Modelling of operator’s focusing scheme along working hours: harvesting operation

ArticleIn consistent with the growing research activities regarding the Farm 4.0 concept, it is valuable to consider each possible chance of enhancement which is expected to contribute positively to the productivity and the safety of planned operations. Human centred design concept is becoming essential for the multitasking vehicles market, which promotes the research experiments aiming to understand the human behaviour inside the vehicle cabins to proceed with upgrading the design, planning and production procedures based on validated inputs leading to introducing reliable solutions for more productive and safety conduct of operations. The accurate and deep analysis of the operator behaviour inside the cabin will lead to a better understanding for the problems and issues need to be resolved in new designs in addition to providing the production planning (i.e. manpower planning and working shift period) with the necessary data to ensure achieving the maximum efficiency and effectiveness. In this research, the operator’s glance behaviour inside the tractor cabin is studied during the harvesting operation to develop a model for the change of operator's focusing scheme along working hours

Operator’s behaviour measuring methodology inside off-road vehicle cabin, operator’s focusing scheme

ArticleOperator’s workplace design takes a priority to be developed in order to reach the highest possible level of Quality, Safety and productivity. Continual improvement of the workplace is yield from studies carried out on different approaches, each approach shall keep into consideration many aspects, in this research; the results will be used for feeding the productivity aspects with valuable and reliable input data using relatively simple engineering solutions. This research is made based on literature of the accumulated knowledge from diverse fields in which different studies and analysis are made to provide the necessary input for Human Centred Design process, adopting the-state-of-the-art technologies and methodologies used for data collection and analysis for Human behaviour inside the dedicated workplace. Better understanding of the operator’s Gaze in addition to the change according to the mental and physical workloads inside the tractor cabin will lead to optimal designs for higher productivity operation

Inertial and 3D-odometry fusion in rough terrain Towards real 3D navigation

Many algorithms related to localization need good pose prediction in order to produce accurate results. This is especially the case for data association algorithms, where false feature matches can lead to the localization system failure. In rough terrain, the field of view can vary significantly between two feature extraction steps, so a good position prediction is necessary to robustly track features. This paper presents a method for combining dead reckoning sensor information in order to provide an initial estimate of the six degrees of freedom of a rough terrain rover. An inertial navigation system (INS) and the wheel encoders are used as sensory inputs. The sensor fusion scheme is based on an extended information filter (EIF) and is extensible to any kind and number of sensors. In order to test the system, the rover has been driven on different kind of obstacles while computing both pure 3D-odometric and fused INS/3D-odometry trajectories. The results show that the use of the INS significantly improves the pose prediction

COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION

This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice

A Novel Relative Navigation Control Strategy Based on Relation Space Method for Autonomous Underground Articulated Vehicles

This paper proposes a novel relative navigation control strategy based on the relation space method (RSM) for articulated underground trackless vehicles. In the RSM, a self-organizing, competitive neural network is used to identify the space around the vehicle, and the spatial geometric relationships of the identified space are used to determine the vehicle’s optimal driving direction. For driving control, the trajectories of the articulated vehicles are analyzed, and data-based steering and speed control modules are developed to reduce modeling complexity. Simulation shows that the proposed RSM can choose the correct directions for articulated vehicles in different tunnels. The effectiveness and feasibility of the resulting novel relative navigation control strategy are validated through experiments

AUTONOMOUS SHUTTLE CAR DOCKING TO A CONTINUOUS MINER USING RGB-DEPTH IMAGERY

A great deal of research is currently being conducted in automating mining equipment to improve worker health and safety and increase mine productivity. Significant progress has been made in some applications, e.g., autonomous haul trucks for surface mining. However, little progress has been made in autonomous face haulage in underground room-and pillar coal mines. Accordingly, this thesis addresses automating the operation of a shuttle car, focusing on positioning the shuttle car under the continuous miner coal-discharge conveyor during cutting and loading operations. The approach uses a stereo depth camera as the sensor, and machine-learning algorithms are used to identify various objects in the mine environment, such as the continuous miner coal-discharge conveyor, continuous miner body, roof, ribs, etc. An occupancy map is generated, a path to the continuous miner discharge conveyor is planned, and a controller is used to execute the path. The approach is developed and tested on a 1/6th-scale mock mine and in a simulated mine laboratory using full-scale equipment and manual controls