of the new technique in time-varying noisy channels. Finally, the central unit is modelled as a mobile agent to lower the time-varying noise caused by the sheepdog’s motion during the task. So, I propose a Q-learning- based incremental search to increase transmission success between the shepherd and the central unit. In addition, two unique reward functions are presented to ensure swarm guidance success with minimal energy consumption. The results demonstrate an increase in the success rate for shepherding

Context Awareness in Swarm Systems

Recent swarms of Uncrewed Systems (UxS) require substantial human input to support their operation. The little 'intelligence' on these platforms limits their potential value and increases their overall cost. Artificial Intelligence (AI) solutions are needed to allow a single human to guide swarms of larger sizes. Shepherding is a bio-inspired swarm guidance approach with one or a few sheepdogs guiding a larger number of sheep. By designing AI-agents playing the role of sheepdogs, humans can guide the swarm by using these AI agents in the same manner that a farmer uses biological sheepdogs to muster sheep. A context-aware AI-sheepdog offers human operators a smarter command and control system. It overcomes the current limiting assumption in the literature of swarm homogeneity to manage heterogeneous swarms and allows the AI agents to better team with human operators. This thesis aims to demonstrate the use of an ontology-guided architecture to deliver enhanced contextual awareness for swarm control agents. The proposed architecture increases the contextual awareness of AI-sheepdogs to improve swarm guidance and control, enabling individual and collective UxS to characterise and respond to ambiguous swarm behavioural patterns. The architecture, associated methods, and algorithms advance the swarm literature by allowing improved contextual awareness to guide heterogeneous swarms. Metrics and methods are developed to identify the sources of influence in the swarm, recognise and discriminate the behavioural traits of heterogeneous influencing agents, and design AI algorithms to recognise activities and behaviours. The proposed contributions will enable the next generation of UxS with higher levels of autonomy to generate more effective Human-Swarm Teams (HSTs)

Contextually aware intelligent control agents for heterogeneous swarms

An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.</p

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain a low-computational ceiling while increasing the swarm's abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm-control intelligent agent. The intelligent control agent (shepherd) first uses swarm metrics to recognise the type of swarm it interacts with to then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. information contents) of the control agent without sacrificing the low-computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.Comment: 37 pages, 3 figures, 11 table

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Synthesis and Analysis of Minimalist Control Strategies for Swarm Robotic Systems

The field of swarm robotics studies bio-inspired cooperative control strategies for large groups of relatively simple robots. The robots are limited in their individual capabilities, however, by inducing cooperation amongst them, the limitations can be overcome. Local sensing and interactions within the robotic swarm promote scalable, robust, and flexible behaviours. This thesis focuses on synthesising and analysing minimalist control strategies for swarm robotic systems. Using a computation-free swarming framework, multiple decentralised control strategies are synthesised and analysed. The control strategies enable the robots—equipped with only discrete-valued sensors—to reactively respond to their environment. We present the simplest control solutions to date to four multi-agent problems: finding consensus, gathering on a grid, shepherding, and spatial coverage. The control solutions—obtained by employing an offline evolutionary robotics approach—are tested, either in computer simulation or by physical experiment. They are shown to be—up to a certain extent—scalable, robust against sensor noise, and flexible to the changes in their environment. The investigated gathering problem is proven to be unsolvable using the deterministic framework. The extended framework, using stochastic reactive controllers, is applied to obtain provably correct solutions. Using no run-time memory and only limited sensing make it possible to realise implementations that are arguably free of arithmetic computation. Due to the low computational demands, the control solutions may enable or inspire novel applications, for example, in nanomedicine

Natural Computing and Beyond

This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics