Control and Coordination in a Networked Robotic Platform

Control and Coordination of the robots has been widely researched area among the swarm robotics. Usually these swarms are involved in accomplishing tasks assigned to them either one after another or concurrently. Most of the times, the tasks assigned may not need the entire population of the swarm but a subset of them. In this project, emphasis has been given to determination of such subsets of robots termed as ”flock” whose size actually depends on the complexity of the task. Once the flock is determined from the swarm, leader and follower robots are determined which accomplish the task in a controlled and cooperative fashion. Although the entire control system,which is determined for collision free and coordinated environment, is stable, the results show that both wireless (bluetooth) and internet (UDP) communication system can introduce some lag which can lead robot trajectories to an unexpected set. The reason for this is each robot and a corresponding computer is considered as a complete robot and communication between the robot and the computer and between the computers was inevitable. These problems could easily be solved by integrating a computer on the robot or just add a wifi transmitter/receiver on the robot. On going down the lane, by introducing smarter robots with different kinds of sensors this project could be extended on a large scale for varied heterogenous and homogenous applications

Swarm robotics: Cooperative navigation in unknown environments

Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes

On-Board High-Performance Computing For Multi-Robot Aerial Systems

With advancements in low-energy-consumption multi/many core embedded-computing devices, a logical transition for robotic systems is Supercomputing, formally known as high performance computing (HPC), a tool currently used for solving the most complex problems for humankind such as the origin of the universe, the finding of deceases’ cures, etc. As such, HPC has always been focused on scientific inquires. However, its scope can be widening up to include missions carried out with robots. Since a robot could be embedded with computing devices, a set of robots could be set as a cluster of computers, the most reliable HPC infrastructure. The advantages of setting up such an infrastructure are many, from speeding up on-board computation up to providing a multi-robot system with robustness, scalability, user transparency, etc., all key features in supercomputing. This chapter presents a middleware technology for the enabling of high performance computing in multi-robot systems, in particular for aerial robots. The technology can be used for the automatic deployment of cluster computing in multi-robot systems, the utilization of standard HPC technologies, and the development of HPC applications in multiple fields such as precision agriculture, military, civilian, search and rescue, etc

A self-parametrization framework for meta-heuristics

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.This work was supported by national funds through the FCT - Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021

Cooperative control for multi-vehicle swarms

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying su itable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle le vel that demonstrates autonomous, cooperative and optimal behaviour whilst the preserving precision and reliability at the local vehicle level

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions