Ibrahim A. Hameed

Driving Angle and Track Sequence Optimization for Operational Path Planning Using Genetic Algorithms

The objective of this article was to develop an initial approach for a method to combine two recently developed methods related to the field area coverage problem. The first stage generated a field geometrical representation and the second stage aimed to optimize the routing of agricultural vehicles within this geometrically defined world. In the first stage, using the former method the optimal driving direction is derived based on the minimization of the overlapped area. The second stage uses the later method, the optimal routing is determined for the derived driving direction and is based on the minimization of the non-working distance. In its current state, the developed approach can provide optimal solutions in terms of overlapped area and sub-optimal solutions in terms of total non-working travelled distance. Still, these sub-optimal solutions proved more efficient compared to the conventional field work patterns. Improving this sub-optimality as well as the reducing the computation times of the method in order to be feasible for real-time implementation, are considered issues of future researching.

Field robotics in sports: automatic generation of guidance lines for automatic grass cutting, striping and pitch marking of football playing fields

Progress is constantly being made and new applications are constantly coming out in the area of field robotics. In this paper, a promising application of field robotics in football playing fields is introduced. An algorithmic approach for generating the way points required for the guidance of a GPS-based field robotic through a football playing field to automatically carry out periodical tasks such as cutting the grass field, pitch and line marking illustrations and lawn striping is represented. The manual operation of these tasks requires very skilful personnel able to work for long hours with very high concentration for the football yard to be compatible with standards of Federation Internationale de Football Association (FIFA). In the other side, a GPS-based guided vehicle or robot with three implements; grass mower, lawn stripping roller and track marking illustrator is capable of working 24 h a day, in most weather and in harsh soil conditions without loss of quality. The proposed approach for the automatic operation of football playing fields requires no or very limited human intervention and therefore it saves numerous working hours and free a worker to focus on other tasks. An economic feasibility study showed that the proposed method is economically superimposing the current manual practices.

Side-to-side 3D coverage path planning approach for agricultural robots to minimize skip/overlap areas between swaths

Automated path planning is an important tool for the automation and optimization of field operations. It can provide the waypoints required for guidance, navigation and control of agricultural robots and autonomous tractors throughout the execution of these field operations. Typical field operations?are repetitively required nearly every cropping season and therefore it should be carried out in a manner that maximizes the yield and minimizes operational cost, time and environmental impact taking into account the topographic land features. Current 3D terrain field coverage path planning algorithms are simply 2D coverage path planning projected into 3D through field terrain represented by the fields Digital Elevation Model (DEM). When projecting 2D coverage plan into its 3D counterpart, the actual distance between adjacent paths on the topographic surface either increases or decreases, and consequently there might be skips or overlaps between adjacent paths on the slopes. In addition, when the machine rolls on slopes the effective width of the implement decreases by a similar amount to double this error and complicates the problem. Skips and overlaps can lead to an inefficient use of land and resources. In this paper, a numerical approach to estimate the total skip/overlap areas is developed and applied to determine the optimum-driving angle that minimizes this impact. Also, a novel side-to-side 3D coverage path planning approach, which ensures zero skips/overlaps regardless of the topographical nature of the field terrain, is developed. The approaches developed in this paper are tested and validated using a hypothetical test field of a tailored terrain and a real experimental field of uneven terrain nature. The proposed approaches illustrated that a significant percentage of uncovered area could be saved if appropriate driving angle is chosen and if a side-to-side 3D coverage is used. We developed a more efficient 3D field coverage approach compared to existing approaches.We developed a numerical approach to examine the efficiency of 3D coverage algorithms in terms of skip/overlap areas.We developed side-to-side 3D field coverage approach, which ensure 100% coverage regardless of the topographical nature of the field surface.Simulation and real field experiments are conducted to prove the efficiency and superiority of the developed approaches.

Intelligent computer-automated crane design using an online crane prototyping tool

In an accompanying paper submitted concurrently to this conference, we present our first complete version of a generic and modular software framework for intelligent computer-automated product design. The framework has been implemented with a client-server software architecture that automates the design of offshore cranes. The framework was demonstrated by means of a case study where we used a genetic algorithm (GA) to optimise the crane design of a real and delivered knuckleboom crane. For the chosen objective function, the optimised crane design outperformed the real crane. In this paper, we augment our aforementioned case study by implementing a new crane optimisation client in Matlab that uses a GA both for optimising a set of objective functions and for multi-objective optimisation. Communicating with an online crane prototyping tool, the optimisation client and its GA are able to optimise crane designs with respect to two selected design criteria: the maximum safe working load and the total crane weight. Our work demonstrates the modularity of the software framework as well as the viability of our approach for intelligent computer-automated design, whilst the results are valuable for informing future directions of our research.

