Mahmoud Efatmaneshnik

Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies, which has widespread usage in industry, is also regarded as an ideal solution for automated agriculture because it fulfils the accuracy, reliability and availability requirements of industrial and agricultural applications. Agriculture applications use position, velocity and heading information for automated vehicle guidance and control to enhance the yield and quality of the crop, and in order to vary the application of fertilizer and herbicides according to soil heterogeneity at sub-field level. A loosely coupled GPS/INS integration algorithm known as “AhrsKf” is introduced for automated agriculture vehicle guidance and control utilizing MEMS inertial sensors and GPS. The AhrsKf can produce high-frequency attitude solutions for the vehicle’s guidance and control system, by using inputs from a single survey grade L1/L2 antenna, eliminating the need for the previous two antenna solutions. Given its agricultural application, the AhrsKf has been implemented with some specific design features to improve the accuracy of the attitude solution including, temperature compensation of the inertial sensors, and the aid of plough lines of farm lands. To evaluate the AhrsKf solution, two benchmarking tests have been conducted by using a three-antenna GPS system and NovAtel’s SPAN-CPT. The results have demonstrated that the AhrsKf solution is stable and can correctly track the movement of the farming vehicle.

A modified multidimensional scaling with embedded particle filter algorithm for cooperative positioning of vehicular networks

Vehicular communication technologies are on their way to be recognized as staples of modern societies. One important challenge to safety-related applications of vehicular communication is provision of semi-precise positioning. Cooperative positioning is proposed for that purpose, and of course from research point of view is very attractive. From the practical point of view the attractiveness of cooperative positioning lies in its independence from any major additional infrastructure other than the vehicular communication systems. This paper introduces a new positioning algorithm for localization of mobile networks, in general, that applies directly to vehicular networks. The algorithm is based on the well known multidimensional algorithm and shows impressive performance compared to its counterparts in the vehicular positioning literature.

On optimal modularity for system construction

Modularity is a natural instrument and a ubiquitous practice for the engineering of human-made systems. However, modularization remains more of an art than a science; to the extent that the notion of optimal modularity is rarely used in engineering design. We prove that optimal modularity exists (at least for construction)—and is achieved through balanced modularization as structural symmetry in the distribution of the sizes of modules. We show that system construction cost is highly sensitive to both the number of modules and the modularization structure. However, this sensitivity has an inverse relationship with process capability and is minimal for highly capable construction processes with small process uncertainties. Conclusions are reached by a Bayesian estimation technique for a relatively simple construction model originally introduced by Herbert Simon for the hypothetical production of a linear structure, taking into account errors that may occur in the work associated with the production of the links between the nodes in the structure for varied numbers of modules.

Assembly sequence planning for processes with heterogeneous reliabilities

Stochasticity in assembly processes is often associated with the processing time and availability of machinery, tools and manpower, however in this paper it is determined by probability of an assembly task successful completion which here is referred to as task reliability. We present a mathematical model for optimising the expected assembly cost, and consider two scenarios: the first a situation where a failure of one assembly task requires rework of that task alone; second a situation in which a failure in the midst of the process requires resumption of previously completed tasks. In the worst case scenario the assembly process must restart from the beginning. We show that the first scenario is insensitive to sequencing unless there are set-up costs. In the second scenario the process is sensitive to tasks’ sequence. We present a heuristic that argues for accomplishing more uncertain tasks (with less reliability) earlier in the process to decrease the expected cost of assembly, and show that in a mutually dependent assembly process, when tasks’ reliabilities are similar, the cheaper tasks should be executed earlier in the process.

Immune Decomposition and Decomposability Analysis of Complex Design Problems with a Graph Theoretic Complexity Measure

Large scale problems need to be decomposed for tractability purposes. The decomposition process needs to be carefully managed to minimize the interdependencies between sub-problems. A measure of partitioning quality is introduced and its application in problem classification is highlighted. The measure is complexity based (real complexity) and can be employed for both disjoint and overlap decompositions. The measure shows that decomposition increases the overall complexity of the problem, which can be taken as the measure’s viability indicator. The real complexity can also indicate the decomposability of the design problem, when the complexity of the whole after decomposition is less than the complexity sum of sub-problems. As such, real complexity can specify the necessary paradigm shift from decomposition based problem solving to evolutionary and holistic problem solving.

