Effect of Communication Delays on the Successful Coordination of a Group of Biomimetic AUVs

In this paper, the influence of delays on the ability of a formation control algorithm to coordinate a group of twelve Biomimetic Autonomous Underwater Vehicles (BAUVs) is investigated. In this study the formation control algorithm is a decentralized methodology based on the behavioural mechanisms of fish within school structures. Incorporated within this algorithm is a representation of the well-known and frequently used communication protocol, Time-Division-Multiple-Access (TDMA). TDMA operates by assigning each vehicle a specific timeslot during which it can broadcast to the remaining members of the group. The size of this timeslot varies depending on a number of operational parameters such as the size of the message being transmitted, the hardware used and the distance between neighbouring vehicles. Therefore, in this work, numerous timeslot sizes are tested that range from theoretical possible values through to values used in practice. The formation control algorithm and the TDMA protocol have been implemented within a validated mathematical of the RoboSalmon BAUV designed and manufactured at the University of Glasgow. The results demonstrate a significant deterioration in the ability of the formation control algorithms as the timeslot size is increased. This deterioration is due to the fact that as the timeslot size is increased, the interim period between successive communication updates increases and as a result, the error between where the formation control algorithm estimates each vehicle to be and where they actually are, increases. As a result, since the algorithm no longer has an accurate representation of the positioning of neighbouring vehicles, it is no longer capable of selecting the correct behavioural equation and subsequently, is unable to coordinate the vehicles to form a stable group structure

Quantification of aircraft trajectory prediction uncertainty using polynomial chaos expansions

A novel approach to quantify the uncertainty associated with any aircraft trajectory prediction based on the application of the Polynomial Chaos (PC) theory is presented. The proposed method relies on univariate polynomial descriptions of the uncertainty sources affecting the trajectory prediction process. Those descriptions are used to build the multivariate polynomial expansions that represent the variability of the aircraft state variables along the predicted trajectory. A case study compares the results obtained by a classical Monte Carlo approach with those generated by applying the so-called arbitrary Polynomial Chaos Expansions (aPCE). The results provided herein lead to conclude that this new methodology can be used to accurately quantify trajectory prediction uncertainty with a very low computational effort, enabling the capability of computing the uncertainty of the individual trajectories of a traffic sample of thousands flights within very short time intervals

Inverse Simulation as a Tool for Fault Detection and Isolation in Planetary Rovers

With manned expeditions to planetary bodies beyond our own and the Moon currently intractable, the onus falls upon robotic systems to explore and analyse extraterrestrial environments such as Mars. These systems typically take the form of wheeled rovers, designed to navigate the difficult terrain of other worlds. Rovers have been used in this role since Lunokhod 1 landed on the Moon in 1970. While early rovers were remote controlled, communication latency with bodies beyond the Moon and the desire to improve mission effectiveness have resulted in increasing autonomy in planetary rovers. With an increase in autonomy, however, comes an increase in complexity. This can have a negative impact on the reliability of the rover system. With a fault-free system an unlikely prospect and human assistance millions of miles away, the rover must have a robust fault detection, isolation and recovery (FDIR) system. The need for comprehensive FDIR is demonstrated by the recent Chinese lunar rover, Yutu (or “Jade Rabbit”). Yutu was rendered immobile 42 days after landing and remained so for the duration of its operational life: 31 months. While its lifespan far exceeded its expected value, Yutu's inability to move severely impaired its ability to perform its mission. This clearly highlights the need for robust FDIR. A common approach to FDIR is through the generation and analysis of residuals. Output residuals may be obtained by comparing the outputs of the system with predictions of those outputs, obtained from a mathematical model of the system which is supplied with the system inputs. Output residuals allow simple detection and isolation of faults at the output of the system. Faults in earlier stages of the system, however, propagate through the system dynamics and can disperse amongst several of the outputs. This problem is exemplified by faults at the input, which can potentially excite every system state and thus manifest in every output residual. Methods exist for decoupling and analysing output residuals such that input faults may be isolated, however, these methods are complex and require comprehensive development and testing. A conceptually simpler approach is presented in this paper. Inverse simulation (InvSim) is a numerical method by which the inputs of a system are obtained for a desired output. It does so by using a Newton-Raphson algorithm to solve a non-linear model of the system for the input. When supplied with the outputs of a fault-afflicted system, InvSim produces the input required to drive a fault-free system to this output. The fault therefore manifests itself in this generated input signal. The InvSim-generated input may then be compared to the true system input to generate input residuals. Just as a fault at an output manifests itself in the residual for that output alone, a fault at an input similarly manifests itself only in the residual for that input. InvSim may also be used to generate residuals at other locations in the system, by considering distinct subsystems with their own inputs and outputs. This ability is tested comprehensively in this paper. Faults are applied to a simulated rover at a variety of locations within the system structure and residuals generated using both InvSim and conventional forward simulation. Residuals generated using InvSim are shown to facilitate detection and isolation of faults in several locations using simple analyses. By contrast, forward simulation requires the use of complex analytical methods such as structured residuals or adaptive thresholds