Using Theatre to Study Interaction with Care Robots

This paper describes an innovative approach for studying interaction between humans and care robots. Using live theatrical performance, we developed a play that depicts a plausible, future care scenario between a human and a socially assistive robot. We used an expanded version of the Godspeed Questionnaire to measure the audiences’ reactions to the robot, the observed interactions between the human and the robot, and their overall reactions to the performance. We present our results and propose a methodology and guidelines for using applied theatre as a platform to study human robot interaction (HRI). Unlike other HRI studies, the subject of our research is not the user who interacts with the robot but rather the audiences observing the HRI. We consider the technical and artistic challenges of designing and staging a believable care scenario that could potentially influence the perception and acceptance of care robots. This study marks a first step towards designing a robust framework for combining applied theatre with HRI research.

A Software Framework For Intelligent Computer-Automated Product Design

For many years, NTNU in Ålesund (formerly Aalesund University College) has maintained a close relationship with the maritime industrial cluster, centred in the surrounding geographical region, thus acting as a hub for both education, research, and innovation. Of many common relevant research topics, virtual prototyping is currently one of the most important. In this paper, we describe our first complete version of a generic and modular software framework for intelligent computer-automated product design. We present our framework in the context of design of offshore cranes, with easy extensions to other products, be it maritime or not. Funded by the Research Council of Norway and its Programme for Regional R&D and Innovation (VRI), the work we present has been part of two separate but related research projects (grant nos. 241238 and 249171) in close cooperation with two local maritime industrial partners. We have implemented several software modules that together constitute the framework, of which the most important are a server-side crane prototyping tool (CPT), a client-side web graphical user interface (GUI), and a client-side artificial intelligence for product optimisation (AIPO) module that uses a genetic algorithm (GA) library for optimising design parameters to achieve a crane design with desired performance. Communication between clients and server is achieved by means of the HTTP and WebSocket protocols and JSON as the data format. To demonstrate the feasibility of the fully functioning complete system, we present a case study where our computer-automated design was able to improve the existing design of a real and delivered 50-tonnes, 2.9 million EUR knuckleboom crane with respect to some chosen desired design criteria. Our framework being generic and modular, both clientside and server-side modules can easily be extended or Corresponding author: Robin T. Bye, robin.t.bye@ntnu.no. replaced. We demonstrate the feasibility of this concept in an accompanying paper submitted concurrently, in which we create a simple product optimisation client in Matlab that uses readily available toolboxes to connect to the CPT and optimise various crane designs by means of a GA. In addition, our research team is currently developing a winch prototyping tool to which our existing AIPO module can connect and optimise winch designs with only small configuration changes. This work will be published in the near future.

Robotic weed monitoring

Abstract In this paper, an integrated management system for the planning and activation of a field monitoring task is presented. The architecture of the system is built around a mobile robotic unit. The internet-based architecture of the system includes a station unit that works as a mobile on-farm operating console, the mobile robotic unit and a field server for generating and storing maps. The hypothesis is that it is possible to automate the planning and execution of the operation of monitoring the in-field weed density and species distribution. The developed planning system includes the automatic field geometrical representation and the route planning for the mobile unit. For the field representation two algorithmic approaches for automated track generation were used. For the route planning, a graph-based field coverage algorithm and a discrete grid-based path planning method were used. The low computational requirements of the implemented algorithms make it feasible to adopt a real-time re-planning strategy in which a set of new planning problems are solved based on the latest information. The central part of such a planning, concerns the dynamic re-evaluation of the initial plan for sampling and routing based on the on-line analysis of the samples. This provides the basis for a fully sequential adaptive adjustment of the sampling procedure after each individual sampling. It is expected that such a dynamic targeted sampling and routing system will reduce the overall cost and time consumption of the weed monitoring operation.

