Bio-inspired plume tracking algorithm for UAVS

There is an increased interest on the use of UAVs for environmental research and to track bush fire plumes, volcanic plumes or pollutant sources. The aim of this paper is to describe the theory and results of a bio-inspired plume tracking algorithm. A memory based and gradient based approach, were developed and compared. A method for generating sparse plumes was also developed. Results indicate the ability of the algorithms to track plumes in 2D and 3D.

Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Evaluating unmanned aircraft systems for deployment in plant biosecurity

This report provides a qualitative evaluation of Unmanned Aircraft Systems (UAS) and on-board sensor technology for use in plant biosecurity in the Australian context. The more general term UAS describes both the Unmanned Aerial Vehicle (UAV) and all supporting components required to operate it. This may include a ground station, operator or pilot, and a launch and recovery device for example. The focus is to identify how and under what circumstances UAS may be useful for plant biosecurity. This can be used to help guide future decisions regarding investment in UAS for plant biosecurity.

Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open-pit mine sites

Blasting is an integral part of large-scale open cut mining that often occurs in close proximity to population centers and often results in the emission of particulate material and gases potentially hazardous to health. Current air quality monitoring methods rely on limited numbers of fixed sampling locations to validate a complex fluid environment and collect sufficient data to confirm model effectiveness. This paper describes the development of a methodology to address the need of a more precise approach that is capable of characterizing blasting plumes in near-real time. The integration of the system required the modification and integration of an opto-electrical dust sensor, SHARP GP2Y10, into a small fixed-wing and multi-rotor copter, resulting in the collection of data streamed during flight. The paper also describes the calibration of the optical sensor with an industry grade dust-monitoring device, Dusttrak 8520, demonstrating a high correlation between them, with correlation coefficients (R2) greater than 0.9. The laboratory and field tests demonstrate the feasibility of coupling the sensor with the UAVs. However, further work must be done in the areas of sensor selection and calibration as well as flight planning.

A dynamic navigation model for unmanned aircraft systems and an application to autonomous front-on environmental sensing and photography using low-cost sensor systems

This paper presents an unmanned aircraft system (UAS) that uses a probabilistic model for autonomous front-on environmental sensing or photography of a target. The system is based on low-cost and readily-available sensor systems in dynamic environments and with the general intent of improving the capabilities of dynamic waypoint-based navigation systems for a low-cost UAS. The behavioural dynamics of target movement for the design of a Kalman filter and Markov model-based prediction algorithm are included. Geometrical concepts and the Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of a target, thus delivering a new waypoint for autonomous navigation. The results of the application to aerial filming with low-cost UAS are presented, achieving the desired goal of maintained front-on perspective without significant constraint to the route or pace of target movement.

Ship Seakeeping Operability, Motion Control, and Autonomy - A Bayesian Perspective

Ship seakeeping operability refers to the quantification of motion performance in waves relative to mission requirements. This is used to make decisions about preferred vessel designs, but it can also be used as comprehensive assessment of the benefits of ship-motion-control systems. Traditionally, operability computation aggregates statistics of motion computed over over the envelope of likely environmental conditions in order to determine a coefficient in the range from 0 to 1 called operability. When used for assessment of motion-control systems, the increase of operability is taken as the key performance indicator. The operability coefficient is often given the interpretation of the percentage of time operable. This paper considers an alternative probabilistic approach to this traditional computation of operability. It characterises operability not as a number to which a frequency interpretation is attached, but as a hypothesis that a vessel will attain the desired performance in one mission considering the envelope of likely operational conditions. This enables the use of Bayesian theory to compute the probability of that this hypothesis is true conditional on data from simulations. Thus, the metric considered is the probability of operability. This formulation not only adheres to recent developments in reliability and risk analysis, but also allows incorporating into the analysis more accurate descriptions of ship-motion-control systems since the analysis is not limited to linear ship responses in the frequency domain. The paper also discusses an extension of the approach to the case of assessment of increased levels of autonomy for unmanned marine craft.

A novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties

In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.

The social consequences of control: Accounting for indentured labour in Fiji 1879 - 1920

Purpose: This is a study of the social consequences of accounting controls over labour. It examines the system of tasking used to control Indian indentured workers using a governmentality approach in the historical context of Fijian sugar plantations during the British colonial period, from 1879 to 1920. Method/ Methodology: Archival data consisting of documents from the Colonial Secretary’s Office, reports and related literature on Indian indentured labour was accessed from the National Archives of Fiji. In addition, documented accounts of the experiences of indentured labourers over the period of the study give voice to the social costs of the indenture system, highlighting the social impact of accounting control systems. Findings: Accounting and management controls were developed to extract surplus value from Indian labour. The practice of tasking was implemented in a plantation structure where indentured labourers were controlled hierarchically through a variety of calculative monitoring practices. This resulted in the exploitation and consequent economic, social and racial marginalisation of indentured workers. Originality: The paper contributes to the growing body of literature highlighting the social effects of accounting control systems. It exposes the social costs borne by indentured workers employed on Fijian sugar plantations. Practice/ Research Implications: The study promotes better understanding of the practice and impact of accounting as a technology of government and control within a particular institutional setting, in this case the British colony of Fiji. By highlighting the social implications of these controls in their historical context, we alert corporations, government policy makers, accountants and workers to the socially damaging effects of exploitive management control systems.

Towards vision-based deep reinforcement learning for robotic motion control

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.

New directions: Air pollution challenges for developing megacities like Delhi

Most major cities around the world experience periods of elevated air pollution levels, which exceed international health-based air quality standards (Kumar et al., 2013). Although it is a global problem, some of the highest air pollution levels are found in rapidly expanding cities in India and China. The sources, emissions, transformations and broad effects of meteorology on air pollution are reasonably well accounted in air quality control strategies in many developed cities; however these key factors remain poorly constrained in the growing cities of countries with emerging economies. We focus here on Delhi, one of the largest global population centres, which faces particular air pollution challenges, now and in the future.