Simulation studies relating to rudder roll stabilization of a container ship using neural networks

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

A Comparison of Forward and Inverse Simulation Methods for Fault Detection on a Rover

Fault tolerant design is hugely important for autonomous mobile robots such as planetary exploration rovers (PERs), as they are required to be both robust and reliable in extremely harsh environments. One of the main principles of fault tolerance is the detection and diagnosis of any faults afflicting the system. A model-based fault detection procedure is presented using forward and inverse simulation methods. The results of each method are compared for faults in different system locations to display the differences and advantages of both methods. It is shown by this comparison that the methods complement each other and can be used concurrently to diagnose output and input faults

Simulation Studies Relating to Rudder Roll Stabilization of a Container Ship Using Neural Networks

RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully

Engineering the locusts: Hind leg modelling towards the design of a bio-inspired space hopper

The mechanical operation of a biologically inspired robot hopper is presented. This design is based on the hind leg dynamics and jumping gait of a desert locust (Schistocerca gregaria). The biological mechanism is represented as a lumped mass system. This emulates the muscle activation sequence and gait responsible for the long, coordinated jump of locusts, whilst providing an engineering equivalent for the design of a biological inspired hopper for planetary exploration. Despite the crude simplification, performance compares well against biological data found in the literature and scaling towards size more typical of robotic realisation are considered from an engineering point of view. This aspect makes an important contribution to knowledge as it quantifies the balance between biological similarity and efficiency of the biomimetic hopping mechanism. Further, this work provides useful information towards the biomimetic design of a hopper vehicle whilst the analysis uncover the range maximisation conditions for powered flight at constant thrust by analytic means. The proposed design bridges concepts looking at the gait dynamics and designs oriented to extended, full powered trajectories

Coordination of a School of Robotic Fish Using Nearest Neighbour Principles

Autonomous Underwater Vehicles (AUVs) are Unmanned Underwater Vehicles (UUVs) that are able to function without direct control from a human operator. Consequently, they have a wide range of applications from scientific research of the oceans to military applications such as maritime surveillance. However, there is now the demand for AUVs to be operated within a multi-vehicle scenario to allow large areas of the ocean to be monitored simultaneously. However, in order for this to become a reality algorithms have to be created that ensure that a group of AUVs could be self-organising. Therefore, using a validated mathematical model of a biomimetic robotic fish (called RoboSalmon) and taking inspiration from nature, this paper outlines the implementation of co-ordination algorithms based upon the behavioural mechanisms exhibited by schools of fish to allow a group of AUVs to become self-organising. The algorithms implemented are based on two different methodologies known as the Discrete and Continuous Behavioral Zone methodologies. The results obtained demonstrated that although both methodologies result in the formation of a school structure, the results obtained from the Continuous Behavioral Zone (CBZ) methodology were more resilient to changes in parameters associated with school structures and therefore these algorithms provided the most effective way to allow a group of AUVs to be considered as self-organising