An Interval Type-2 Fuzzy Logic System for Assessment of Studentsź Answer Scripts under High Levels of Uncertainty

The proper system for evaluating the learning achievement of students is the key to realizing the purpose of education and learning. Traditional grading methods are largely based on human judgments, which tend to be subjective. In addition, it is based on sharp criteria instead of fuzzy criteria and suffers from erroneous scores assigned by indifferent or inexperienced examiners, which represent a rich source of uncertainties, which might impair the credibility of the system. In an attempt to reduce uncertainties and provide more objective, reliable, and precise grading, a sophisticated assessment approach based on type-2 fuzzy set theory is developed. In this paper, interval type-2 (IT2) fuzzy sets, which are a special case of the general T2 fuzzy sets, are used. The transparency and capabilities of type-2 fuzzy sets in handling uncertainties is expected to provide an evaluation system able to justify and raise the quality and consistency of assessment judgments.

Building an Intelligent Controller using Simple Genetic Type-2 Fuzzy Logic System

Despite the advantages offered by type-2 fuzzy systems (T2FS) in handling uncertainties in control applications, one major problem that hinders its wide-spread implementation in real-time applications is its high computational cost (Hameed, 2010). In order to reduce the computational burden of T2FS, a simplified T2FS based on a hybrid structure of four type-1 fuzzy systems (T1FS) and a genetic algorithm (GA) is introduced (Hameed, 2009). In addition to its rule in providing the system with adaptability to cope with changing conditions, a GA provides the system with a tool to detect and illustrate the amount of uncertainty incorporated in the system. In order to show the robustness and reliability of the new implementation, the developed approach is applied to: (a) control a nonlinear multiinput multi-output (MIMO) system equipped with various types of uncertainties as an example of using T2FS in industrial applications, and (b) evaluate students’ learning achievement as an example of using T2FS in decision support systems. The new implementation of T2FS showed a superior response compared to the very complex and computational costly type-reduction approach. In addition, the ease of using the new implementation, which does not require more than the basic knowledge of T1FS and GA, is expected to help advancing the application of T2FS in multiple different areas of applications. FLS constructed based on type-1 fuzzy systems (T1FS), referred to as T1FLS, have demonstrated their ability in many applications, especially for the control of complex nonlinear systems that are difficult to model analytically (Zadeh, 1973; King & Mamdani, 1997). However, researchers have shown that T1FLS have difficulty in modeling and minimizing the effect of uncertainties (Mendel, 2001). A reason being that, T1FS are certain in the sense that for each input there is a crisp membership grade. T2FS, characterized by membership grades that are themselves fuzzy, were first introduced by Zadeh in 1975 to account for this problem (Zadeh, 1975a). As it is illustrated in Fig. 1, the MF of a T2FS has a footprint of uncertainty (FOU), which represents the uncertainties in the shape and position of T1FS (Wu & Tan, 2004). The FOU is bounded up by an upper MF (UMF) and lower by a lower MF (LMF), both of which are T1MF. Since the FOU of T2FS provides an extra mathematical dimension, they are very useful in circumstances where it is difficult to determine an exact membership grade for FS. Therefore, the amount of uncertainty in a system could be reduced by using T2FLS since it offers better capabilities to handle

Comparative predictive modelling of the occurrence of faecal indicator bacteria in a drinking water source in Norway

Presently, concentrations of fecal indicator bacteria (FIB) in raw water sources are not known before water undergoes treatment, since analysis takes approximately 24h to produce results. Using data on water quality and environmental variables, models can be used to predict real time concentrations of FIB in raw water. This study evaluates the potentials of zero-inflated regression models (ZI), Random Forest regression model (RF) and adaptive neuro-fuzzy inference system (ANFIS) to predict the concentration of FIB in the raw water source of a water treatment plant in Norway. The ZI, RF and ANFIS faecal indicator bacteria predictive models were built using physico-chemical (pH, temperature, electrical conductivity, turbidity, color, and alkalinity) and catchment precipitation data from 2009 to 2015. The study revealed that pH, temperature, turbidity, and electrical conductivity in the raw water were the most significant factors associated with the concentration of FIB in the raw water source. Compared to the other models, the ANFIS model was superior (Mean Square Error=39.49, 0.35, 0.09, 0.23CFU/100ml respectively for coliform bacteria, E. coli, Intestinal enterococci and Clostridium perfringens) in predicting the variations of FIB in the raw water during model testing. However, the model was not capable of predicting low counts of FIB during both training and testing stages of the models. The ZI and RF models were more consistent when applied to testing data, and they predicted FIB concentrations that characterized the observed FIB concentrations. While these models might need further improvement, results of this study indicate that ZI and RF regression models have high prospects as tools for the real-time prediction of FIB in raw water sources for proactive microbial risk management in water treatment plants.