IMMUNITY AS A DESIGN DECISION MAKING PARADIGM FOR COMPLEX SYSTEMS: A ROBUSTNESS APPROACH

Within complex organizational systems such as Concurrent Engineering (CE) Product Development environments, uncertainty in information, and thus the knowledge required to make effective decisions, strongly influences the quality of the final product. Such systems are marked by high degrees of data variability making techniques such as optimization less than ideal, particularly for multiobjective problem types. Although different methods that enable designers to deal with uncertainty have been utilized, they derive from what appears to be a less than adequate representation of complex system behavior. This article presents a representation for complex systems based on the analogy of immunity where the environment of a system or “nonself” represents the set of input and outputs with the “self” of the system as the resulting “effect.” A fuzzy approach to the random perturbation of the system variables through the introduction of a global robustness index is proposed. The approach is presented in the context of decision making for tolerance control within manufacturing process design.

Failure propagation in SoS: Why SoS should be loosely coupled

SoS are an ensemble of complex systems, which have the potential for an extraordinary amount of structural complexity, even temporarily, as a result of interconnections and couplings that can vary in strength. Because of this, SoS can be extremely vulnerable to very sudden catastrophic collapse as a result of small and insignificant partial functionality losses in one of the constituent systems. This is an indication that SoS has the potential to behave like a complex system, or even worse if the interactions are not managed carefully. This paper provides an analysis of the SoS response to uncertainties in the form of partial failures, which is complemented by the presentation of a measure of structural complexity for SoS. Experiments with the development of random graphs to simulate SoS show that a partial failure initiated in one system has a high possibility of leading to a collapse of another system of SoS, sometimes even before leading to total failure in the originating system.

A fast multidimensional scaling filter for vehicular cooperative positioning

Vehicular communication technologies are becoming staples of modern societies. This paper proposes a new positioning algorithm for vehicular networks. The algorithm is a non-classic Multi-Dimensional Scaling (MDS) based Filter (MDSF) that builds on a novel and computationally effective MDS solution covariance estimation technique and also a Maximum Likelihood (ML) filter. In general a major drawback of the non-classic MDS is the high computational cost because of the iterative nature. It is shown that a special blend between vehicular MapMatching (MM) and MDSF considerably reduces the number of iterations and the convergence time, making the MDSF a suitable algorithm for vehicular network positioning. The performance of MDSF is compared with that of an Extended Kalman Filter (EKF) together with the Cramar Rao Lower Bound (CRLB). It is shown through simulation that for all types of traffic conditions MDSF performs better than EKF and closer to CRLB than EKF. It is also shown that both MDSF and EKF algorithms are robust to typical GPS (Global Positioning System) outages in the deep urban canyons. CRLB also proves that CP in general has the ability to gap short GPS outages.

A general framework for measuring system complexity

In this work, we are motivated by the observation that previous considerations of appropriate complexity measures have not directly addressed the fundamental issue that the complexity of any particular matter or thing has a significant subjective component in which the degree of complexity depends on available frames of reference. Any attempt to remove subjectivity from a suitable measure therefore fails to address a very significant aspect of complexity. Conversely, there has been justifiable apprehension toward purely subjective complexity measures, simply because they are not verifiable if the frame of reference being applied is in itself both complex and subjective. We address this issue by introducing the concept of subjective simplicity—although a justifiable and verifiable value of subjective complexity may be difficult to assign directly, it is possible to identify in a given context what is “simple” and, from that reference, determine subjective complexity as distance from simple. We then propose a generalized complexity measure that is applicable to any domain, and provide some examples of how the framework can be applied to engineered systems.

Information fusion for localization within vehicular networks

Cooperative positioning (CP) is a localization technique originally developed for use across wireless sensor networks. With the emergence of Dedicated Short Range Communications (DSRC) infrastructure for use in Intelligent Transportation Systems (ITS), CP techniques can now be adapted for use in location determination across vehicular networks. In vehicular networks, the technique of CP fuses GPS positions with additional sensed information such as inter-vehicle distances between the moving vehicles to determine their location within a neighbourhood. This paper presents the results obtained from a research study undertaken to demonstrate the capabilities of DSRC for meeting the positioning accuracies of road safety applications. The results show that a CP algorithm that fully integrates both measured/sensed data as well as navigation information such as map data can meet the positioning requirements of safety related applications of DSRC (<0·5 m). This paper presents the results of a Cramer Rao Lower Bound analysis which is used to benchmark the performance of the CP algorithm developed. The Kalman Filter (KF) models used in the CP algorithm are detailed and results obtained from integrating GPS positions, inter-vehicular ranges and information derived from in-vehicle maps are then discussed along with typical results as determined through a variety of network simulation studies.