Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

An intelligent novel tripartite - (PSO-GA-SA) optimization strategy

A solution approach for many challenging and non-differentiable optimization tasks in industries is the use of non-deterministic meta-heuristic methods. Some of these approaches include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). However, with the implementation usage of these robust and stochastic optimization approaches, there are still some predominant issues such as the problem of the potential solution being trapped in a local minima solution space. Other challenges include the untimely convergence and the slow rate of arriving at optimal solutions. In this research study, a tripartite version (PSO-GA-SA) is proposed to address these deficiencies. This algorithm is designed with the full exploration of all the capabilities of PSO, GA and SA functioning simultaneously with a high level of intelligent system techniques to exploit and exchange relevant population traits in real time without compromising the computational time. The design algorithm further incorporates a variable velocity component that introduces random intelligence depending on the fitness performance from one generation to the other. The robust design is validated with known mathematical test function models. There are substantial performance improvements when the novel PSO-GA-SA approach is subjected to three test functions used as case studies. The results obtained indicate that the new approach performs better than the individual methods from the fitness function deviation point of view and in terms of the total simulation time whilst operating with both a reduced number of generations and populations. Moreover, the new novel approach offers more beneficial trade-off between exploration and exploitation of PSO, GA and SA. This novel design is implemented using an object oriented programming approach and it is expected to be compatible with a variety of practical problems with specified input-output pairs coupled with constraints and limitations on the available resources

Swarm computational intelligence design for a high integrity protection system

The search meta-heuristic procedure that mimics the process of biological natural selection is an embedded part of artificial intelligence (AI). This is regularly used for obtaining the solution to some optimization problems such as the minimization of disastrous occurrence events in industries. Extra precautions are given to people and equipment operating in hazardous and harsh environments; thus there are safety systems designed to give the required, accurate, necessary and timely protections. There is hence the need to drastically reduce the probability of the occurrence of a system failure. A High Integrity Protection System (HIPS) is a safety device which could be installed on offshore facilities with the objective to mitigate a high pressure upsurge that has the potential to cause immense harm and subsequently destroy the system. The aim of the research is to use a Particle Swarm Optimization (PSO) approach to intelligently design the system in order to optimize and reduce the unavailability of the HIPS design. A Fault Tree Analysis (FTA) model is employed to build the HIPS structure. FTA is a top-down approach using Boolean logic operations that is used to analyze causes, investigates potential and likely faults and to quantify their contribution to system failure in the process of product design. Comparison is made between this HIPS-PSO approach and the previous work performed using a genetic algorithm(GA). Alongside from the simplicity in the design of the HIPS-PSO approach, a much faster execution time and a reduced system unavailability was obtained when compared with the GA approach

A novel ontological approach to modelling engineering processes: A coupled tank system case study

Many modelling techniques such as Artificial Neural Network (ANN) and SIMULINK have been employed in engineering processes such as control systems. However, these techniques lack some beneficial features such as the auto-classification and self-awareness of knowledge, the dynamic knowledge discovery, validating the consistency of knowledge and the possibilities of embedding Semantic Web Rule Language (SWRL) rules into various modelling tasks. This paper presents an original and innovative ontology design that models the coupled tanks system (CTS) with additional capabilities of providing aforementioned advantages. This new approach for modelling engineering phenomena employs the Web Ontology Language (OWL) and also processes the capabilities of incorporating Description Logics (DL) and Semantic Web technologies into the ontology-based design. The results obtained in this paper show the successful demonstration and implementation of our new knowledge modelling approach